Strategic Liquidity Hoarding and Predatory Trading:

An Empirical Investigation∗

Job Market Paper

MICHAEL LIU†

September 2015

Abstract

This paper examines strategic liquidity hoarding and predatory trading by studying portfolio

decisions of United States insurers. I find that insurers located in hurricane-prone areas sell

bonds to hoard cash before disasters. Interestingly, inland insurers also sell bonds to hoard

cash before disasters, leading to price falling excessively after disasters when affected insurers

are forced to sell. Using approximately 40% of pre-disaster cash holdings, the inland insurers

exploit the discounted prices after disasters and realize $5.72 million in abnormal profits. This

is consistent with models in which predatory traders take advantage of the price pressure from

liquidity-constrained, disaster-affected traders. These results highlight the strategic motive for

hoarding liquidity and the effect of predatory trading on the corporate bond market.

JEL Classifications: G11, G14, G22, G23

Key words: Predatory Trading; Strategic Liquidity Hoarding; Insurer; Natural Experiment;

∗I am greatly indebted to Neal Galpin, Joachim Inkmann, Hae Won Jung, and Jordan Neyland for theirextensive help and support. I thank NAIC for providing data. I thank Lynnette Purda (discussant), Martin Boyer,Jason Smith, Yoko Shirasu (discussant), Richard Lowery, Jonathan Berk, as well as the conference participantsat the 2013 Research Reference Groups of Australia Centre for Financial Studies, 2014 Ottawa Northern FinanceAssociation, 2015 Chicago Midwest Finance Association, 2015 Orlando Financial Management Association (FMA)Annual Meeting (scheduled), 2015 Orlando FMA Doctoral Consortium (scheduled), and 2015 Australasian Financeand Banking Conference (scheduled).†Ph.D. Candidate, Department of Finance, University of Melbourne, Australia. Email: yubol@student.

unimelb.edu.au.

1 Introduction

Since Keynes (1936), finance research has demonstrated that institutions hoard liquidity as a

precaution against subsequent liquidity shocks that prevent investments in positive net present

value projects.1 A less well-understood incentive for hoarding liquidity is a strategic consideration

of taking advantage of liquidity constraints of peers that are trading in the same financial market.2

However, to date, there is little research that differentiates between the two motives for hoarding

liquidity in portfolios, or provides an evaluation of their importance for hoarding liquidity. This

paper fills this gap by examining the strategic considerations of hoarding in the context of insurers.

I show that before disasters some insurers that do not expect to pay claims increase cash by

reducing illiquid assets. This behavior further erodes the liquidity of assets for insurers that

expect to pay claims, creating an opportunity to exploit prices when they are forced to sell their

illiquid assets aggressively at discounted prices after disasters.

The following example places Brunnermeier and Pedersen (2005) (BP hereafter) model in the

context of insurers to demonstrate the manner in which insurers engage in strategic liquidity

hoarding and predatory trading. Consider three insurers: a Louisiana insurer, a Florida insurer,

and a Utah insurer. Prior to a hurricane season, none of the insurers has perfect information

about the actual cost of a potential hurricane or the location affected by the hurricane, though

all three insurers know a hurricane event is likely to occur (i.e. the knowledge level is equivalent

to the assumption that the timing of a pending order is known in BP). Both the Florida insurer

and the Louisiana insurer hoard cash as a precaution against future claims. Knowing that cash

today will allow other insurers to avoid fire sales tomorrow, the Utah insurer (the predator in

BP) does not provide liquidity, and in fact, further erodes liquidity by trading alongside others.

Given that they have hoarded less cash than necessary, the Florida insurer and the Louisiana

insurer (both prey in BP) are forced to sell illiquid assets even more aggressively than necessary

at discounted prices when they are affected by a hurricane event (e.g. Hurricane Katrina). The

Utah insurer is then able to buy the illiquid assets at deeper discounts than if it had not traded

before the hurricane events. In the situation that only the Louisiana insurer (the prey in BP)

was affected by a hurricane (e.g. Hurricane Ike), the Florida insurer (the “lucky” insurer) would

be fortunate, and be able join the Utah insurer in exploiting the discounts.

1See Froot, Scharfstein, and Stein (1993); Acharya, Almeida, and Campello (2007); Acharya, Shin, and Yorul-mazer (2011); Ashcraft, McAndrews, and Skeie (2011); Cornett, McNutt, Strahan, and Tehranian (2011); Acharyaand Skeie (2011); Diamond and Rajan (2011); Acharya and Merrouche (2012).

2For example, Brunnermeier and Pedersen (2005) and Acharya, Shin, and Yorulmazer (2011)

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Using a sample of U.S. insurers from 2000 to 2009, I find evidence that is consistent with

strategic liquidity hoarding and predatory trading. First, I find that in the quarter before a

disaster, all insurers sell bonds to hoard cash, and those that do not expect to pay claims, hoard

more than others, though the difference in cash holding between insurers that expect claims

and insurers that do not expect claims is indistinguishable. Consistent with the Brunnermeier

and Pedersen (2005) model applied to insurers, predators (e.g. the Utah insurer in the previous

example) initially trade alongside prey (e.g. the Louisiana insurer in the previous example).

The evidence also suggests that treatments and controls behave similarly (i.e. in their portfolio

allocations) before the treatment (i.e. the disaster), validating the “parallel-trend” assumption

of natural experiments.

Second, I find that during the quarter of the disaster, affected insurers are forced to hoard

low-yield liquid assets and cash by selling aggressively in the corporate bond market. Unaffected

insurers, particularly those that did not expect claims ex-ante, significantly decrease their pre-

disaster cash holdings and reallocate the capital to corporate bonds, municipal bonds, common

stocks, and other risky assets. They use approximately 40% of their pre-disaster cash holdings

to purchase the bonds sold by affected insurers. The reallocation process continues into the first

quarter after the disaster before becoming insignificant the second quarter after the disaster.

Finally, I find that the ex-post primary and secondary-market performance for corporate

bonds held by insurers suggest that unaffected insurers significantly outperform affected insurers.

Further tests demonstrate that unaffected insurers that did not expect claims ex-ante account for

the majority of the performance effects. Lucky insurers (i.e. unaffected insurers that expect some

claims ex-ante) can only outperform affected insurers after hurricane seasons. The evidence of ex-

post corporate-bond performance further supports the hypothesis of strategic liquidity hoarding

and predatory trading.

The bond market provides an ideal laboratory in which to investigate predatory trading

because the major bond investors are insurance firms. Insurance firms have liquidity needs that

arise from an observable event (e.g. a hurricane).3 Moreover, while the timing of a natural

disaster is relatively predictable, there is important variability in the magnitude of the effect and

the exact firms affected by the disaster. In addition, compared with the traditional candidates in

research of liquidity hoarding (e.g. banks and open-end funds), insurance firms suffer less from

3Policyholders are eligible to claim when insured properties are damaged or destroyed. Local residents may re-ceive monetary support from the United States Federal Emergency Management Association, and insured residentssupplement these funds by claiming to their insurance firms.

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performance-based endogenous liquidity needs.4 The only major liquidity demands stem from

policyholders’ claims. To the extent that identifying determinants of portfolio liquidity requires

exogenous variations in liquidity demands, insurers provide the best chance to understand clearly

the portfolio-liquidity decisions.

There are some alternative explanations for and concerns about my observation. First, an

internal capital market for affiliated insurers and an interstate funds-reallocation process for

unaffiliated multi-state insurers may also explain my observation. I address these two potential

issues together by repeating the main tests using only single-state stand-alone insurers and find

similar results. Second, the assignment of treatments and controls is not purely random in my

tests, and might be correlated with insurers’ characteristics (e.g. an insurer with large realized

claims is likely to be in the affected group). To address this issue, I match insurers ex-ante along

several dimensions and find similar results. Third, the actuarial estimation of claims outstanding

might imperfectly reflect the claims expectation of insurers.5 I address this issue using insurer-

level disaster exposure as an alternative proxy for expected claims and find similar results. Finally,

I explore the richness of the disaster data and conduct several case studies in which the timing of

disasters is difficult to predict. Results from these cases demonstrate unanimously weak evidence

of strategic liquidity hoarding and little evidence of abnormal performance. Compared to cases

in which the timing of the disaster is very predictable, these cases suggest that prices are dropped

to a lesser extent than they would be if unaffected insurers had traded before the disaster.

This paper builds on and contributes to several strands of literature. First, it contributes to

the institution and liquidity literature by providing empirical evidence for the strategic motive

for hoarding liquidity. Despite the well-documented evidence of precautionary liquidity hoarding

by banks (e.g. Ashcraft, McAndrews, and Skeie (2011); Acharya and Merrouche (2012)), recent

studies by Diamond and Rajan (2011) and Acharya, Shin, and Yorulmazer (2011) note the theo-

retical possibility of strategic liquidity hoarding. However, given the unavailability of proprietary

transaction data, no empirical evidence has been produced, though anecdotal evidence seems to

be consistent with the argument for the existence of strategic liquidity hoarding. As noted by

Acharya, Shin, and Yorulmazer (2011) in their concluding remarks, “ It remains an important

4 Performance-based endogenous liquidity needs are very unlikely for insurers because insurers face long-termend investors and are equipped with long lock-ups and penalties for early withdrawals (Manconi, Massa, andYasuda, 2012).

5The main test quantifies expected claims using actuarial estimates of claims outstanding, which is potentiallysubject to the accuracy of actuary models and the discretion and uncertainty in actuarial estimation. Indeed,relevant studies in the insurance and accounting literature have clearly documented that insurers manipulate claimsestimates to hide financial weaknesses (see Petroni (1992); Harrington and Danzon (1994); Gaver and Paterson(2004)) or to smooth income (see Weiss (1985); Beaver, McNichols, and Nelson (2003)).

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empirical question to differentiate and measure the importance of strategic motive relative to the

more traditional precautionary motive for holding liquidity.”

This paper also contributes to a growing strand of literature on portfolio choices of insurance

firms. Financial economists are interested in insurers partially because they play an important

role in transmitting funds to provide credit to industrial firms.6 However, the existing literature

overwhelmingly argues that capital regulations drive the insurers’ asset-side behavior (e.g. Ellul,

Jotikasthira, and Lundblad (2011); Merrill, Nadauld, Stulz, and Sherlund (2012); Koijen and

Yogo (2013, 2015)). This paper demonstrates that even in a highly regulated industry such as

the insurance industry, not all portfolio decisions are driven by regulations. The most similar

research to this paper is Becker and Ivashina (2014), who demonstrate that by holding regulatory

constraints constant, insurers exhibit a significant propensity to buy riskier assets to achieve

higher yields. However, unlike Becker and Ivashina (2014), this paper demonstrates that U.S.

insurers strategically hoard liquidity ex-ante and engage in predatory trading ex-post.

This paper is organized as follows. Section 2 outlines the institutional settings. Section 3

reviews the relevant literature. Section 4 describes the data and methodology. Section 5 presents

the empirical results. Section 6 performs various robustness tests, and Section 7 concludes.

2 Institutional Setting

This section outlines the institutional background of the U.S. insurance sector. It demonstrates

how regulators, rating agencies, and stakeholders affect incentives for insurers to manage liquidity

risk and to reach for yield.

2.1 Regulators

U.S. insurers are subject to capital requirements through the risk-based capital (RBC) system.

RBC measures the minimum amount of capital appropriate for a reporting entity to support its

overall business operations in consideration of its risk profile, including insurance risk, investment

risk, and credit risk.7 Companies that fail to comply with the capital requirements may be taken

over by state insurance departments. In principle, insurers in the U.S. are regulated at the state

level and each subsidiary of a parent insurer is subject to state laws and regulations.

6According to the U.S. Flow of Funds Accounts, the insurance sector held $2.3 trillion in bonds in 2010morethan the bond holdings of mutual and pension funds taken together (Becker and Ivashina, 2014). They also had$4,965 billion policyholders’ liabilities in 2012, which is substantial even when compared with $6,979 billion insavings deposits at U.S. depository institutions (Koijen and Yogo, 2013).

7See Appendix for detailed computation of RBC and associated regulatory actions.

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However, liquidity risk is not explicitly considered in the RBC calculation. Insurance firms are

not compelled to hold capital against liquidity risk if they are able to demonstrate that they have

an appropriate liquidity risk-management framework entailing adequate mitigating actions. The

liquidity risk-management framework is assessed by regulators quarterly and annually around

statutory fillings, and also through less frequent on-site examinations every three to five years.

Failures to pass on-site examinations and off-site financial analysis will lead to regulatory actions

ranging from appropriate corrective action plans to mandatory control of the insurer. General

insurers view their exposure to liquidity risk as a consequence of a major catastrophe, and so the

risk is usually contained within insurance, investment or credit risk.

The common law and the Employee Retirement Income Security Act also govern insurers

through prudent man regulations, which require investment managers who have fiduciary respon-

sibilities to investors to behave conservatively to avoid losses on imprudent investments (Badri-

nath, Kale, and Ryan Jr, 1996). Insurers thus have incentives to manage their portfolio liquidity

to avoid their investments being considered “imprudent investments”.

2.2 Rating Agencies

Third-party rating agencies, including Standard & Poor’s, Moody’s and A.M. Best, apply liquidity

models in their credit-risk assessment and incorporate liquidity risk into their credit ratings.

Major sources of liquidity risk vary by type of insurance. Property and casualty risks are largely

uncorrelated with market risk, as short-term concerns over liquidity likely would be triggered by a

catastrophic loss event. Life insurers, particularly annuity writers, have a higher correlation with

market movements, as their products carry equity and interest-rate risks. Nevertheless, given that

credit ratings affect profits of insurers (see Epermanis and Harrington (2006)), rational insurers

should have incentive to manage their liquidity risk.

2.3 Stakeholders

At the individual insurer level, value-maximizing stakeholders also have incentives to manage

liquidity through asset-liability management. If conditioning on compliance with regulatory re-

quirements and credit ratings remain unaffected, insurance firms have the incentive to maximize

the yield on their investments because investment portfolio return is one of the primary sources

of earnings for insurers (Becker and Ivashina, 2014).

In addition, the portfolio managers of insurers (whether in-house or outsourced) also have

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incentives to maximize the investment yield. According to NAIC (2014), annual investment-

management fees for core fixed-income mandates are generally in the range of 10 to 25 basis points

(bps) of assets under management. The performance of portfolio managers is evaluated against a

standard market metric (e.g. Barclays Capital Fixed Income Index) or a custom index designed to

meet the insurer’s investment objectives. The manager is considered to have successfully managed

the portfolio “if the manager outperforms the index by as much or more than the specified margin

while meeting the other constraints.” (NAIC, 2014)

3 Relevant Literature

Given the economic significance of insurers, particularly in debt markets, it is not surprising to

see a large growing body of literature dedicated to understanding the trading behavior of insurers.

However, it is rather surprising to see that almost all studies so far have argued that regulations

or accounting treatments drive the portfolio decisions of insurers (see Ellul, Jotikasthira, and

Lundblad (2011); Manconi, Massa, and Yasuda (2012); Ellul, Jotikasthira, Lundblad, and Wang

(2012); Merrill, Nadauld, Stulz, and Sherlund (2012, 2013); Becker and Opp (2013)). Although

this argument is understandable given that the U.S. insurance sector is highly regulated, research

evidence cannot yet conclude that all the asset-side behavior of insurers is driven by regulations

or accounting treatments.

Only very recently, one study began to consider other incentives driving insurers’ management

of their portfolios. Becker and Ivashina (2014) demonstrate that by conditioning on non-binding

capital requirements, insurance portfolios, compared to those of pension funds and mutual funds,

are systematically biased towards riskier asset classes with higher yield. This paper investigates

the following questions. Are there other profound incentives for insurers to manage their portfolios

besides regulations and accounting incentives? If so, how do these incentives govern insurers’

portfolio decisions?

I establish the hypotheses in two stages. In the first stage, I establish that besides regu-

lation and accounting incentives, liquidity risk also provides a strong incentive for insurers to

manage portfolios. In the second stage, I rely on the literature and hypothesize several potential

mechanisms through which liquidity risk affects portfolio decisions.

In a complete frictionless market, there is no incentive for insurers to manage liquidity and

hold low-yield liquid assets to smooth their claim payouts. If markets are perfectly liquid, insurers

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can smooth claims by using normal operating cash flows or capital markets at no cost. If markets

are complete, insurers are able to establish contingent contracts for the provision of cash ex-ante

for every possible state in the future. However, insurance markets and capital markets are far from

complete and frictionless. Despite capital markets (e.g. catastrophe bonds) and residual market

mechanisms (e.g. reinsurers, state guaranty funds), disaster risk is considered “uninsurable”,

implying that it is extreme expensive and impossible for insurers to write contingent contracts

ex-ante against every future disaster.

In addition, given the various frictions present in the market, external financing also becomes

very expensive or unavailable at the precise time it is most needed. Overwhelming evidence from

the literature demonstrates that market frictions cause insurers’ capital to adjust very slowly

after disaster shocks. In addition, charging a higher insurance premium after disasters is also

very difficult. As noted by Darrell Duffie in his 2010 presidential address, in the absence of other

capital shocks, extremely slow capital movements lead to slow insurance-premium adjustments

(Duffie, 2010).

Finally, given the law of large numbers, the fundamental mechanism of insurance does not

work in the case of extreme disaster events. It is extremely difficult, if not impossible, for insurers

to predict disaster claims. Large disaster claims can suddenly wipe out the liquidity pool of the

entire insurance sector, not to mention any single exposed insurer.8

If market incompleteness and market frictions induce intertemporal liquidity considerations

for insurers to hoard liquidity, do the incentives for hoarding differ among insurers? The literature

generally suggests there are two motives for hoarding liquidity, namely, a precautionary motive

and a strategic motive. The tension between the two motives is the probability of a liquidity

shock and the expected aggregate liquidity. According to Acharya, Shin, and Yorulmazer (2011)

and Gale and Yorulmazer (2013), the precautionary motive for hoarding liquidity is an increasing

function of the probability of liquidity shock. Given frictions and market incompleteness (e.g.

expensive external financing, expensive bankruptcy, aggregate illiquidity), insurers that expect a

high probability of liquidity shock will hoard liquidity to insure against future uncertain liquidity

requirements. However, for insurers that do not expect to receive a future liquidity shock, the

decision about whether to hoard depends on the expected aggregate liquidity. The endogenous

choice of insurers’ liquidity is then a declining function of aggregate liquidity. If the expected

8Anecdotally, the 2012 10-K file of the insurance company ACE Group Ltd. discloses on page 89, “Despite oursafeguards, if paid losses accelerated beyond our ability to fund such paid losses from current operating cash flows,. . . we could be required to liquidate a portion of our investments, potentially at distressed prices.”

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aggregate liquidity is low, the deviation of prices from fundamentals is high, creating a motive

to hold liquidity to exploit discounted prices. Conversely, if aggregate liquidity is expected to be

high, the expected gains from exploiting are low, leading insurers to carry low liquid buffers.

According to Brunnermeier and Pedersen (2005) and Acharya, Shin, and Yorulmazer (2011),

one must demonstrate strategic liquidity hoarding in two stages. In the first stage, the unaffected

insurers (predators) trade in the same direction as the affected insurers (prey) and hoard liquidity

in portfolios. In the second stage, the predators realize abnormal rents from exploiting the

positions of the prey. Indeed, Diamond and Rajan (2011) demonstrate that the gains from

acquiring impaired institutions at fire-sale prices make it attractive for liquid institutions to hoard

liquidity. Similarly, Acharya, Shin, and Yorulmazer (2011) and Acharya, Gromb, and Yorulmazer

(2012) demonstrate that limited pledgeability of risky cash flows, coupled with the potential

for future acquisitions at fire-sale prices, induces banks to hoard liquidity in their portfolios.

Empirical evidence of predatory trading and strategic hoarding of liquidity is very scarce in the

literature.

[INSERT FIGURE 1 HERE]

The Brunnermeier and Pedersen (2005) model can be easily recast in an insurer setting as follows.

Figure 1 provides an overview of the timeline of my framework that puts Brunnermeier and

Pedersen (2005) in the context of insurers. It can be assumed that no insurers have perfect

information about the actual cost of a disaster, though insurers know a disaster is likely. Before

the disaster (t=-1), insurers estimate their disaster claims. Those that expect large claims from

the disaster will sell bonds to hoard liquidity for precautionary motives. Knowing that having

cash today will allow insurers that expect large claims to avoid fire sales tomorrow, insurers that

do not expect claims from a disaster do not provide liquidity in the bond market, and, in fact,

further erode liquidity by trading alongside those that expect large claims. Given that insurers

that expect large claims cannot raise sufficient cash at t=-1, they are forced to trade even more

aggressively than necessary at t=0 when they are actually affected by the disaster. Those that do

not expect large claims exploit this opportunity, buying bonds at an even deeper discount than

if they had not traded at t=-1.9

9 Past and recent crises have witnessed several occasions in which such predatory trading has occurred. Forexample, predatory behavior against Long-term Capital Management in 1998 (Cai, 2009); predatory behavioragainst several hedge funds during the 2008 Global Financial Crisis is documented in Financial Times; and thememorable account of how the National City Bank, which eventually became Citibank, grew from a small treasuryunit into one of the biggest commercial banks by strategically building up liquidity and benefit from the difficultiesof its competitors in the middle of crises: see Acharya, Shin, and Yorulmazer (2011) and Cleveland and Huertas(1985) for details.

9

In such a scenario, affected insurers are considered “prey” and unaffected insurers are consid-

ered “lucky insurers” or “predators”. To be precise, lucky insurers are the hoarders that expect

some disaster claims ex-ante at time t=-1 and are not affected by disasters ex-post at time t=0.

To demonstrate evidence of predatory trading, this paper focuses on unaffected insurers that did

not expect disaster claims ex-ante at time t=-1.

The Brunnermeier and Pedersen (2005) model assumes that trades have strictly permanent

price impacts proportional to the size of the order imbalance. However, this assumption is

not critical to generating predatory trading. As noted by Bessembinder, Carrion, Tuttle, and

Venkataraman (2014), long-lived temporary price impacts could also accommodate predatory

trading. Although insurers’ liquidity sales driven by exogenous claims are unlikely to have a

permanent price impact, the illiquid bond markets allow temporary price impacts to persist for

long time. For example, Ellul, Jotikasthira, and Lundblad (2011) document that temporary

deviations from corporate-bond fundamentals can last as long as 35 weeks after initial bond

downgrades.

This paper differs from past research, not only in the fact that it studies insurance firms, but

also (and more fundamentally) because it explores individual transaction data to measure the

importance of strategic liquidity hoarding relative to traditional precautionary hoarding, and to

assess the relevance of predatory trading theories.

4 Data and Methodology

This section describes the sample compiling process, sample statistics, variable constructions, and

empirical methodology used in this paper.

4.1 Sample Construction

I compile the data for the analysis from multiple sources for the 2000:Q1 to 2009:Q4 period. I

complement National Association of Insurance Commissioners (NAIC) data on insurance firms’

holdings and transactions with the Mergent Fixed Income Securities Database (FISD) for primary

corporate-bond-market analysis, and Trade Reporting and Compliance Engine (TRACE) for

secondary-market analysis. I also extract information from the Center for Research in Security

Prices (CRSP) to control for characteristics of common stocks held by insurers.

Researchers such as Schultz (2001), Campbell and Taksler (2003), Krishnan, Ritchken, and

Thomson (2005), and Bessembinder, Maxwell, and Venkataraman (2006) use NAIC data for

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different sample periods. The data-compiling process begins with the NAIC position data. NAIC

position data provides year-end holding information, including insurance-company identification,

bond identification, bond description, acquired date, maturity date, holding size in par, and

security type. As the security type indicated by NAIC is sometimes misleading, I supplement

NAIC security type by the security type from FISD. NAIC further classifies each type of bond

into two categories: issuer obligations and single/multiple class mortgage-backed/asset-backed

securities. I exclude mortgage-backed/asset-backed securities, and only keep issuer obligations,

which represent the direct obligations of issuers.

I first partition bond position data into treasury securities, corporate bonds, and municipal

bonds. The treasury-securities sample excludes all agency bonds and requires U.S. government

bonds, U.S. government bills, U.S. government notes, and U.S. trust certificates by FISD se-

curity type. The corporate-bond sample requires industry and public utility bonds by NAIC

security type, and corporate debentures, corporate medium-term-notes (MTNs), corporate MTN

Zeros, corporate pass-through trusts, corporate payment-in-kind (PIK) bonds, corporate strips,

and corporate zeros by FISD security type. Given that municipal bonds have very limited iden-

tifications in FISD, I rely on the Municipal Securities Rulemaking Board (MSRB) to identify

municipal bonds. MSRB has overseen a mandatory transaction-reporting regime since 1997 and

reports all dealer-to-dealer and dealer-to-customer municipal bond-transaction information, in-

cluding the date of each trade, date of issue, maturity, and more importantly to this study, the

issuer CUSIPs. I use MSRB issuer CUSIPs to filter municipal bonds in the sample.

NAIC transaction data provides insurance-company identification, bond identification, trade

date, direction, price, and size. I first eliminate all data errors (e.g. negative or missing prices

or par values) and all bonds with missing or incorrect CUSIPs. To be included in the bond-

transaction sample, a bond transaction must involve counterparties in the secondary market. Non-

secondary-market transactions include pay down, maturity, called, canceled, put, and redemption.

I then partition the transaction data into treasury securities, corporate bonds, and municipal

bonds by the exact same procedures implemented for position data.

For each asset class, I then merge the position data with the transaction data to infer quarter-

end positions from year-end positions. As a final step to compile insurer-level control variables,

I merge the quarterly holding data with the NAIC InfoPro financial positions of insurance com-

panies. Several data restrictions are applied to the NAIC InfoPro data. First, I focus on publicly

listed (stock) insurance companies and exclude companies classified as mutual, reinsurers, Llyod’s,

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risk-retention group, or pure holding companies. Second, I eliminate insurance companies that

report negative direct premium written, direct premium earned, total assets, and policyholder

surplus or investment positions. Such insurance companies are not viable operating entities but

are retained in the database by NAIC for regulatory purposes such as the resolution of insolven-

cies. Finally, I winsorize by year quarter the top and bottom 1% of the claim payments.

Security-level analysis also requires controls of various security characteristics. I first collect

issue credit ratings and bond characteristics (e.g. maturity and offering amount) from FISD.

Ratings are issued by Standard & Poor’s, Moody’s, and Fitch, and are combined into a single

numerical rating for each bond according to the lowest rating assigned by the three rating agencies

at any given point in time.

Following Becker and Ivashina (2014) in tests of ex-post corporate-bond performance in the

primary market, I consider spread between the offered yield to maturity and a matched treasury

bond reported by FISD. When FISD does not report a spread, I estimate it using the yield curve

implied by other spreads reported at the same time and a bond’s yield to maturity.

I also consider ex-post corporate-bond performance in the secondary market using TRACE.

I follow Bessembinder, Kahle, Maxwell, and Xu (2009) and Dick-Nielsen (2009) to clean the

data. For a given bond, I calculate the median yield of all transactions occurring on the last

active trading day in a given quarter. The yield spread in the secondary market then takes the

difference between the median yield to maturity and the end-of-quarter yield on the treasury

bond matched on maturity.

Finally, I consider security-level liquidity as a control variable. To measure the liquidity of

bonds, I use imputed round-trip cost (IRC) following Feldhutter (2012). IRC is the difference

between the largest price in an imputed round-trip (IRT) and the smallest price in the IRT,

divided by the smallest price in the IRT. Specifically:

IRCi,t =Pmaxi,t − Pmin

i,t

Pmini,t

(1)

where Pmaxi,t is the largest price in an IRT for security i at time t and Pmin

i,t is the smallest price

for security i at time t in the IRT . If two or three trades in a given security with the same trade

size occur on the same day, and there are no other trades with the same size on that day, I define

the transaction as part of an IRT . A daily estimate of IRC is the average of IRCs on that day

for different trade sizes. I estimate quarterly IRCs by taking the median of daily estimates. A

larger IRC thus implies higher trading costs and lower liquidity.

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4.2 Proxies for Liquidity Needs

This section describes the insurance underwriting and catastrophic-claim recognition process, and

explains the empirical proxies for liquidity needs perceived by insurers. Insurance is a business in

which one pays premiums to secure financial protection against low–probability high–consequence

events (Zeckhauser, 1995). Upon receipt of the premium, insurers recognize a net written premium

after adjusting the premium collected by reinsurance assumed and ceded. The net premium

written becomes “earned” only until the time for which protection is provided has passed. As

time passes, the balance of the net written premium will decrease and insurers will recognize

the earned premium as income. By the end of the policy period, the entire unearned premium

becomes earned and the balance of the net written premium goes to zero.

Another essential function of insurance is indemnification. In the event of a disaster, affected

policyholders may lodge claims with insurers. To decide whether to accept or deny the claim,

insurers will send assessors to investigate the situation. A decision will typically be made within

three to four months. Other factors, including shortage of staff to handle increased claim volume,

complexity of claim damages, availability of vendors and safe access to damaged property, may

also contribute to the length of time it takes to settle the claim. When a claim is settled,

insurers make the claim payments and report the total indemnification as loss paid. When a

claim is unsettled, actuaries estimate loss reserves for reported and unreported claims based on

the insurer’s and the industry’s past loss experience.

For reported unsettled claims, actuaries estimate a case reserve. For unreported unsettled

claims, incurred but not reported (IBNR) reserve is reported and substantial managerial discretion

is involved in choosing the IBNR reserve level. Thus, estimation errors not only contain reserve-

management incentives, but also contain other reserve-estimation uncertainties. For example, not

all claims for current-period losses are filed by the date of the balance sheet (i.e. IBNR reserve

uncertainty). In addition, even when claims are filed in the current period, the ultimate settlement

date is highly uncertain (i.e. case-reserve uncertainty). As new information becomes available

for last-period claims, insurers revise their original estimate of loss reserves with a charge to

current-period operations (Grace and Leverty, 2012). I thus use unpaid-losses reserves (including

reserves for reported and unreported unpaid claims) to net premium earned ratio as a proxy for

expected liquidity needs ExpectedClaim. Specifically:

ExpectedClaim =Case+ IBNR

NetPremiumEarned(2)

13

I also measure expected claims by utilizing the geography of insurance business and disasters.

Among insurers that operate in the disaster-prone U.S. states, insurers that have higher than

median disaster exposure at quarter zero will be labeled as affected insurers; while insurers that

have below median disaster exposure will be labeled as unaffected insurers. The motivation for

using an alternative expected claim measure is the reserve-management incentive documented in

insurance and accounting literature. Generally, researchers have found that insurers manipulate

reserves to hide financial weaknesses or to smooth income (Petroni, 1992; Petroni and Beasley,

1996).

4.3 Empirical Design

The key scope of this study is to use liquidity demand shocks stimulated by disasters to examine

the incentives for hoarding liquidity. This setting allows me to assign high or low liquidity needs

to the affected or unaffected insurers. In a difference-in-differences framework, I can estimate the

changes resulting from a disaster event in average portfolio holdings for the unaffected insurers,

netting out the change in means for affected insurers.

For the natural experiments to be valid, they must meet two conditions: relevance and ex-

ogeneity. To meet the relevance condition, I retain only year quarter with aggregated insured

losses of more than $5 billion dollars according to Swiss Re Sigma reports. According to Swiss

Re, insured loss is defined as property and business interruption losses, excluding life and liability

insurance losses.

[INSERT TABLE 1 HERE]

Table 1 depicts the disaster-year quarters used in this study. The disaster sample includes 14

hurricanes and associated thunderstorms, one hailstorm and wildfire, and one terrorist attack

(i.e. 9/11). A single disaster event in this sample causes insured losses ranging from $3 billion

to $45 billion, with Hurricane Katrina being the most expensive disaster. The fact that some

disasters in this sample occur earlier in the year than others provides a valuable opportunity for

me to assess the exogeneity of my experiment.

[INSERT FIGURE 2 HERE]

Figure 2 depicts the geography of the sample disasters. The figure suggests that the disasters

in this sample have a very rich geographic dispersion, with some disasters occurring in coastal

states and others occurring in inland states. This unique feature also allows me to conduct several

14

case studies to verify the exogeneity of my experiment because presumably, hurricanes that affect

inland states are more surprising to insurers than those that affect coastal states.

Are there other profound incentives for insurers to manage their portfolios besides regulations

and accounting incentives? If so, how do these incentives govern insurers portfolio decisions?

The framework that puts the Brunnermeier and Pedersen (2005) and Acharya, Shin, and Yorul-

mazer (2011) models in the context of insurers suggests that one of the other incentives that

affect insurers’ portfolio decisions should be a strategic motive for hoarding liquidity ex-ante to

exploit discounted prices ex-post. This leads to the following question: do insurers actually hoard

liquidity due to a strategic motive ex-ante and engage in predatory trading ex-post? I answer

this question by examining portfolio allocations and portfolio performance. First, I investigate

whether unaffected insurers hoard liquidity and sell alongside affected insurers before reversing

their positions. I compare changes in portfolio allocations for unaffected insurers to those for

affected insurers. Specifically, I estimate the following form:

∆Holdingi,t = θ0 + θ1UnaffectedInsurerIndicatori ,0 + θIIi,t + θMMt + ε (3)

where ∆Holdingit is the quarterly changes in securities held by insurer i, scaled by the cash

holdings at the beginning of the quarter. Unaffected Insurer Indicator equals 1 if insurer i has

changes in expected claims that are lower than median in the disaster quarter t=0. Ii,t is a vector

of insurer-level control variables, including logged capital and surplus, logged RBC ratio, change in

realized claims, and tax shield. Mt is a vector of quarterly changes in market conditions, including

Pastor and Stambaugh (2003) measure of aggregate liquidity, CRSP value-weighted stock market

return, and treasury return. For detailed definitions of control variables, see Appendix. In

addition, I allow t to vary from t=-1 to t=+2 to test for pre-trends and convergence of positions

between affected and unaffected insurers.

I expect θ1 to be insignificantly different from zero when t=-1 because all insurers should

follow similar trends before disasters for the experiment to be valid and I expect all insurers to

hoard liquidity by selling illiquid assets. I expect θ1 to be significantly negative for cash holdings

and significantly positive for corporate bonds, municipal bonds, and common stocks when t=0

and t=+1. The rationale is as follows. If unaffected insurers are predators, they should allocate

less capital in cash and allocate more capital in corporate bonds, municipal bonds, and common

stocks compared with the portfolio allocations of affected insurers. At two quarters after disasters,

when t=+2, I expect θ1 to be insignificantly different from zero because portfolio allocations of

15

unaffected and affected insurers should become indistinguishable as affected insurers pay claims

and unaffected insurers provide liquidity.

I then examine portfolio performance to see whether unaffected insurers achieve abnormal

returns. I compare the changes in corporate-bond yield spread around the disasters for unaffected

insurers with those for affected insurers. To be precise, I estimate the following form:

∆Spreadkjih = β0 + β1 ∗UnaffectedInsurerIndicatorj ,i ,h + β2Bj,i,h + β3Mh + Ψjih (4)

where Spreadkjih is event-quarter k change in yield spread for bond j held by insurer i in disaster

h period. For the primary market, the yield spread is estimated as the yield difference between

the offering yield–to–maturity and a matched treasury bond, reported by Mergent FISD. For the

secondary market, the yield spread is the yield difference between the median end–of–quarter

yield–to–maturity reported by TRACE and the median end-of-quarter yield of the treasury bond

matched on maturity. When a spread is missing, I estimate it using the yield curve implied by

other spreads reported at the same time.

Unaffected Insurer Indicator equals one if insurer i that holds bond j has changes in expected

claims that are lower than median in the disaster quarter t=0 of disaster h. It equals zero for

affected insurers. Bj,i,h is a vector of time-varying and time-invariant characteristics of bond j

held by insurer i in disaster h period. The characteristics include logged issue size, bond maturity

(in years), bond illiquidity (Feldhutter (2012) IRCs computed using TRACE data), and issuers’

credit ratings.10 Mh is a vector of changes in market conditions in the disaster h period, including

Pastor and Stambaugh (2003) measure of aggregate liquidity, CRSP value-weighted stock-market

return, and treasury return. I expect β1 to be significantly positive throughout the period from

t=0 to t=+2 given previous evidence that abnormal performance in bond markets can persist as

long as 35 weeks (or nearly three quarters) (Ellul, Jotikasthira, and Lundblad, 2011).

[INSERT TABLE 2 HERE]

Table 2 reports summary statistics at insurer level and at bond level. Panel A of Table 2 reports

insurer-level statistics for the full sample, and separately for quarter t=-1, t=0, and t=+1. Both

the average change in expected claims and the average change in realized claims surge during

disaster quarters. The standard deviation of change in expected claims steadily increases from

10 The credit rating is measured in numerical terms scaling from 1 (AAA by Fitch and S&P, and Aaa by Moody’s)to 22 (lower than C by Fitch, S&P and Moody’s). The lowest rating is used when an issuer has multiple ratingsfrom different rating agencies.

16

1.03 at one quarter before the disaster to its peak of 1.64 during disaster quarter, and eventually

declines to 1.40 one quarter after the disaster. The means and standard deviations of tax shield,

logged surplus, logged assets, and logged RBC ratios are very persistent throughout the period

from t=-1 to t=+1, suggesting that any changes in portfolio allocations are unlikely to have

resulted from changes in insurers’ characteristics. Finally, the logged RBC ratio has a mean of

2.04 and a standard deviation of 1.08, suggesting that the majority of insurers in the sample

are far from triggering regulatory actions (e.g. RBC ratio of 2 or a logged RBC ratio of 0.7

triggers regulatory actions), and that their portfolio decisions are thus unlikely to be affected by

regulatory pressures.

Panel B of Table 2 reports bond-level statistics for the full sample, and separately for quarter

t=-1, t=0, and t=+1. The mean and standard deviations of both primary yield spread and

secondary yield spread increase after disasters, suggesting that disasters indeed introduce tur-

bulence in these markets. Issue size and maturity are relatively static characteristics and are

thus expected to be persistent throughout the period. The ratings of the corporate-bond sample

warrant greater attention given the potential fire-sale effects of downgraded bonds. However, it

is very unlikely for downgrades to drive portfolio decisions in this sample because the majority of

the sample bonds are investment-grade bonds by relatively conservative measurement (e.g. recall

that I use the lowest bond ratings from Standard & Poor’s, Fitch, and Moody’s ratings, and

ratings lower than 10 are investment grades.).

5 Main Results

Do insurers engage in strategic liquidity hoarding and predatory trading? To answer this ques-

tion, in Section 5.1 I first examine the portfolio decisions of insurers around disaster events. In

principle, if insurers engage in strategic liquidity hoarding and predatory trading, the first piece

of evidence should be that unaffected insurers sell alongside affected insurers before reversing

their positions. In Section 5.2, I then investigate the yield spreads of insurers’ corporate-bond

portfolios. Unaffected insurers, particularly those that expect no disaster claims ex-ante, are

hypothesized to be able to exploit affected insurers and reach for yield ex-post.

5.1 Insurer Portfolio Decisions around Disasters

In this section, I perform difference-in-differences analysis in which I compare the difference in

portfolio allocations around disasters for unaffected insurers to the difference in portfolio alloca-

17

tions around disasters for affected insurers. Given that different insurers may belong to the same

parent, I estimate t-statistics based on clustered (by insurers) standard errors.

5.1.1 A Univariate Analysis

The Brunnermeier and Pedersen (2005) and Acharya, Shin, and Yorulmazer (2011) models imply

a stark difference between traditional precautionary hoarding and strategic liquidity hoarding.

Strategic liquidity hoarding requires market participants to trade alongside each other before

trading against each other, while precautionary liquidity hoarding does not require the latter.

Models of precautionary hoarding imply that liquid holdings should be an increasing function

of the probability of liquidity shocks. Indeed, empirical evidence demonstrates that “hoarders”

who expect higher probability of liquidity shocks in future stock up liquidity by selling illiquid

assets. “Non-hoarders” also hoard liquidity but hoard to a lesser extent than hoarders. The

model lies critically on the premise that hoarders and non-hoarders have a positive expectation

about further liquidity shocks, providing incentives for non-hoarders to hoard liquidity. However,

neither have perfect information about the timing of the future liquidity shocks.

In a strategic-hoarding model, one of the critical assumptions is the predictable liquidation by

“prey”. In stark contrast to precautionary hoarding, market participants in a strategic-hoarding

model have perfect information about the timing of a future liquidation. “Predators” are the

strategic investors who do not expect a future liquidity shock, and thus have no incentive to

hoard liquidity in a precautionary nature. The only incentive remaining for them to hoard, if

any, is to gain from trading against prey in the future.

Guided by the implications of the model, I first examine the trading behavior of insurers in a

four-quarter event window around disasters. I present evidence in Figure 3 and Figure 4. Figure 3

plots average quarterly within-insurer changes in total cash holdings and decomposes the changes

into changes resulting from counterparty transactions in secondary markets, non-counterparty

transactions, and other cash-flow sources, including cash flows from operating and cash flows

from financing. Figure 4 focuses on counterparty transactions in secondary markets and traces

the cash-flow sources.

[INSERT FIGURE 3 HERE]

Figure 3 suggests that unaffected insurers trade alongside affected insurers before reversing their

positions. First, as demonstrated in the top left of Figure 3, one quarter before disasters, affected

18

and unaffected insurers trade in the same direction in secondary markets and hoard cash. This

suggests that insurers are able to predict the timing of disasters and associated liquidations.

Second, the top right of Figure 3 demonstrates that throughout the period from the disaster

quarter to one quarter after the disaster, unaffected insurers reduce cash and trade against affected

insurers in secondary markets. The positions of unaffected and affected insurers begin to converge

two quarters after disasters.

Finally, the bottom left of Figure 3 further demonstrates that unaffected insurers utilize cash

flows from non-counterparty transactions (e.g. maturing bonds, putting bonds) to trade against

affected insurers in secondary markets. Again, the behavior of insurers for non-counterparty

transactions also begins to converge two quarters after disasters.

[INSERT FIGURE 4 HERE]

Figure 4 further deconstructs the cash flows that result from counterparty transactions into

quarterly market-value changes in treasury securities, corporate bonds, municipal bonds, and

common stocks.

One quarter before disasters, unaffected insurers and affected insurers hoard liquidity and

reduce positions in treasury securities, corporate bonds, municipal bonds, and common stocks.

Throughout the period from quarter t=0 to t=+1, unaffected insurers reverse their positions and

trade against affected insurers in the corporate bond market, the municipal bond market, and

the common stock market. At quarter t=+2, the positions of unaffected insurers and affected

insurers begin to converge.

The patterns seem consistent with what predicts from the framework that puts Brunnermeier

and Pedersen (2005) and Acharya, Shin, and Yorulmazer (2011) in the context of insurers. They

also suggest that some insurers opportunistically hoard liquidity ex-ante and are lucky (e.g.

unaffected by disasters) ex-post to trade against affected insurers. To isolate predatory trading

from opportunistic trading, I partition the sample of unaffected insurers into those that did and

did not expect hurricane claims ex-ante one quarter before disasters. The insurers that did expect

hurricane claims ex-ante and are not affected ex-post are more likely to be the lucky insurers,

while the insurers that did not expect claims are more likely to be predators. I present the results

in Figure 5, Figure 6, and Figure 7.

[INSERT FIGURE 5 HERE]

Figure 5 repeats the analysis in Figure 3 but partitions unaffected insurers into those with zero

19

estimated hurricane exposure ex-ante and those with positive estimated hurricane exposure ex-

ante. As presented in Figure 5, unaffected insurers with zero estimated hurricane exposure ex-ante

account for the majority of the evidence documented in Figure 3. They hoard cash ex-ante and

reduce cash holdings ex-post in hurricane seasons (event-quarter t=0 and t=+1). However, the

unaffected insurers that had estimated positive hurricane exposure ex-ante hoard cash in hurricane

seasons (event-quarter t=0 and t=+1) presumably because they are still uncertain whether they

will be affected by potential hurricanes. They start to reduce cash holdings outside hurricane

seasons at t=+2.

[INSERT FIGURE 6 HERE]

[INSERT FIGURE 7 HERE]

Figure 6 and Figure 7 repeat the analysis in Figure 4 but partitions unaffected insurers into

those with zero estimated hurricane exposure ex-ante and those with positive estimated hurricane

exposure ex-ante.. Figure 6 compares the quarterly changes in market value of securities held

by unaffected insurers with zero estimated hurricane exposure ex-ante to the quarterly changes

of affected insurers, and Figure 7 compares the quarterly changes of unaffected insurers with

positive estimated hurricane exposure ex-ante to quarterly changes of affected insurers.

Figure 6 and Figure 7 suggest that unaffected insurers with zero estimated hurricane exposure

ex-ante account for the majority of the trading patterns documented in Figure 4. Specifically,

only insurers with zero estimated hurricane exposure sell securities to hoard cash before disasters;

insurers with positive estimated exposure do not hoard cash ex-ante. After disasters, insurers with

zero estimated exposure ex-ante trade more aggressively than insurers with positive estimated

exposure ex-ante.

These results suggest that unaffected insurers hoard cash ex-ante by selling risky securities,

and they buy back the risky securities ex-post after disasters. The evidence concentrates on the

unaffected insurers that did not expect hurricane claims ex-ante, rendering strong supports to

the hypothesis of strategic liquidity hoarding and predatory trading. That is, these unaffected

insurers seem to hoard cash ex-ante through strategic considerations to profit from investment

opportunities ex-post in the financial market.

20

5.1.2 Portfolio Allocations t=0 to t=+1: A Multivariate Analysis

The visual patterns documented above may be attributed to insurer characteristics or changes in

aggregate market conditions. Do the visual patterns survive controlling for insurer characteristics

and changes in aggregate market conditions? Table 3 reports the results of difference-in-differences

regressions that test whether, and by how much, insurers change their portfolio allocations from

quarter t=0 to quarter t=+1 in response to claim shocks stimulated by disasters at quarter t=0,

controlling for logged insurers’ surplus, logged RBC ratio, changes in realized claims, tax shields,

aggregate liquidity, stock-market returns, and treasury returns.

[INSERT TABLE 3 HERE]

Overall, the results in Table 3 confirm the documented visual patterns, and suggest that unaffected

insurers initially hoard liquidity before disasters and subsequently trade against affected insurers.

Specifically, Unaffected Insurer Indicator equals 1 if insurer i has expected claim changes that

are lower than median during the disaster quarter t=0 and 0 otherwise. The coefficient estimate

of the Unaffected Insurer Indicator compares the quarterly difference in portfolio allocations

for unaffected insurers to the quarterly difference in portfolio allocations for affected insurers.

The coefficient estimates of the Unaffected Insurer Indicator are negative for cash and treasury

securities, and positive for corporate bonds, municipal bonds, common stocks, and other risky

assets. All the estimates are economically large and statistically significant. Compared to affected

insurers, unaffected insurers reduce cash by an additional 79% and treasury securities by an

additional 45% of their pre-disaster cash balance, and increase corporate bonds by an additional

67%, municipal bonds by an additional 64%, common stocks by an additional 23%, and other

risky assets by an additional 20%, as a percentage of pre-disaster cash balance respectively.

In all specifications, I add controls for insurer characteristics and controls for overall market

conditions. The documented liquidity hoarding pertains to the investment decisions made by

insurers, and insurers might adjust their liquidity preferences due to institutional features or

overall market conditions. Given the endogenous trade-off insurers consider when they hoard

liquidity, specific concerns will arise from both the benefits of hoarding (e.g. meeting current

realized liquidity demand, taking tax advantage, meeting regulatory requirements) and the costs

of hoarding (e.g. agency costs, opportunity costs).

I first control for RealizedClaim. RealizedClaim captures current liquidity demand and in

principle, investors should use cash and liquid assets to meet current liquidity demand (Brown,

21

Carlin, and Lobo, 2010). The negative coefficients of RealizedClaim for cash, treasury securities,

and common stocks are consistent with Brown, Carlin, and Lobo (2010). That is, insurers use

their liquid assets to meet current liquidity requirements

I also control for logged surplus and capital, Log(Surplus). It captures size of insurers. The

positive coefficients of Log(Surplus) for all risky asset classes, and the negative coefficient for

cash reflect the risk appetite of insurers. To reach for yield, larger insurers seem to be able to bias

their portfolio towards higher risk. To control for regulatory constraints, I include logged RBC

ratio. Similarly, the positive coefficients of Log(RBC) for all risky asset classes and the negative

coefficient for cash reflect insurers’ desire to reach for yield while holding regulatory requirements

unaffected.

Taxation is also an important consideration in making portfolio decisions of insurers (Hen-

dershott and Koch, 1980). In addition, tax benefits are an important motivation for insurers to

manage ExpectedClaim if they can overestimate ExpectedClaim to shelter earnings (the claim

loss provision is tax deductible) (Petroni, 1992). I thus include tax shield as a control variable; tax

shield equals the sum of net income and reserve scaled by total assets. Tax-management strate-

gies of general insurers involve investing in tax-preferred securities (e.g. common stocks and

municipal bonds). Consistent with the tax-management incentive, holdings of common stocks

and municipal bonds are significantly increasing in tax shield.

Another important consideration in making portfolio decisions is potential opportunity costs,

more specifically, the cost of forgoing profitable investments. At aggregate-level, I include aggre-

gate treasury returns to control for default-free yield; I include aggregate value-weighted stock-

market returns to control stock-market conditions. In addition, I also control for aggregate market

liquidity by including the Pastor and Stambaugh (2003) measure. As expected, given an increase

in default-free bond yield, insurers reallocate funds away from risky investments. Moreover, funds

are redirected by insurers into stock markets when stock-market return is high. Finally, when

aggregate liquidity is higher, insurers generally hold less cash and invest more.

Together, I have shown that before a disaster, unaffected insurers trade alongside affected

insurers and hoard cash. One quarter after a disaster, unaffected insurers trade against affected

insurers and invest heavily in corporate bonds, municipal bonds, common stocks, and other risky

assets. The results are consistent with the hypothesis of strategic liquidity hoarding and predatory

trading. However, they are also consistent with the opportunistic trading hypothesis. Several

other important questions remain to be explored. Do the results provide evidence of predatory

22

trading or evidence of opportunistic trading? In a natural experiment framework, do treatments

and controls follow similar trends before treatment? When do unaffected insurers begin to trade

against affected insurers? Do the positions of unaffected insurers converge to those of affected

insurers, and if so, when? I will address these questions in the following sections.

5.1.3 Portfolio Allocation Around Disasters: A Multivariate Analysis

A critical assumption for a natural experiment to be valid is that treatments and controls should

have followed similar trends in the absence of treatment. To test for the parallel-trend assumption,

I examine quarter t=-1 changes in portfolio allocations. In addition, I examine the period from

t=0 to t=+2 to test whether and when there is a convergence of positions of unaffected and

affected insurers. Table 4 presents the results.

[INSERT TABLE 4 HERE]

During quarter t=-1, coefficient estimates of Unaffected Insurer Indicator are insignificant across

different assets, suggesting the portfolio allocations of affected and unaffected insurers are indis-

tinguishable before disasters and validating the parallel-trend assumption. In addition, the signs

are also consistent with the pattern observed in Figure 3 and Figure 4. One quarter before a

disaster, affected and unaffected insurers trade in the same direction and hoard liquidity.

During quarter t=0, trading begins to differentiate between affected and unaffected insurers.

Unaffected insurers begin to reduce cash and treasury bond holdings and increase the holdings of

corporate bonds and municipal bonds. Compared to affected insurers, unaffected insurers reduce

cash by an additional 44% and treasury securities by an additional 23% of their pre-disaster cash

balance, and increase the holdings of corporate bonds by an additional 59%, and municipal bonds

by an additional 60% of their pre-disaster cash balance. Although the coefficient estimates of

common stocks and other risky assets are not significant, they are positive, suggesting consistent

evidence that unaffected insurers trade against affected insurers as early as during the disaster

quarters.

I then extend the event window to two quarters after disaster quarters (t=+2) to verify

whether there is any convergence in portfolio allocations. The evidence suggests that the posi-

tions of unaffected insurers and affected insurers converge during quarter t=+2. At the second

quarter after the disaster, the quarterly changes in the holdings of cash, treasury securities, mu-

nicipal bonds, and common stocks become insignificant, and the differences in the holdings of

corporate bonds and other risky assets between unaffected insurers and affected insurers reveals

23

only marginal significance. In all specifications, I control for insurer characteristics and market

conditions to ensure observed portfolio effects are not driven by other factors. Overall, the po-

sitions of unaffected insurers two quarters after disasters are almost indistinguishable from the

positions of affected insurers in this quarter, providing evidence for a convergence of positions.

[INSERT TABLE 5 HERE]

To shed further light on predatory trading, in Table 5, I partition the unaffected insurers according

to their ex-ante expectation about hurricane claims. Panel A of Table 5 compares changes in

portfolio allocations for unaffected insurers with zero estimated hurricane exposure ex-ante to

those of affected insurers, while Panel B of Table 5 compares changes in portfolio allocations

for unaffected insurers with positive estimated hurricane exposure ex-ante to those of affected

insurers.

Insurer-level hurricane exposure is estimated as follows. At t=-1, I predict one-quarter ahead

state-level hurricane probability using past 150-year hurricane data (e.g. severity, location, timing,

etc.). I then estimate the insurer-state-level hurricane exposure as the state-level insurance market

share of the insurer, multiplied by the predicted state-level hurricane probability. Insurer-level

hurricane exposure then aggregates insurer-state-level hurricane exposure over all the states in

which the insurer operates.

Together, Panel A and Panel B suggest that the unaffected insurers, particularly those that

had zero ex-ante expectation about hurricane claims, account for the majority of the trading

behavior documented in Table 4. The most plausible reason for such insurers to hoard liquidity

ex-ante is a strategic consideration to gain profits from investment opportunities ex-post in the

financial market.

5.2 Insurer Portfolio Performance

The investigation into predatory trading and strategic liquidity hoarding is not complete without

an examination of portfolio performance during the period when unaffected insurers trade against

affected insurers. Given the data availability, I will focus on corporate bonds in this section

and examine corporate-bond yield spreads in both the primary market (in Section 5.2.1) and

the secondary market (in Section 5.2.2). If unaffected insurers are indeed the predators who

exploit the affected insurers or prey’s positions, there should be evidence of abnormal profits,

even controlling for bond characteristics and changes in aggregate market conditions.

24

5.2.1 Performance in Primary Markets

This section examines the yield spreads of corporate bonds at issuance. I focus on offering yield

spreads and perform a difference-in-differences analysis, in which I compare the quarterly changes

in yield spreads for corporate bonds held by unaffected insurers to those held by affected insurers.

Table 6 presents the results.

[INSERT TABLE 6 HERE]

The dependent variable is event-quarter change in yield spread for bonds held by insurers. Yield

spread at issuance is the yield difference between the offering yield to maturity and a matched

treasury bond, reported by Mergent FISD. When a spread is missing from Mergent FISD, I

estimate it using the yield curve implied by other spreads reported at the same time. Given

that insurers trade against each other only throughout the period from quarter t=0 to t=+2, I

examine quarterly changes in yield spread for this period. Given that one issuer may issue multiple

bonds, I cluster standard errors at issuer-level. Overall, the results in Table 6 demonstrate that

during each quarter, up to two quarters after the disaster, the average change in yield spread for

corporate-bond portfolios held by unaffected insurers is statistically and economically significantly

higher than the corporate-bond portfolios held by affected insurers.

Specifically, Column (1) of Table 6 presents the results for disaster quarters. At issuance, the

difference-in-differences estimator Unaffected Insurer Indicator suggests that the average change

in yield spread of bonds held by unaffected insurers is 16 bps higher than the average change in

yield spread of bonds held by affected insurers. The intercept indicates the quarterly change in

yield spread for bonds held by affected insurers, and it is negative and statistically significant,

implying that affected insurers are unable to “reach for yield” during disaster quarters. That is,

unaffected insurers reach for yield to a greater extent than do affected insurers during disaster

quarters.

In Column (3) of Table 6, I examine the quarterly change in yield spread one quarter after

disasters. Again, compared to affected insurers, unaffected insurers strongly reach for yield.

The average change in yield spread for bonds held by unaffected insurers is 31 bps higher than

the average change in the yield of bonds held by affected insurers. The intercepts are -13 bps

and are marginally significant, implying that affected insurers are still unable to reach for yield.

Unaffected insurers continue to reach for yield and increase the yield spread by 18 bps (31 bps −

13 bps) in their bond investments.

25

As presented in Column (5) of Table 6, the reported reaching for yield behavior in quarter

t=+1 continues into the second quarter after disasters. Unaffected insurers continue to reach for

yield to a greater extent than do affected insurers. The change in yield spread for bonds held by

unaffected insurers is on average 24 bps higher than the change in yield for bonds held by affected

insurers. The intercept is significantly negative, implying that affected insurers are still not able

to reach for yield in the second quarter after disasters. Part of the reason affected insurers are not

able to reach for yield is the commitment to indemnify policyholders for verified insured damage

upon request or within several months. Indeed, Figure 8 demonstrates that affected insurers have

significant realized claims throughout the sample period.

However, the observed effects on yield spreads may be driven by the preference of insurers

for certain bond or issuer characteristics. If these features are correlated with yield, the observed

effect on yield spreads is difficult to distinguish from investors’ preferences. In Columns (2),

(4), and (6) of Table 6, I control for offering amount, bond maturity, bond illiquidity for each

testing period respectively. The offering amount captures the size of the issue. It may also proxy

for liquidity if larger bond issues are more liquid. Since most bonds are held to maturity by

insurers, maturity also captures liquidity of the bond (longer maturity bonds are less liquid).

Indeed, Edwards, Harris, and Piwowar (2007) find that bid-ask spreads decrease with bond-issue

size. I also explicitly include a bond-illiquidity measure as a control. The negative coefficient

on Log(OfferingAmount) and the positive coefficient on Maturity and Illiquidity are consistent

with the notion that insurers hold fewer liquid bonds, and hold such bonds only if they can be

compensated by high yield as argued in Becker and Ivashina (2014).

The documented effects on yield may also reflect credit-risk preference. I thus include a

numerical credit rating variable Rating, scaling from 1 (corresponding to the highest credit rating)

to 22 (corresponding to the lowest credit rating). Across all specifications and across all testing

periods, Rating is significantly positive, suggesting that insurers prefer to hold bonds with lower

ratings and they do so if they can be compensated with high yield (Becker and Ivashina, 2014).

Models of Brunnermeier and Pedersen (2005) and Acharya, Shin, and Yorulmazer (2011) em-

phasize the importance of aggregate liquidity. Low aggregate liquidity creates an environment in

which any deviation of prices from fundamental values will be high, creating strong incentives for

unaffected insurers to exploit affected insurers’ positions. In addition, investment opportunities

in other markets are important. Bad investment opportunities in markets outside corporate bond

markets may also create incentives for insurers to reach for yield in the corporate bond market.

26

Therefore, I include Pastor and Stambaugh (2003) aggregate liquidity measure, CRSP value-

weighted stock-market return, and risk-free yield as controls. The estimates for these controls in

Columns (2), (4), and (6) of Table 6 are all negative and the majority are significant, implying

that market conditions do matter. Lower aggregate liquidity, lower stock-market return, lower

risk-free yield are associated with increases in yield spread in primary markets.

Controlling for the above bond and issuer characteristics and overall market conditions, the

difference-in-differences estimate is still statistically significantly positive, though the magnitude

is reduced. This implies that a part of the yield-spread changes is due to the preference of insurers

for certain bond and issuer characteristics and overall market conditions.

5.2.2 Performance in Secondary Markets

Although many investment activities occur in primary markets, the secondary corporate bond

market provides a further laboratory to test my hypothesis. As such, this section further ex-

plores the yield-spread effect in secondary corporate bond markets. In particular, I examine how

movements in liquidity demand by insurers alter yield spreads in secondary markets. I conduct

difference-in-differences analysis and cluster standard errors by issuers. I focus on the spread

to maturity on the over-the-counter secondary-market transactions reported in TRACE. Table 7

reports the results.

[INSERT TABLE 7 HERE]

The dependent variable is event-quarter change in yield spread for bonds held by insurers in the

secondary market. The yield spread is estimated as the median yield to maturity on the last

trading day of the quarter minus the median end-of-quarter yield on the treasury bond matched

on maturity. In Table 7, Columns (1) and (2) present the results for the disaster quarters,

Columns (3) and (4) present the results for quarter t=+1, and Columns (5) and (6) present the

results for quarter t=+2. The evidence in the secondary market in general confirms the evidence

in the primary market.

Column (1) of Table 7 reports the results for the testing period from t=-1 to t=0. The

results in Column (1) suggest that unaffected insurers are not able to exploit affected insurers

in the secondary market. The intercept is -0.15 and significant and the difference-in-differences

estimate is not significant, suggesting that affected insurers are unable to reach for yield to a

similar extent as do unaffected insurers. Specifically, the yield spread for bonds held by affected

27

insurers decreases 15 bps during disaster quarters, while the yield spread for bonds held by

unaffected insurers decreases 18 bps.

I further extend the analysis into quarter t=+1 and t=+2. Similar to the evidence documented

in Table 6 for the primary market, the results in Columns (3) and (5) suggest that unaffected

insurers reach for yield to a greater extent than do affected insurers. The difference-in-differences

estimate is economically large and statistically significant. Compared to the change in yield spread

for bonds held by affected insurers, the change in yield spread for bonds held by unaffected insurers

is 37 bps greater in quarters t=+1 and t=+2. In both quarters, the intercepts are significantly

negative, implying that affected insurers are unable to reach for yield.

Again, the observed yield-spread effects may also be driven by insurers’ preference or changes

in market conditions. In Columns (2), (4), and (6) of Table 7, I then include logged issue size,

bond maturity, bond liquidity, credit rating of issuer, aggregate liquidity, stock-market return,

and risk-free yield as control variables. Consistent with the findings in Table 6, smaller issue,

longer maturity, higher illiquidity, lower credit rating, lower aggregate liquidity, lower stock-

market return, and lower risk-free yield are associated with higher yield spread in secondary

markets.

5.2.3 Performance for “Predatory” and “Lucky” Insurers

To shed further light on the evidence of strategic liquidity hoarding and predatory trading, in this

section, I partition unaffected insurers into those with zero ex-ante expectations about hurricane

claims (e.g. the predator insurers) and those with positive ex-ante expectations (e.g. the lucky

insurers), and compare each with affected insurers respectively in Table 8 and Table 9.

[INSERT TABLE 8 HERE]

[INSERT TABLE 9 HERE]

Table 8 focuses on unaffected insurers that expect no disaster claims ex-ante, and Table 9 focuses

on unaffected insurers that expect some disaster claims ex-ante. In each table, Panel A investi-

gates the performance in the primary market and Panel B investigates the performance in the

secondary market. The dependent variables are the quarterly changes in yield spreads and they

are defined as before in Table 6 for primary markets and in Table 7 for secondary markets re-

spectively. Together, the results suggest that the unaffected insurers with zero ex-ante expected

hurricane claims account for the majority of the evidence of reaching for yield documented in

28

Table 6 and Table 7, providing strong support for the hypothesis of strategic liquidity hoarding

and predatory trading.

Specifically, in Table 8, I focus on a situation in which opportunistic trading is unlikely (i.e.

insurers that expect no disaster claims ex-ante). I regress event-quarter changes in yield spreads

on an Unaffected Unexposed Indicatorj,i,h that equals one if insurer i that holds bond j has

expected claims changes that are lower than median in the disaster quarter t=0 of disaster h

and had zero ex-ante expectation about hurricane claims at quarter t=-1. It equals zero for all

affected insurers.

Insurer-level ex-ante expectation about hurricane claims is estimated as follows. At t=-

1, I predict one-quarter ahead state-level hurricane probability using past 150-year hurricane

data (e.g. severity, location, timing, etc.). I then estimate the insurer-state-level hurricane

exposure as the state-level insurance market share of the insurer, multiplied by the predicted

state-level hurricane probability. Insurer-level hurricane exposure then aggregates insurer-state-

level hurricane exposure over all the states in which the insurer operates.

In Table 8 Panel A for primary corporate bond markets, throughout the four-quarter event

window, the Unaffected Unexposed Indicator is significantly positive and the intercept is sig-

nificantly negative. This evidence suggests that unaffected insurers with zero ex-ante expected

hurricane exposure reach for yield to a greater extent than do affected insurers. Similar evidence

is observed in Panel B for secondary corporate bond markets.

Table 9 focuses on insurers that expect some hurricane claims ex-ante and that are not affected

by hurricanes ex-post (i.e. lucky insurers). Specifically, in Table 9, I regress event-quarter changes

in yield spreads on an Unaffected Exposed Indicatorj,i,h that equals one if insurer i that holds

bond j has expected claims changes that are lower than median in the disaster quarter t=0 of

disaster h and had positive ex-ante expectation about hurricane claims at quarter t=-1. It equals

zero for all affected insurers. Insurer-level ex-ante expectation about hurricane claims is estimated

in the same manner as in Table 8.

Table 9 suggests that there is some evidence that lucky insurers reach for yield to a greater

extent than do affected insurers, but they can only do so outside the hurricane seasons. It seems

that inside hurricane seasons, insurers that expect some hurricane claims ex-ante cannot out-

perform affected peers because they need to hold up cash as a precaution against future claims

from potential hurricanes. Outside hurricane seasons when future claims are unlikely, these lucky

insurers are able to reallocate capital to fund profitable investments and thus outperform affected

29

peers. Together, the evidence suggests that the majority of the superior performance for unaf-

fected insurers in Table 6 and Table 7 is attributed to those that are unlikely to be opportunistic

insurers, thus supporting the hypothesis of strategic liquidity hoarding and predatory trading.

Before reaching my final conclusion, I present some robustness tests in the following section.

6 Robustness

This section provides robustness checks, including a test using a single-state stand-alone subsam-

ple, a test using a propensity score matched subsample, a full sample test using an alternative

liquidity-need measure, and several case studies. My main results pass all robustness tests, ren-

dering further support to the hypothesis of strategic liquidity hoarding and predatory trading.

6.1 Single-state Stand-alone Insurers

The first potential alternative explanation of my results is from the perspective of the internal

capital market. Specifically, given that most of the sample insurers are operating within an

insurance conglomerate, the documented phenomena of predatory trading and strategic liquidity

hoarding might simply reflect transmission of capital in the internal capital market. Most of

the sample insurers also operate in more than one state, which makes it possible even for a

stand-alone insurer to transfer funds from unaffected states to affected states in which funds

are mostly needed. However, since I have manually eliminated any transactions that I believe

might be internal capital-market transactions, it is very unlikely for the internal capital market

to affect my results.11 Nevertheless, I address these two concerns together by using a subsample

of single-state stand-alone insurers.

[INSERT TABLE 10 HERE]

To find single-state stand-alone insurers, I rely on NAIC insurers’ demographic file and the state

page from financial statements. The NAIC demographic file contains information about whether

an insurer is operating within an insurance group. For any insurer, the state page reports the

direct premium written for each U.S. state. The single-state stand-alone insurers are the insurers

that are not affiliated with any other insurance entities and have a positive direct premium

written in only one state. Table 10 reports the results. The results are consistent with my main

11I manually scan names of purchasers and vendors. Any names that do not indicate a counterparty are dropped.Names that I believe indicate an internal transaction include the following: conversion, swap, adjustment, affiliate,exchange, reorganization, transfer, split, restructure, dividend, refund, company trade, company managed, in-house,interaccount, intercompany, intermanager, interfund.

30

results, though the magnitude is smaller. Given that the majority of the internal transactions are

removed by manually checking names of purchasers and vendors, the most likely reason is that

the single-state stand-alone subsample only contains a fraction (10%) of the full sample.

6.2 Propensity Score Matching

A concern about the reliability of the results may also arise from the difference-in-differences

framework, that is, the expected liquidity needs may be correlated with realized liquidity needs,

and the effect we observe is merely the effect of realized liquidity needs. In addition, firm char-

acteristics (e.g. financial constraints) might drive the results. For example, holding the liquidity

requirement constant, less financially constrained insurers are more likely to engage in predatory

trading. I address this concern by matching each insurer in the treatment group with a “twin”

insurer in the control group before disasters. I use propensity score matching, where the score is

computed by estimating the following logistic function:

Pr(Treatmenti = 1|Vi) = Λ(θ0 + θ′1Vi) (5)

where Treatmenti equals 1 if insurer i is in the affected group and 0 if insurer i is in the unaffected

group, and Λ is the cumulative density function of the logarithm distribution. Vi is a vector of

matching variables, including RealizedClaim, Log(Asset), Log(Surplus), and Log(RBC). I

then use the estimates of this logistic regression to compute the probability (the “score”) that

an insurer is assigned in a group. I then apply one-to-one match treatment and control insurers

according to their scores. The propensity score matching procedure generates 3,488 unique twins.

INSERT TABLE 11 HERE

First, Panel B of Table 11 evaluates the effectiveness of the propensity score matching. I report

summary statistics, p-value of test of equality of medians, and p-value of the Kolmogorov–Smirnov

test of equality of distributions for each matching variables. The p-values suggest that affected

and unaffected insurers are indistinguishable along the dimension of realized claims, total assets,

total surplus, and the RBC ratio. Propensity score matching does a good job in matching sample

insurers.

Panel A of Table 11 reports the results from the difference-in-differences regression using

the matched twins. Unaffected Insurer Indicator equals one for unaffected insurers and zero

for affected peers. The results are consistent with the main tests, suggesting that changes in

31

observable insurer characteristics are not likely to drive the results.

6.3 Geographical Disaster Exposures

A large body of accounting literature has provided considerable evidence of uncertainty and

subjectivity in insurers estimating loss reserves. Petroni and Beasley (1996) report that more than

90% of the insurer-years in their sample exhibit material estimation errors. Although such errors

may be attributed to unanticipated economic factors, there is considerable evidence that insurers

intentionally bias reported loss reserves. For example, many researchers have demonstrated that

insurers manage loss reserves to hide financial weaknesses (see Petroni (1992); Harrington and

Danzon (1994); Gaver and Paterson (2004)) or to smooth income (see Weiss (1985); Beaver,

McNichols, and Nelson (2003)). To ensure my results are not biased by reserve-management

incentives, I perform the full sample test using geographic disaster exposure of insurers as an

alternative measure of expected claims.

To estimate insurer-level disaster exposure, I employ NAIC state pages from financial state-

ments and the Spatial Hazard Events and Losses Database for the United States (SHELDUS).

For each insurer, the state page from financial statements reports written premium and unpaid

claims at the state level. SHELDUS also reports insured damage at the state level. These two

databases enable me to compute each insurer’s exposure to disaster. More precisely, I compute

insurer-level disaster exposure as follows:

DisasterExposurei =∑s

Premiumi,s∑iPremiumi,s

(6)

where I compute a state-level insurance coverage (Premiumi,s/∑iPremiumi,s) for insurer i in

disaster state s as the insurer i direct premium written in the disaster state s as a percentage of

the aggregate state-level insurance premium written by all insurers operating in the state. I then

estimate disaster exposure for insurer i by aggregating the state-level insurance coverages over

all the states in which the insurer operates. Table 12 reports the results.

INSERT TABLE 12 HERE

The results are very similar to the main results in their magnitude and significance, implying

that the results reported in this research are unlikely to have been affected by potential reserve-

management incentives.

32

6.4 Case Studies

Another concern about the reliability of the results is the exogeneity condition of the experiment.

Although disasters are exogenous to insurer characteristics, the timing of disasters is relatively

easy to predict (e.g. the hurricane season in U.S. is usually in the third quarter of a year).

Therefore, given that hurricanes have strong seasonal pattern and represent ten-sixteenths of the

sample disasters, I take advantage of the richness of my data and perform several case studies to

check the validity of the exogeneity condition, even though each case will only use a very small

fraction of the full sample. I pick up several disasters that I believe are surprising to insurers.

Table 14 reports the results.

INSERT TABLE 14 HERE

Panel A of Table 14 reports the results for coastal disaster states during early disaster seasons

(e.g. in the second quarter). Panel B of Table 14 reports the results for inland disaster states

during early disaster seasons. Panel C of Table 14 reports the results for a unique disaster (i.e. a

wildfire in California). The wildfire case is interesting for the following reason. All other disasters

in the sample period (apart from the 9/11 attacks) are hurricanes and associated thunderstorms,

the timing of which is quite easy to predict.12 The timing of a wildfire that causes billions of

dollars of losses to the insurance sector is almost unpredictable. The results from all these three

cases are consistent with the main results, further supporting the hypothesis that insurers engage

in predatory trading and strategic liquidity hoarding.

7 Conclusion

This paper empirically examines predatory trading and the strategic motives for hoarding liquidity

in portfolios. Relying on disasters to generate exogenous shocks to liquidity demand, I am able

to track changes in portfolio liquidity around the disasters. I demonstrate that before disasters,

insurers that expect large disaster claims hoard liquidity; insurers that expect no disaster claims

also hoard liquidity by withdrawing liquidity from corporate bond markets. Throughout the

disaster periods, unaffected insurers, particularly those expecting no disaster claims ex-ante,

reverse their positions and trade against affected insurers. During this period, the corporate

bond portfolios held by insurers that expect no disaster claims ex-ante perform much better than

12The reason I do not examine the 9/11 attacks separately is that there is no appropriate control group. Theinsurance sector did not seriously consider a terrorism risk and terrorism insurance is barely sold before the 9/11attacks.

33

the corporate bond portfolios held by the affected insurers. The evidence is robust to single-state

stand-alone insurers, matched insurer peers, and alternative measures of expected liquidity needs.

Given this evidence, the most plausible explanation is that some unaffected insurers strategically

hoard liquidity ex-ante to exploit discounted prices ex-post.

The evidence documented by this paper contributes to the understanding of predatory trading

and strategic liquidity hoarding. Models of Brunnermeier and Pedersen (2005) and Acharya, Shin,

and Yorulmazer (2011) also imply that fire sales are highly prevalent during periods in which

predators exploit prey. Although this paper does not directly examine fire sales, the documented

yield-spread effects point to the possibility of corporate bond fire sales that are driven entirely

by exogenous liquidity events. In future, I will work to explore the fire sales and take advantage

of the exogeneity to investigate the real effects of fire sales and other potential externalities. In

addition, the extensive hoarding of cash by insurers mirrors the cash-holding puzzle for industrial

firms. Therefore, another interesting extension of this research would be to examine whether

industrial firms hoard cash due to a desire to reach for yield in the financial market.13

13 Indeed, as recently noted by Duchin, Gilbert, Harford, and Hrdlicka (2014), the standard measure of “cash” inprior studies lumps together cash and risky assets, and the investments in risky securities are highly concentratedin firms with excess liquidity and a low demand for precautionary savings. Duchin, Gilbert, Harford, and Hrdlicka(2014) further argue that the concentration of risky investments can be explained by an agency conflict combinedwith a desire to reach for yield in the financial market.

34

A Appendix

A.1 Variable Definitions

Variables Definitions

Loss In insurance literature, “loss” refers to some injury, harm, damage or fi-nancial detriment that a person sustains. Depending on the terms of theinsurance contract and local law, some losses may be insured and othersmay be uninsured.

Loss Paid The loss paid, or net loss paid, is the total claim paid out to policyholdersby insurers in the current accounting period. The net loss paid equals directloss paid adjusted by reinsurance. Any reinsurance assumed will increasenet loss paid and any reinsurance recovered from reinsurers will reduce netloss paid.

Loss Unpaid Loss unpaid, or loss reserve, expresses the amount the insurer expects topay out in the future to cover indemnity payments that will become due onpolicies already written for losses that have already been incurred (reportedand unreported [IBNR])

Loss Incurred Loss incurred is sustained losses, paid or not, during a specified period. Lossincurred equals losses paid during the period plus the difference betweenending unpaid losses and beginning unpaid losses.

RealizedClaim This is the main variable that measures realized liquidity needs perceived byinsurers. For insurer i at time t, RealizedClaimi,t is the ratio of net lossespaid to net premium earned.

ExpectedClaim This is the main variable that measures the expected liquidity needs per-ceived by insurers at time t. For insurer i at time t, it is estimated as thedifference between time t net losses unpaid and time t− 1 net losses unpaid,scaled by time t net premium earned

Tax Shield One of the motivations for insurers to manage loss reserve is tax bene-fits. The principle is that overestimating reserves provides an opportu-nity for a firm to shelter earnings (loss reserves are pretax deductionsfrom earnings). One of the measures used in the literature is the valueof the tax shield (Grace, 1990). Specifically, TaxShield = (NetIncome +EstimatedReserve)/TotalAsset.

Log(RBC) This refers to the natural logarithm value of the RBC ratio. For details ofRBC ratio, please see Appendix A.3

Yield Spread Primary-market yield spread is estimated as the spread between the offeringyield to maturity and a matched treasury bond, reported by FISD. WhenFISD does not report a spread, I estimate it using the yield curve impliedby other spreads reported at the same time, as well as by a bond’s yieldto maturity. The yield spread in a secondary market takes the differencebetween the median yield to maturity of all transactions occurring on thelast active trading day in a given quarter and the end-of-quarter yield onthe treasury bond matched on duration.

35

Variables Definitions

Log(Offering Amount) This is the natural logarithm value of the par value of debt initiallyissued.

Illiquidity This refers to the Feldhutter (2012) IRCs. Please see Appendix A.2for details.

Maturity This refers to the years between the current transaction date and thematurity.

Ratings I consider ratings from three major rating agencies, including Stan-dard & Poor’s, Moody’s, and Fitch. I then covert their ratings intonumerical values (e.g. AAA to 1, AA+ to 2, C to 21). At the end of agiven quarter, a bond rating is the highest numerical value or lowestrating among Standard & Poor’s, Moody’s, and Fitch.

Treasury Return This refers to the return on a constant-maturity 10-year treasury note,measured as the product of the change in yield from the previoustrading day to current trading day, and the maturity of the bond onthe previous trading day. Quarterly treasury return is compoundedover the quarter. The data of daily yield of a constant-maturity 10-year treasury note is downloaded from the Federal Reserve Bank ofSt Louis.

Aggregate Liquidity I measure aggregate liquidity using Pastor and Stambaugh (2003)measure. Specifically, I obtain monthly aggregate Pastor and Stam-baugh (2003) liquidity measures from Wharton Research Data Ser-vices (WRDS) Fama-French & Liquidity Factors. The quarterlyPastor and Stambaugh (2003) aggregate liquidity measure then takesthe average of monthly measures.

Stock Market Return This refers to the aggregate value-weighted stock-market return fromCRSP.

36

A.2 Implementation of Liquidity Measures

A.2.1 Imputed Round-trip Cost

Feldhutter (2012) develops a liquidity measure based on the dispersion of traded prices around

the market-wide consensus valuation.

IRCi,t =Pmaxi,t − Pmin

i,t

Pminit

(7)

where Pmaxi,t is the largest price in an imputed round-trip transaction (or IRT ) for security i at

time t and Pmini,t is the smallest price for security i at time t in the IRT . If two or three trades

in a given security with the same trade size occur on the same day, and there are no other trades

with the same size on that day, I define the transaction as part of an IRT. A daily estimate of

IRC is the average of IRCs on that day for different trade sizes, and I estimate quarterly IRC by

taking the median of daily estimates.

A.2.2 Aggregate Liquidity Measure

Pastor and Stambaugh (2003) develop a measure of price impact termed Gamma by running the

following regression:

ret+1 = θ + φrt + (Gamma)sign(ret )(V olumet) + εt (8)

where ret is the stock’s excess return above the CRSP value-weighted market return on day t and

V olumet is the dollar volume on day t. Intuitively, Gamma measures the reverse of the previous

day’s order-flow shock. The Gamma should have a negative sign. The larger the absolute value of

Gamma, the larger the implied price impact. Monthly aggregate Pastor and Stambaugh (2003)

liquidity measures are obtained from WRDS FamaFrench & Liquidity Factors. Quarterly Pastor

and Stambaugh (2003) aggregate liquidity is the average of monthly Pastor and Stambaugh (2003)

measures.

37

A.3 The Risk-based Capital System

I present the calculation of the RBC ratio for property/liability insurance companies and the

corresponding regulatory actions. Generally, RBC measures the minimum amount of capital

appropriate for a reporting entity to support its overall business operations in consideration of

its size and risk profile. A separate RBC formula exists for each type of insurance, reflecting the

differences in the economic environments. The formula does not necessarily capture every single

risk exposure. It focuses on the material risks that are common for the particular insurance type.

The RBC ratio is defined as follows:

RBCRatio =Statutory Surplus

Authorized Control Level RBC(9)

where Statutory Surplus is the book value of equity of the insurance company and the Authorized

Control Level (ACL) RBC is the minimum amount of capital required to avoid regulatory actions.

ACL RBC is calculated as follows.

First, Risk Charges are calculated using the following formula:

Risk Charges = R0 +√

(R1)2 + (R2)2 + (R3)2 + (R4)2 + (R5)2 (10)

R0 = Insurance affiliate investment and (non-derivative) off-balance-sheet risk

R1 = Invested asset – risk fixed-income investments

R2 = Invested asset – risk equity investments

R3 = Credit risk (non-reinsurance plus one-half reinsurance credit risk)

R4 = Loss-reserve risk, loss-reserve growth risk, and one-half reinsurance credit risk

R5 = Premium risk and premium growth risk

Second, the variable Risk Charges is reduced through a covariance adjustment to reflect the effect

of diversification. Finally, the result after diversification adjustments is the ACL RBC.

Depending on the capital deficiency indicated by the RBC ratio, regulators have a range of

preventive and corrective actions to select. The actions are designed to provide for early regula-

tory intervention to correct problems before insolvencies become inevitable, thereby minimizing

the number and adverse impact of insolvencies. The potential regulatory actions and the corre-

sponding RBC ratios are presented as follows:

38

Risk-based Capital Ratios and Regulatory Actions

RBC Ratio Range Regulatory Action Explanation

≥ 200% No Action No action is required

[150%, 200%] Company Action Level The insurance company must prepare a re-port to the regulator. The report shouldidentify the current financial conditions,propose plans to correct the financialproblems, and provide projections of thefinancial conditions with and without theproposed corrections

[100%, 150%] Regulatory Action Level The state insurance commissioner is re-quired to examine and analyze the insur-ance company’s operations. If necessary,the commissioner may issue appropriatecorrective orders to address the company’sfinancial problems

[70%, 100%] Authorized Control Level This is the first point at which the reg-ulator may take control of the insurancecompany. The commissioner has the le-gal grounds to rehabilitate or liquidate thecompany.

[0%, 70%] Mandatory Control Level The insurance commissioner is required toseize the company.

39

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42

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ers

that

exp

ect l

arge

cla

ims

are

prec

autio

nary

insu

rers

2)

Hoa

rder

s th

at e

xpec

t sm

all c

laim

s ar

e op

port

unis

tic in

sure

rs

3) H

oard

ers

that

exp

ect n

o cl

aim

s ar

e pr

edat

ory

insu

rers

Som

e in

sure

rs a

re a

ffect

ed b

y a

disa

ster

.

1) N

ot a

ll pr

ecau

tiona

ry in

sure

rs a

re a

ffect

ed

2) N

ot a

ll op

port

unis

tic in

sure

rs a

re a

ffect

ed

3) N

o pr

edat

ory

insu

rers

are

affe

cted

Una

ffect

ed in

sure

rs e

x-po

st a

t t =

0 th

us in

clud

e

1) P

reda

tory

insu

rers

that

did

not

exp

ect d

isas

ter

clai

ms

ex-a

nte

at t

= −

1 2)

Luc

ky in

sure

rs (p

reca

utio

nary

and

opp

ortu

nist

ic)

that

did

exp

ect s

ome

clai

ms

ex-a

nte

at t

= −

1

Fig

ure

1:

Tim

elin

e.

Th

isfi

gu

rep

rese

nt

the

tim

elin

eof

afr

amew

ork

that

pu

tsB

run

ner

mei

eran

dP

eder

sen

(200

5)an

dA

char

ya,

Shin

,an

dY

oru

lmaz

er(2

011)

mod

els

inth

eco

nte

xt

ofin

sure

rs.

43

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2001 Quarter 2

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2001 Quarter 3

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2002 Quarter 2

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2003 Quarter 2

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2003 Quarter 3

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2003 Quarter 4

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2004 Quarter 3

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2005 Quarter 3

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2005 Quarter 4

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2006 Quarter 2

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2008 Quarter 2

WA

MTND

MN

SD

WY

ID

OR

CA

NV

UT

AZ

CO

NMOK

KS

NEIA

MO

AR

WI

IL

KY

IN

MI

OH

WV

TN

MS AL GA

SC

NC

FL

VA

PA

NY

VT

NH

ME

MA

CT

RI

NJ

DEMD

AK

HI

TX LATX

LA

Disaster Zone, 2008 Quarter 3

Figure 2: Geography of Disasters, 2001-2009. This figure describes the geography of majorinsured disaster quarters in U.S., 2001-2009. Major insured disaster quarters are quarters withmore than $5 billion aggregated insured losses according to Swiss Re Sigma reports. Disasterstates, highlighted in red, are states that have more than $100 million insured losses for thedisaster quarter according to SHELDUS and Swiss Re

44

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Cou

nter

part

y/Ca

sht-

1 fo

r Una

ffect

ed In

sure

rs

∆Cou

nter

part

y/Ca

sht-

1 fo

r Affe

cted

Insu

rers

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑦𝑦 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for U

naffe

cted

Insu

rers

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑦𝑦 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Non

-Cou

nter

part

y/Ca

sht-

1 fo

r Una

ffect

ed In

sure

rs

∆Non

-Cou

nter

part

y/Ca

sht-

1 fo

r Affe

cted

Insu

rers

Δ𝑁𝑁𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑦𝑦

𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

Δ𝑁𝑁

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑦𝑦

𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Cas

ht /C

asht

-1 fo

r Una

ffect

ed In

sure

rs

∆Cas

ht /C

asht

-1 fo

r Affe

cted

Insu

rers

∆𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for A

ffect

ed In

sure

rs

∆𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for U

naffe

cted

Insu

rers

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Oth

erCa

sh/C

asht

-1 f

or U

naffe

cted

Insu

rers

∆O

ther

Cash

/Cas

ht-1

for

Affe

cted

Insu

rers

Δ𝑂𝑂

𝐶𝐶ℎ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

Δ𝑂𝑂

𝐶𝐶ℎ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶ℎ

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for A

ffect

ed In

sure

rs

Fig

ure

3:

Qu

art

erl

yC

han

ges

inIn

sure

rs’

Cash

Hold

ings

aro

un

dD

isast

ers

.T

he

figu

res

plo

tav

erag

equ

arte

rly

wit

hin

-firm

chan

ges

into

tal

cash

hol

din

gs

(top

left

)an

dd

econ

stru

ctth

ech

ange

sin

toca

shch

ange

sre

sult

ing

from

cou

nte

rpar

tytr

ansa

ctio

ns

inse

curi

tym

arke

ts(t

opri

ght)

,n

on-c

ou

nte

rpart

ytr

ansa

ctio

ns,

e.g.

mat

uri

ng

orp

utt

ing

bon

ds

(bot

tom

left

),an

dch

ange

sin

oth

erca

shfl

ows,

e.g.

cash

flow

sfr

omop

erat

ing

and

fin

an

cin

g(b

ott

omri

ght)

.A

llch

an

ges

are

scal

edby

the

cash

bal

ance

atth

eb

egin

nin

gof

the

qu

arte

r.A

tth

een

dof

the

dis

aste

rqu

arte

rt

=0,

Ip

art

itio

nth

esa

mp

lein

toaff

ecte

dan

du

naff

ecte

din

sure

rsac

cord

ing

toth

em

edia

nch

ange

inco

nte

mp

oran

eou

sex

pec

ted

clai

ms.

Qu

arte

rly

chan

ges

inca

shh

old

ings

are

then

plo

tted

for

each

qu

arte

rin

afo

ur-

qu

arte

rev

ent

win

dow

for

affec

ted

insu

rers

(str

iped

bar

s)an

du

naff

ecte

din

sure

rs(d

otte

db

ars

).

45

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Cor

pora

te(t

)/∆Ca

sh(t

) for

Una

ffect

ed In

sure

rs

∆Cor

pora

te(t

)/∆Ca

sh(t

) for

Affe

cted

Insu

rers

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑒𝑒𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑒𝑒𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Tre

asur

y(t)

/∆Ca

sh(t)

for U

naffe

cted

Insu

rers

∆T

reas

ury(

t)/∆

Cash

(t) fo

r Affe

cted

Insu

rers

Δ𝑇𝑇𝐶𝐶𝑒𝑒𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶𝑇𝑇/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

Δ𝑇𝑇𝐶𝐶𝑒𝑒𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶𝑇𝑇 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Mun

icip

al(t

)/∆Ca

sh(t)

for U

naffe

cted

Insu

rers

∆M

unic

ipal

(t)/∆

Cash

(t) fo

r Affe

cted

Insu

rers

Δ𝑀𝑀

𝑇𝑇𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝑙𝑙𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

Δ𝑀𝑀

𝑇𝑇𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝑙𝑙𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Sto

ck(t

)/∆Ca

sh(t

) for

Una

ffect

ed In

sure

rs

∆Sto

ck(t

)/∆Ca

sh(t

) for

Affe

cted

Insu

rers

Δ𝑆𝑆𝐶𝐶𝐶𝐶𝑀𝑀𝑘𝑘

𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

Δ𝑆𝑆𝐶𝐶𝐶𝐶𝑀𝑀𝑘𝑘

𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

Fig

ure

4:Q

uart

erl

yC

han

ges

inM

ark

et

Valu

eof

Secu

riti

es

held

by

Insu

rers

(Un

aff

ecte

dIn

sure

rsv.s

.A

ffecte

dIn

sure

rs)

Th

efi

gure

sp

lot

qu

arte

rly

mar

ket

valu

ech

an

ges

for

trea

sury

secu

riti

es(t

ople

ft),

corp

orat

eb

ond

s(t

opri

ght)

,m

un

icip

alb

ond

s(b

otto

mle

ft),

and

com

mon

stock

s(b

otto

mri

ght)

.T

he

mar

ket

valu

ech

anges

resu

lton

lyfr

omco

unte

rpar

tytr

ansa

ctio

ns

inse

curi

tym

arket

s.F

orea

chas

set

clas

s,th

em

arke

tva

lue

chan

ge

ism

easu

red

asth

ed

iffer

ence

bet

wee

nth

em

arke

tva

lue

atth

een

dof

qu

arte

rt

and

the

mar

ket

valu

eat

the

end

ofqu

arte

rt-

1,sc

aled

by

the

cash

bal

ance

atth

een

dof

qu

art

ert-

1.A

tth

een

dof

the

dis

aste

rqu

arte

rt

=0,

Ip

arti

tion

the

sam

ple

insu

rers

into

affec

ted

and

un

affec

ted

insu

rers

acc

ord

ing

toth

em

edia

nch

an

ge

inco

nte

mp

oran

eou

sex

pec

ted

clai

ms.

Qu

arte

rly

chan

ges

inm

arke

tva

lue

ofse

curi

ties

are

then

plo

tted

for

each

qu

art

erin

afo

ur-

qu

art

erev

ent

win

dow

for

affec

ted

insu

rers

(str

iped

bar

s)an

du

naff

ecte

din

sure

rs(d

otte

db

ars)

.

46

-100

%

-80%

-60%

-40%

-20%0%20%

40%

60%

80%

100%

-10

12

∆Cas

ht /C

asht

-1 fo

r Una

ffect

ed In

sure

rs w

ith

Posit

ive

Hurr

ican

e Ex

posu

re

∆Cas

ht /C

asht

-1 fo

r Affe

cted

Insu

rers

∆𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for A

ffect

ed In

sure

rs

∆𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1f

or U

naffe

cted

Insu

rers

with

Po

sitiv

e H

urri

cane

Exp

osur

e

-100

%

-80%

-60%

-40%

-20%0%20%

40%

60%

80%

100%

-10

12

∆Cas

ht /C

asht

-1 fo

r Una

ffect

ed In

sure

rs w

ith Z

ero

Hurr

ican

e Ex

posu

re

∆Cas

ht /C

asht

-1 fo

r Affe

cted

Insu

rers

Δ𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for A

ffect

ed In

sure

rs

Δ𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for U

naffe

cted

Insu

rers

with

Z

ero

Hur

rica

ne E

xpos

ure

Fig

ure

5:

Qu

art

erl

yC

han

ges

inIn

sure

rs’C

ash

Hold

ings

(wit

hF

iner

Part

itio

nof

Un

aff

ecte

dIn

sure

rs)

Th

efi

gure

sp

lot

aver

age

qu

arte

rly

wit

hin

-firm

chan

ges

into

tal

cash

hol

din

gs.

Th

ele

ftfi

gure

focu

ses

onu

naff

ecte

din

sure

rsw

ith

zero

ex-a

nte

exp

ecta

tion

abou

thu

rric

ane

clai

ms,

wh

ile

the

right

figu

refo

cuse

son

un

affec

ted

insu

rers

wit

hpo

siti

veex

-ante

exp

ecta

tion

abou

thu

rric

ane

clai

ms.

Ex-p

ost

atd

isas

ter

qu

arte

rt

=0,

Ip

arti

tion

the

sam

ple

insu

rers

into

affec

ted

and

un

affec

ted

insu

rers

acco

rdin

gto

the

med

ian

qu

arte

rly

chan

gein

conte

mp

oran

eous

exp

ecte

dcl

aim

s.I

then

furt

her

refi

ne

the

un

aff

ecte

din

sure

rsa

mp

leu

sin

gin

sure

r-le

vel

ex-a

nte

exp

ecta

tion

abou

thu

rric

ane

clai

ms.

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insu

rer-

leve

lex

-ante

exp

ecta

tion

ab

out

hu

rric

ane

clai

ms

ises

tim

ated

asfo

llow

s.A

tt

=-1

,I

pre

dic

ton

e-qu

arte

rah

ead

stat

e-le

vel

hu

rric

ane

pro

bab

ilit

yu

sin

gp

ast

150-

yea

rhu

rric

ane

dat

a(e

.g.

seve

rity

,lo

cati

on

,ti

min

g,et

c.).

Ith

enes

tim

ate

the

insu

rer-

stat

e-le

vel

hu

rric

ane

exp

osu

reas

the

stat

e-le

vel

insu

ran

cem

arke

tsh

are

ofth

ein

sure

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ult

ipli

edby

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ted

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e-le

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hu

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ane

pro

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ilit

y.In

sure

r-le

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osu

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enag

greg

ates

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stat

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hu

rric

ane

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osu

reov

erall

the

stat

esin

wh

ich

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rer

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ates

.Q

uar

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ych

ange

sin

cash

hol

din

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eth

enp

lott

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rea

chqu

arte

rin

afo

ur-

qu

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ent

win

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for

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ted

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(str

iped

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s)an

du

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ecte

din

sure

rs(d

otte

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ars)

.

47

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0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

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t) fo

r Una

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sure

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r Affe

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Insu

rers

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑒𝑒𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

with

Ze

ro H

urri

cane

Exp

osur

e Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑒𝑒𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Tre

asur

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∆Cas

h(t)

for U

naffe

cted

In

sure

rs

∆Tre

asur

y(t)/

∆Cas

h(t)

for

Affe

cted

In

sure

rs

Δ𝑇𝑇𝐶𝐶𝑒𝑒𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶𝑦𝑦 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for U

naffe

cted

Insu

rers

w

ith Z

ero

Hur

rica

ne E

xpos

ure

Δ𝑇𝑇𝐶𝐶𝑒𝑒𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶𝑦𝑦 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Mun

icip

al(t)

/∆Ca

sh(t)

for U

naffe

cted

In

sure

rs

∆Mun

icip

al(t)

/∆Ca

sh(t)

for A

ffect

ed

Insu

rers

Δ𝑀𝑀𝑇𝑇𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝑙𝑙𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

w

ith Z

ero

Hur

rica

ne E

xpos

ure

Δ𝑀𝑀𝑇𝑇𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝑙𝑙𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Sto

ck(t)

/∆Ca

sh(t)

for U

naffe

cted

Insu

rers

∆Sto

ck(t)

/∆Ca

sh(t)

for A

ffect

ed In

sure

rs

Δ𝑆𝑆𝐶𝐶𝐶𝐶𝑀𝑀𝑘𝑘

𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

with

Ze

ro H

urri

cane

Exp

osur

e Δ𝑆𝑆𝐶𝐶𝐶𝐶𝑀𝑀𝑘𝑘

𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

Fig

ure

6:

Qu

art

erl

yC

han

ges

inM

ark

et

Valu

eof

Secu

riti

es

held

by

Insu

rers

(Un

aff

ecte

dIn

sure

rsw

ith

Zero

Ex-a

nte

Hu

rric

an

eE

xp

osu

rev.s

.A

ffecte

dIn

sure

rs)

Th

efi

gu

res

plo

tqu

arte

rly

mar

ket

valu

ech

ange

sfo

rtr

easu

ryse

curi

ties

(top

left

),co

rpor

ate

bon

ds

(top

righ

t),

mu

nic

ipal

bon

ds

(bott

omle

ft),

and

com

mon

stock

s(b

otto

mri

ght)

.T

he

mar

ket

valu

ech

ange

sre

sult

only

from

cou

nte

rpar

tytr

ansa

ctio

ns

inse

curi

tym

arke

ts.

For

each

asse

tcl

ass,

the

mar

ket

valu

ech

ange

ism

easu

red

asth

ed

iffer

ence

bet

wee

nth

em

arke

tva

lue

atth

een

dof

qu

arte

rt

and

the

mar

ket

valu

eat

the

end

ofqu

arte

rt-

1,

scal

edby

the

cash

bal

ance

atth

een

dof

qu

arte

rt-

1.E

x-p

ost

atth

een

dof

the

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aste

rqu

arte

rt

=0,

Ip

arti

tion

the

sam

ple

insu

rers

into

aff

ecte

dan

du

naff

ecte

din

sure

rsac

cord

ing

toth

em

edia

nch

ange

inco

nte

mp

oran

eou

sex

pec

ted

clai

ms.

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enfu

rth

erre

fin

eth

eu

naff

ecte

din

sure

rsa

mp

leto

only

incl

ud

eu

naff

ecte

din

sure

rsth

ath

adze

roex

-ante

exp

ecta

tion

abou

thu

rric

ane

clai

ms.

Insu

rer-

leve

lex

-ante

exp

ecta

tion

abou

thu

rric

ane

claim

sis

com

pu

ted

inth

esa

me

man

ner

asin

Fig

ure

5.Q

uar

terl

ych

ange

sin

mar

ket

valu

eof

secu

riti

esar

eth

enp

lott

edfo

rea

chqu

art

erin

afo

ur-

qu

art

erev

ent

win

dow

for

affec

ted

insu

rers

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iped

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s)an

du

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ecte

din

sure

rs(d

otte

db

ars)

.

48

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Cor

pora

te(t)

/∆C

ash(

t) fo

r Una

ffect

ed In

sure

rs

∆Cor

pora

te(t)

/∆C

ash(

t) fo

r Affe

cted

Insu

rers

Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑒𝑒𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

with

Po

sitiv

e H

urri

cane

Exp

osur

e Δ𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑒𝑒𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Tre

asur

y(t)/

∆Cas

h(t)

for U

naffe

cted

In

sure

rs

∆Tre

asur

y(t)/

∆Cas

h(t)

for

Affe

cted

In

sure

rs

Δ𝑇𝑇𝐶𝐶𝑒𝑒𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶𝑦𝑦 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for U

naffe

cted

Insu

rers

w

ith P

ositi

ve H

urri

cane

Exp

osur

e Δ𝑇𝑇𝐶𝐶𝑒𝑒𝐶𝐶𝐶𝐶𝑇𝑇𝐶𝐶𝑦𝑦 𝑡𝑡

/𝐶𝐶𝐶𝐶𝐶𝐶ℎ 𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Mun

icip

al(t)

/∆Ca

sh(t)

for U

naffe

cted

In

sure

rs

∆Mun

icip

al(t)

/∆Ca

sh(t)

for A

ffect

ed

Insu

rers

Δ𝑀𝑀𝑇𝑇𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝑙𝑙𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

w

ith P

ositi

ve H

urri

cane

Exp

osur

e Δ𝑀𝑀

𝑇𝑇𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝐶𝐶𝐶𝐶𝑙𝑙𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

-100

%-8

0%-6

0%-4

0%-2

0%0%20%

40%

60%

80%

100%

-10

12

∆Sto

ck(t)

/∆Ca

sh(t)

for U

naffe

cted

Insu

rers

∆Sto

ck(t)

/∆Ca

sh(t)

for A

ffect

ed In

sure

rs

Δ𝑆𝑆𝐶𝐶𝐶𝐶𝑀𝑀𝑘𝑘

𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for U

naffe

cted

Insu

rers

with

Po

sitiv

e H

urri

cane

Exp

osur

e Δ𝑆𝑆𝐶𝐶𝐶𝐶𝑀𝑀𝑘𝑘

𝑡𝑡/𝐶𝐶𝐶𝐶𝐶𝐶ℎ

𝑡𝑡−1

for A

ffect

ed In

sure

rs

Fig

ure

7:

Qu

art

erl

yC

han

ges

inM

ark

et

Valu

eof

Secu

riti

es

held

by

Insu

rers

(Un

aff

ecte

dIn

sure

rsw

ith

Posi

tive

Ex-a

nte

Hu

rric

an

eE

xp

osu

rev.s

.A

ffecte

dIn

sure

rs)

Th

efi

gu

res

plo

tqu

arte

rly

mar

ket

valu

ech

ange

sfo

rtr

easu

ryse

curi

ties

(top

left

),co

rpor

ate

bon

ds

(top

righ

t),

mu

nic

ipal

bon

ds

(bott

omle

ft),

and

com

mon

stock

s(b

otto

mri

ght)

.T

he

mar

ket

valu

ech

ange

sre

sult

only

from

cou

nte

rpar

tytr

ansa

ctio

ns

inse

curi

tym

arke

ts.

For

each

asse

tcl

ass,

the

mar

ket

valu

ech

ange

ism

easu

red

asth

ed

iffer

ence

bet

wee

nth

em

arke

tva

lue

atth

een

dof

qu

arte

rt

and

the

mar

ket

valu

eat

the

end

ofqu

arte

rt-

1,

scal

edby

the

cash

bal

ance

atth

een

dof

qu

arte

rt-

1.E

x-p

ost

atth

een

dof

the

dis

aste

rqu

arte

rt

=0,

Ip

arti

tion

the

sam

ple

insu

rers

into

aff

ecte

dan

du

naff

ecte

din

sure

rsac

cord

ing

toth

em

edia

nch

ange

inco

nte

mp

oran

eou

sex

pec

ted

clai

ms.

Ith

enfu

rth

erre

fin

eth

eu

naff

ecte

din

sure

rsa

mp

leto

on

lyin

clu

de

un

affec

ted

insu

rers

that

had

posi

tive

ex-a

nte

exp

ecta

tion

abou

thurr

ican

ecl

aim

s.In

sure

r-le

vel

ex-a

nte

exp

ecta

tion

abou

thu

rric

ane

claim

sis

com

pu

ted

inth

esa

me

man

ner

asin

Fig

ure

5.Q

uar

terl

ych

ange

sin

mar

ket

valu

eof

secu

riti

esar

eth

enp

lott

edfo

rea

chqu

art

erin

afo

ur-

qu

art

erev

ent

win

dow

for

affec

ted

insu

rers

(str

iped

bar

s)an

du

naff

ecte

din

sure

rs(d

otte

db

ars)

.

49

-0.4

0

-0.2

0

0.00

0.20

0.40

0.60

0.80

-10

12

∆Cla

im(t)

/Pre

miu

m(t-

1) fo

r Una

ffect

ed In

sure

rs

∆Clia

m(t)

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r Affe

cted

Insu

rers

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𝑅𝑅𝑃𝑃𝑚𝑚𝑡𝑡−1

for

Una

ffect

ed In

sure

rs

Δ𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑚𝑚𝑡𝑡/𝑃𝑃𝑃𝑃𝑅𝑅𝑚𝑚

𝑅𝑅𝑃𝑃𝑚𝑚𝑡𝑡−1

for

Affe

cted

Insu

rers

Fig

ure

8:

Qu

art

erl

yC

han

ges

inR

ealized

Cla

ims

aro

un

dD

isast

ers

.T

he

figu

rep

lots

aver

age

quar

terl

yw

ith

in-fi

rmch

ange

sin

real

ized

clai

ms.

Th

ech

ange

inre

ali

zed

clai

mis

com

pu

ted

as

the

chan

gein

clai

mp

aid

top

olic

yh

old

ers

scal

edby

insu

ran

cepre

miu

mea

rned

atth

eb

egin

nin

gof

the

qu

art

er.

At

the

end

of

the

dis

aste

rqu

art

ert

=0,

Ip

arti

tion

my

sam

ple

into

affec

ted

and

un

affec

ted

insu

rers

acco

rdin

gto

the

med

ian

chan

gein

conte

mp

oran

eou

sex

pec

ted

clai

ms.

Qu

arte

rly

chan

ges

inre

aliz

edcl

aim

sar

eth

enp

lott

edfo

rea

chqu

arte

rin

afo

ur-

qu

arte

rev

ent

win

dow

for

affec

ted

insu

rers

(str

iped

bars

)an

du

naff

ecte

din

sure

rs(d

otte

db

ars)

.

50

Table 1: Sample Disasters in U.S. from 2001 through 2009

This table describes insured disasters in U.S. over the 2001-2009 period. The major sourcesof information are SHELDUS database and Swiss Re Sigma reports. I retain year-quarters withaggregated insured losses (i.e. “Quarterly Insured Loss”) that are more than $5 billion. Accordingto Swiss Re, insured loss is property and business interruption losses, excluding life and liabilityinsurance losses. The column “Disasters” includes the most costly disaster event within eachyear-quarter. “Event Insured Loss” represents the insured loss associated with the quarterlymost costly disaster. “Start date” and “End date” for each disaster are obtained from SHELDUSdatabase.

Disasters Year Startdate

End date Quarter EventInsured

Loss

QuarterlyInsured

Loss

Hurricane Alison 2001 6/05/2001 6/17/2001 2 5.2 6.3

9/11 Attacks 2001 9/11/2001 9/11/2001 3 19.0 19.1

Thunderstorms 2002 4/27/2002 5/03/2002 2 3.0 5.1

Thunderstorms 2003 5/02/2003 5/11/2003 2 6.0 9.6

Hurricane Isabel 2003 9/06/2003 9/19/2003 3 3.0 5.8

Hail and Wildfire 2003 10/25/2003 11/18/2003 4 4.0 5.2

Hurricane Charley 2004 8/09/2004 8/14/2004 3 8.0 29.4

Hurricane Frances 2004 8/25/2004 9/08/2004 3 5.0 29.4

Hurricane Ivan 2004 9/02/2004 9/24/2004 3 11.0 29.4

Hurricane Jeanne 2004 9/13/2004 9/28/2004 3 4.0 29.4

Hurricane Katrina 2005 8/23/2005 8/30/2005 3 45.0 59.2

Hurricane Rita 2005 9/18/2005 9/25/2005 3 10.0 59.2

Hurricane Wilma 2005 10/15/2005 10/25/2005 4 10.0 10.8

Thunderstorms 2006 4/06/2006 4/15/2006 2 6.0 9.1

Thunderstorms 2008 5/22/2008 6/12/2008 2 7.6 12.6

Hurricane Ike 2008 9/01/2008 9/14/2008 3 24.0 25.9

51

Table

2:

Sum

mary

Sta

tist

ics

Th

ista

ble

rep

ort

sth

esu

mm

ary

stat

isti

csat

insu

rer-

leve

lin

Pan

elA

and

atb

ond

-lev

elin

Pan

elB

.Y

ield

spre

ads

are

pre

sente

din

bas

isp

oints

.R

ati

ngs

ran

ge

from

1to

22,

wit

h“1”

bei

ng

the

hig

hes

tra

tin

gan

d“2

2”b

eing

the

low

est.

Rat

ings

that

are

low

erth

an10

are

inve

stm

ent

grad

es.

Rati

ng

of10

corr

esp

ond

sto

BB

B-

for

S&

Pan

dF

itch

rati

ngs

,an

dB

aa3

for

Mood

y’s

rati

ng.

Defi

nit

ion

sof

vari

able

sca

nb

efo

un

din

Ap

pen

dix

.

Pan

el

A:

Insu

rer-

level

All

Insu

rer-

qu

arte

rs(N

=19

,423

)Q

uar

ter

t+1

Dis

aste

rQ

uar

ter

0Q

uar

ter

t-1

Mea

nS

D25

th50

th75

thM

ean

SD

Mea

nS

DM

ean

SD

∆E

xp

ecte

dC

laim

0.0

81.4

1-0

.03

0.02

0.11

0.07

1.40

0.11

1.64

0.03

1.03

∆R

eali

zed

Cla

im0.

463.9

0-0

.17

0.14

0.41

0.40

4.73

0.62

3.42

0.30

4.90

Tax

Sh

ield

0.2

70.

590.

080.

230.

390.

280.

720.

270.

510.

280.

57

Log(

Su

rplu

s)15.9

41.4

414

.96

15.8

716

.85

15.9

41.

4615

.94

1.45

15.9

51.

44

Log(

Ass

ets)

16.

611.

5915

.52

16.5

517

.65

16.6

21.

5916

.61

1.59

16.6

11.

59

Log(

RB

C)

2.04

1.08

1.44

1.97

2.53

2.05

1.09

2.04

1.08

2.04

1.07

Pan

el

B:

Bon

d-l

evel

All

Bon

d-q

uar

ters

(N=

78,3

78)

Qu

arte

rt+

1D

isas

ter

Qu

arte

r0

Qu

arte

rt-

1

Mea

nS

D25

th50

th75

thM

ean

SD

Mea

nSD

Mea

nS

D

Yie

ldS

pre

ad

(Pri

mar

y)

181.3

4134.

2190

.00

138.

0022

5.00

202.

8915

4.70

183.

8013

6.58

170.

5498

.74

Yie

ldS

pre

ad

(Sec

on

dar

y)

162.

65157

.22

70.2

912

0.20

218.

6012

5.03

147.

2813

2.65

139.

0112

9.97

89.6

7

Log(

Off

erin

gA

mou

nt)

13.

300.

8912

.61

13.2

413

.82

13.3

60.

9113

.22

0.88

13.5

30.

86

Mat

uri

ty8.6

35.

515.

008.

0010

.00

8.25

5.17

8.63

5.42

8.82

5.69

Illi

qu

idit

y*10

00.

600.

690.

130.

330.

850.

730.

780.

580.

650.

680.

74

Rat

ings

7.11

3.72

5.00

6.00

9.00

6.77

3.76

7.49

3.82

6.25

2.95

52

Table

3:

Port

foli

oA

lloca

tions

One

Quart

er

Aft

er

Dis

ast

ers

Th

ista

ble

rep

ort

sth

ere

sult

sof

diff

eren

ce-i

n-d

iffer

ence

sre

gres

sion

sth

atte

stw

het

her

,an

dby

how

mu

ch,

insu

rers

chan

geth

eir

por

tfol

ioal

loca

tion

sfr

om

qu

arte

rt

=0

toqu

art

ert

=+

1in

resp

onse

tocl

aim

shock

sst

imu

late

dby

dis

aste

rsat

qu

arte

rt

=0,

Mor

esp

ecifi

call

y,I

esti

mat

eth

efo

llow

ing

form

,∆Holding i

,t=θ 0

+θ 1

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+θ II i,t

+θ M

Mt+ε

As

ind

icat

edby

the

titl

eof

each

colu

mn

,th

ed

epen

den

tva

riab

les

are

qu

arte

rly

mar

ket

valu

ech

ange

sin

cash

,tr

easu

ryb

ond

,co

rpor

ate

bon

d,

mu

nic

ipal

bon

d,

com

mon

stock

,an

dot

her

ass

eth

old

ings

,sc

aled

by

the

cash

bal

ance

atth

eb

egin

nin

gof

the

qu

arte

r.T

he

exp

lan

ator

yva

riab

le,

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

,eq

uals

on

eif

insu

rer

ih

asex

pec

ted

clai

mch

ange

sth

atar

elo

wer

than

med

ian

du

rin

gth

ed

isas

ter

qu

arte

rt

=0

and

zero

oth

erw

ise.I i,t

isa

vect

orof

insu

rer-

leve

lco

ntr

olva

riab

les,

incl

ud

ing

logg

edca

pit

alan

dsu

rplu

s,lo

gged

RB

Cra

tio,

chan

gein

real

ized

clai

ms,

and

tax

shie

ld.M

tis

ave

ctor

of

qu

art

erly

chan

ges

inm

arke

tco

nd

itio

ns,

incl

ud

ing

Pas

tor

and

Sta

mb

augh

(200

3)m

easu

reof

aggr

egat

eli

qu

idit

y,C

RSP

valu

e-w

eighte

dst

ock

mark

etre

turn

,an

dtr

easu

ryre

turn

.F

ord

etai

led

defi

nit

ion

sof

contr

olva

riab

les,

see

Ap

pen

dix

.T

he

t–st

atis

tics

are

corr

ecte

dfo

rcl

ust

erin

gof

the

ob

serv

atio

ns

atin

sure

rle

vel

and

are

rep

orte

din

the

par

enth

eses

bel

owco

effici

ent

esti

mat

es.

Sig

nifi

can

ceat

10%

,5%

,an

d1%

are

mar

ked

by

*,**

,an

d**

*re

spec

tivel

y.

∆Cash

t=1

Cash

t=0

∆Treasury

t=1

Cash

t=0

∆Corporate

t=1

Cash

t=0

∆Municipal t=1

Cash

t=0

∆Stock

t=1

Cash

t=0

∆OtherAsset

t=1

Cash

t=0

Un

affec

ted

Insu

rer

Ind

icat

or-0

.79*

*-0

.45*

**0.

67**

*0.

64**

0.23

*0.

20**

*

=1

if<

Med

ian

(∆ExpectedClaim

0)

(-2.

17)

(-2.

68)

(5.4

4)(2

.18)

(1.7

2)(2

.59)

Inte

rcep

t0.4

9***

0.17

-0.2

1***

-0.1

1-0

.12

-0.2

8

(2.7

1)(0

.19)

(-3.

00)

(-0.

54)

(-0.

68)

(-0.

61)

Insu

rer

Con

trols

YE

SY

ES

YE

SY

ES

YE

SY

ES

Mark

etC

ontr

ols

YE

SY

ES

YE

SY

ES

YE

SY

ES

R2

0.04

0.02

0.08

0.07

0.01

0.01

Ob

serv

atio

ns

3,16

53,

165

3,16

53,

165

3,16

53,

165

53

Tab

le3

conti

nu

es

∆Cash

t=1

Cash

t=0

∆Treasury

t=1

Cash

t=0

∆Corporate

t=1

Cash

t=0

∆Municipal t=1

Cash

t=0

∆Stock

t=1

Cash

t=0

∆OtherAsset

t=1

Cash

t=0

Insu

rer

Con

trols

Rea

lize

dC

laim

s-0

.03

-0.0

10.

090.

01-0

.02

0.00

(-0.

84)

(-0.

03)

(0.6

1)(1

.09)

(1.4

0)(0

.03)

Log(

Su

rplu

s)-0

.15*

0.00

0.09

0.12

0.00

0.00

(-1.

83)

(0.0

3)(2

.24)

(1.3

8)(0

.11)

(0.1

4)

Log(

RB

C)

-0.0

50.

060.

090.

030.

080.

16**

*

(-0.

28)

(0.7

9)(1

.24)

(0.2

4)(1

.46)

(3.7

0)

Tax

Sh

eild

-0.7

30.

75*

0.18

1.45

**0.

15**

0.10

(-0.

87)

(1.8

4)(0

.65)

(2.1

4)(2

.50)

(0.5

9)

Mark

etC

on

trols

Tre

asu

ryR

etu

rn1.5

3-1

.54*

*-1

.08*

*-6

.76*

*-0

.80*

*-0

.06

(0.6

5)

(-2.

36)

(-2.

44)

(-2.

56)

(-2.

10)

(-0.

10)

Aggr

egate

Sto

ckR

etu

rns

-0.5

40.

67**

0.10

3.18

***

0.41

**0.

33

(-0.

62)

(2.4

0)(0

.70)

(3.7

6)(2

.21)

(1.3

4)

Aggr

egate

Liq

uid

ity

-3.0

7**

1.74

***

0.39

*6.

18**

0.18

1.21

***

(-2.

31)

(2.8

4)(1

.71)

(2.3

5)(0

.05)

(3.4

9)

54

Table

4:

Port

foli

oA

lloca

tions

aro

und

Dis

ast

ers

Th

ista

ble

rep

ort

sth

ere

sult

sof

diff

eren

ce-i

n-d

iffer

ence

sre

gres

sion

sth

atin

vest

igat

ep

ortf

olio

allo

cati

ons

arou

nd

dis

aste

rev

ents

.M

ore

spec

ifica

lly,

∆Holding i

,t=γ0

+γ1Time t

+γ2U

naff

ecte

dIn

sure

rIn

dic

ato

r i,0

+γ3Time t∗

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+γII i,t

+γMM

t+ε

As

ind

icat

edby

the

titl

eof

each

colu

mn

,th

ed

epen

den

tva

riab

les

are

qu

arte

rly

mar

ket

valu

ech

ange

sin

cash

,tr

easu

ryb

ond

,co

rpor

ate

bon

d,

mu

nic

ipal

bon

d,

com

mon

stock

,an

dot

her

ass

eth

old

ings

,sc

aled

by

the

cash

bal

ance

atth

eb

egin

nin

gof

the

qu

arte

r.T

he

exp

lan

ator

yva

riab

le,

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

,eq

uals

on

eif

insu

rer

ih

asex

pec

ted

clai

mch

ange

sth

atar

elo

wer

than

med

ian

du

rin

gth

ed

isas

ter

qu

arte

rt

=0

and

zero

oth

erw

ise.Time t

isa

tim

ed

um

my

vari

able

that

equ

als

one

for

even

t-qu

arte

rt,

wh

eret

isan

inte

gral∈

[−1,

+2]

.I

rep

ortγ1

andγ3

bel

owfo

rea

chas

set

clas

s.I i,t

isa

vect

or

ofin

sure

r-le

vel

contr

olva

riab

les,

incl

ud

ing

logg

edca

pit

alan

dsu

rplu

s,lo

gged

RB

Cra

tio,

chan

gein

real

ized

clai

ms,

an

dta

xsh

ield

.M

tis

avec

tor

ofqu

arte

rly

chan

ges

inm

arke

tco

nd

itio

ns,

incl

ud

ing

Pas

tor

and

Sta

mb

augh

(200

3)m

easu

reof

aggr

egat

eli

qu

idit

y,C

RS

Pva

lue-

wei

ghte

dst

ock

mark

etre

turn

,an

dtr

easu

ryre

turn

.F

ord

etai

led

defi

nit

ion

sof

contr

olva

riab

les,

see

Ap

pen

dix

.T

he

t–st

atis

tics

are

corr

ecte

dfo

rcl

ust

erin

gof

the

obse

rvati

on

sat

insu

rer

leve

lan

dar

ere

por

ted

inth

ep

aren

thes

esb

elow

coeffi

cien

tes

tim

ates

.S

ign

ifica

nce

at10

%,

5%,

an

d1%

are

mark

edby

*,

**,

and

***

resp

ecti

vely

.

∆Cash

t

Cash

t−1

∆Treasury

t

Cash

t−1

∆Corporate

t

Cash

t−1

∆Municipal t

Cash

t−1

∆Stock

t

Cash

t−1

∆OtherAsset

t

Cash

t−1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

γ1

γ3

γ1

γ3

γ1

γ3

γ1

γ3

γ1

γ3

γ1

γ3

Time −

10.

36***

0.2

50.2

90.

31-0

.02

-0.1

2-0

.65*

**0.

11-0

.03

0.03

-0.0

40.

01

(4.8

1)(1

.48)

(1.1

9)(0

.58)

(-0.

66)

(-0.

43)

(-3.

78)

(0.5

1)(-

1.23

)(0

.83)

(-0.

88)

(0.2

9)

Time 0

0.24

*-0

.44*

0.1

7**

-0.2

3**

-0.1

6**

0.59

***

-0.0

70.

60**

*-0

.12

0.18

-0.5

6**

0.12

(1.8

1)

(-1.

77)

(2.0

7)(-

2.11

)(-

2.36

)(5

.06)

(-0.

18)

(3.6

1)(-

1.12

)(1

.26)

(-2.

01)

(0.8

4)

Time +

10.4

9**

*-0

.79**

0.17

-0.4

5***

-0.2

1***

0.67

***

-0.1

10.

64**

-0.1

20.

23*

-0.2

80.

20**

*

(2.7

1)

(-2.

17)

(0.1

9)(-

2.68

)(-

3.00

)(5

.44)

(-0.

54)

(2.1

8)(-

0.68

)(1

.72)

(-0.

61)

(2.5

9)

Time +

20.0

30.

060.

29-0

.47

0.11

***

0.27

*-0

.37

0.20

-0.2

10.

05-0

.17

0.14

(0.1

3)

(0.2

2)(0

.85)

(-1.

41)

(3.2

7)(1

.69)

(-0.

52)

(0.5

2)(-

0.24

)(0

.81)

(-1.

39)

(1.2

2)

Insu

rer

Con

trol

sY

ES

YE

SY

ES

YE

SY

ES

YE

S

Mar

ket

Con

trol

sY

ES

YE

SY

ES

YE

SY

ES

YE

S

R2

0.0

20.

050.

050.

040.

010.

01

Ob

serv

atio

ns

18,

175

18,1

7518

,175

18,1

7518

,175

18,1

75

55

Table

5:

Port

foli

oA

lloca

tions

and

Ex-a

nte

Hurr

icane

Exp

osu

re

Th

ista

ble

rep

eats

the

an

alysi

sin

Tab

le4

usi

ng

fin

erp

arti

tion

sof

un

affec

ted

insu

rers

.A

gain

,I

esti

mat

eth

efo

llow

ing

form

,

∆Holding i

,t=γ0

+γ1Time t

+γ2U

naff

ecte

dIn

sure

rIn

dic

ato

r i,0

+γ3Time t∗

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+γII i,t

+γMM

t+ε

Ire

port

resu

lts

forγ1

an

dγ3.

Defi

nit

ion

sof

vari

able

sar

ed

efin

edin

the

sam

em

ann

eras

inT

able

4,ex

cep

tfo

rU

naff

ecte

dIn

sure

rIn

dic

ato

r i,0

.In

Pan

elA

,U

naff

ecte

dIn

sure

rIn

dic

ato

r i,0

equ

als

one

ifin

sure

ri

has

exp

ecte

dcl

aim

chan

ges

that

are

low

erth

anm

edia

nd

uri

ng

the

dis

aste

rqu

arte

rt

=0

an

dh

ad

zero

ex-a

nte

exp

ecta

tion

ab

out

hu

rric

an

ecl

aim

sat

tim

et

=-1

.In

Pan

elB

,U

naff

ecte

dIn

sure

rIn

dic

ato

r i,0

equ

als

one

ifin

sure

ri

has

exp

ecte

dcl

aim

chan

ges

that

are

low

erth

anm

edia

nd

uri

ng

the

dis

aste

rqu

arte

rt

=0

an

dh

adpo

siti

veex

-ante

exp

ecta

tion

abou

thu

rric

ane

clai

ms

atti

me

t=

-1.

Th

ein

sure

r-le

vel

ex-a

nte

exp

ecta

tion

ab

out

hu

rric

ane

clai

ms

ises

tim

ated

asfo

llow

s.A

tt

=-1

,I

pre

dic

ton

e-qu

arte

rah

ead

stat

e-le

vel

hu

rric

ane

pro

bab

ilit

yu

sin

gp

ast

150-y

ear

hu

rric

an

ed

ata

(e.g

.se

veri

ty,

loca

tion

,ti

min

g,et

c.).

Ith

enes

tim

ate

the

insu

rer-

stat

e-le

vel

hu

rric

ane

exp

osu

reas

the

stat

e-le

vel

insu

ran

cem

arke

tsh

are

ofth

ein

sure

r,m

ult

ipli

edby

the

pre

dic

ted

stat

e-le

vel

hu

rric

ane

pro

bab

ilit

y.In

sure

r-le

vel

hu

rric

ane

exp

osu

reth

enaggr

egate

sin

sure

r-st

ate

-lev

elhu

rric

an

eex

posu

reov

eral

lth

est

ates

inw

hic

hth

ein

sure

rop

erat

es.

Th

et–

stat

isti

csar

eco

rrec

ted

for

clu

ster

ing

ofth

eob

serv

atio

ns

atin

sure

rle

vel

an

dare

rep

orte

din

the

par

enth

eses

bel

owco

effici

ent

esti

mat

es.

Sig

nifi

can

ceat

10%

,5%

,an

d1%

are

mar

ked

by

*,**

,an

d***

resp

ecti

vel

y.

Pan

el

A:

Un

aff

ecte

dIn

sure

rsw

ith

Zero

Ex-a

nte

Hu

rric

an

eE

xp

osu

rev.s

.A

ffecte

dIn

sure

rs

∆Cash

t

Cash

t−1

∆Treasury

t

Cash

t−1

∆Corporate

t

Cash

t−1

∆Municipal t

Cash

t−1

∆Stock

t

Cash

t−1

∆OtherAsset

t

Cash

t−1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

γ1

γ3

γ1

γ3

γ1

γ3

γ1

γ3

γ1

γ3

γ1

γ3

Time −

10.

36***

0.3

20.2

90.

31-0

.02

-0.1

6-0

.65*

**-0

.34

-0.0

30.

10-0

.04

0.00

(4.8

1)(1

.60)

(1.1

9)(0

.58)

(-0.

66)

(-0.

39)

(-3.

78)

(-0.

68)

(-1.

23)

(0.3

4)(-

0.88

)(0

.50)

Time 0

0.24

*-0

.44*

**

0.17

**-0

.23*

*-0

.16*

*0.

66**

*-0

.07

0.63

**-0

.12

0.03

-0.5

6**

0.41

***

(1.8

1)

(-4.

68)

(2.0

7)(-

2.11

)(-

2.36

)(4

.40)

(-0.

18)

(2.4

0)(-

1.12

)(0

.55)

(-2.

01)

(2.8

3)

Time +

10.4

9**

*-0

.49*

0.17

-0.6

4*-0

.21*

**0.

57**

*-0

.11

0.63

**-0

.12

0.32

*-0

.28

0.28

**

(2.7

1)

(-1.

68)

(0.1

9)(-

1.85

)(-

3.00

)(3

.45)

(-0.

54)

(2.1

2)(-

0.68

)(1

.76)

(-0.

61)

(2.5

3)

Time +

20.0

30.

110.

29-0

.17

0.11

***

-0.1

2-0

.37

0.31

-0.2

10.

04-0

.17

0.14

(0.1

3)

(0.3

4)(0

.05)

(-0.

36)

(3.2

7)(-

0.43

)(-

0.52

)(0

.76)

(-0.

24)

(0.3

8)(-

1.39

)(0

.97)

Insu

rer

Con

trol

sY

ES

YE

SY

ES

YE

SY

ES

YE

S

Mar

ket

Con

trol

sY

ES

YE

SY

ES

YE

SY

ES

YE

S

R2

0.0

20.

030.

040.

040.

010.

01

Ob

serv

atio

ns

11,

577

11,5

7711

,577

11,5

7711

,577

11,5

77

56

Pan

el

B:

Un

aff

ecte

dIn

sure

rsw

ith

Posi

tive

Ex-a

nte

Hu

rric

an

eE

xp

osu

rev.s

.A

ffecte

dIn

sure

rs

∆Cash

t

Cash

t−1

∆Treasury

t

Cash

t−1

∆Corporate

t

Cash

t−1

∆Municipal t

Cash

t−1

∆Stock

t

Cash

t−1

∆OtherAsset

t

Cash

t−1

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

γ1

γ3

γ1

γ3

γ1

γ3

γ1

γ3

γ1

γ3

γ1

γ3

Time −

10.

36***

0.0

00.2

90.

00-0

.02

0.05

-0.6

5***

0.00

-0.0

30.

00-0

.04

0.03

(4.8

1)(0

.00)

(1.1

9)(0

.01)

(-0.

66)

(0.4

1)(-

3.78

)(0

.00)

(-1.

23)

(0.0

0)(-

0.88

)(1

.35)

Time 0

0.24

*-0

.54

0.1

7**

0.00

-0.1

6**

0.24

-0.0

70.

34-0

.12

0.24

-0.5

6**

0.07

(1.8

1)

(-1.

60)

(2.0

7)(0

.03)

(-2.

36)

(0.9

3)(-

0.18

)(1

.41)

(-1.

12)

(1.1

9)(-

2.01

)(0

.50)

Time +

10.4

9**

*-0

.83**

0.17

-0.3

9-0

.21*

**0.

70**

*-0

.11

0.43

-0.1

20.

01-0

.28

0.14

(2.7

1)

(-2.

07)

(0.1

9)(-

1.34

)(-

3.00

)(4

.91)

(-0.

54)

(1.4

6)(-

0.68

)(0

.08)

(-0.

61)

(1.5

5)

Time +

20.0

30.

050.

29-0

.68

0.11

***

0.27

-0.3

70.

07-0

.21

0.07

-0.1

70.

12

(0.1

3)

(0.3

4)(0

.05)

(-1.

45)

(3.2

7)(1

.23)

(-0.

52)

(0.2

3)(-

0.24

)(0

.74)

(-1.

39)

(0.9

6)

Insu

rer

Con

trol

sY

ES

YE

SY

ES

YE

SY

ES

YE

S

Mar

ket

Con

trol

sY

ES

YE

SY

ES

YE

SY

ES

YE

S

R2

0.0

10.

020.

020.

010.

010.

01

Ob

serv

atio

ns

6,59

86,

598

6,59

86,

598

6,59

86,

598

57

Table

6:

Liq

uid

ity

Hoard

ing

and

Yie

ldSpre

ad:

Pri

mary

Mark

et

Th

ista

ble

rep

orts

the

resu

lts

ofd

iffer

ence

-in

-diff

eren

ces

regr

essi

ons

that

inves

tiga

teth

epri

mar

ym

arket

per

form

ance

for

corp

orat

eb

ond

sh

eld

by

aff

ecte

dan

du

naff

ecte

din

sure

rs.

Sp

ecifi

call

y,I

esti

mat

e

∆Spreadk jih

=β0

+β1∗

Un

aff

ecte

dIn

sure

rIn

dic

ato

r j,i,h

+β2B

j,i,h

+β3M

h+

Ψjih

Th

ed

epen

den

tva

riab

les

are

even

t-qu

art

erk

chan

gein

yie

ldsp

read

for

bon

dj

hel

dby

insu

reri

du

rin

gd

isas

terh

per

iod

.T

he

yie

ldsp

read

isre

por

ted

inp

erce

nta

ge

an

dis

esti

mat

edas

the

yie

ldd

iffer

ence

bet

wee

nth

eoff

erin

gyie

ldto

mat

uri

tyan

da

mat

ched

trea

sury

bon

d,

rep

orte

dby

Mer

gent

FIS

D.

Wh

ena

spre

ad

ism

issi

ng

from

FIS

D,

Ies

tim

ate

itu

sin

gth

eyie

ldcu

rve

imp

lied

by

oth

ersp

read

sre

por

ted

atth

esa

me

tim

e.I

exam

ine

chan

ges

inyie

ldsp

read

sfr

omqu

art

erk

=-1

toqu

arte

rk

=0

inco

lum

n(1

)an

d(2

),ch

ange

sin

yie

ldsp

read

sfr

omqu

arte

rk

=0

toqu

arte

rk

=+

1in

colu

mn

(3)

an

d(4

),an

dch

an

ges

inyie

ldsp

read

sfr

omqu

arte

rk

=+

1to

k=

+2

inco

lum

n(5

)an

d(6

).U

naff

ecte

dIn

sure

rIn

dic

ato

r j,i,h

equ

als

one

ifin

sure

ri

that

hold

sb

ond

jh

as

chan

ges

inex

pec

ted

clai

ms

that

are

low

erth

anm

edia

nin

the

dis

aste

rqu

arte

rk

=0

ofd

isas

ter

h.

Iteq

ual

sze

rofo

ral

laff

ecte

din

sure

rs.B

j,i,h

isa

vect

orof

tim

e-va

ryin

gan

dti

me-

inva

rian

tch

arac

teri

stic

sof

bon

dj

hel

dby

insu

reri

for

dis

aste

rh

per

iod.

Th

ech

ara

cter

isti

csin

clu

de

logg

edis

sue

size

,b

ond

mat

uri

ty(i

nye

ars)

,b

ond

illi

quid

ity

(Fel

dh

utt

er(2

012)

IRC

sco

mp

ute

du

sin

gT

RA

CE

dat

a),

and

issu

ers’

cred

itra

tin

gs.

Th

ecr

edit

rati

ng

ism

easu

red

innu

mer

ical

term

ssc

alin

gfr

om1

(AA

Aby

Fit

chan

dS

&P

’s,

and

Aaa

by

Mood

y’s

)to

22(l

ower

than

Cby

Fit

ch,

S&

P’s

and

Mood

y’s

).T

he

low

est

rati

ng

isu

sed

wh

enan

issu

erh

asm

ult

iple

rati

ngs

from

diff

eren

tra

tin

gag

enci

es.M

his

ave

ctor

ofch

an

ges

inm

arke

tco

nd

itio

ns

du

rin

gd

isas

ter

hp

erio

d,

incl

ud

ing

Pas

tor

and

Sta

mb

augh

(200

3)m

easu

reof

aggr

egat

eliqu

idit

y,C

RS

Pva

lue-

wei

ghte

dst

ock

mark

etre

turn

,an

dtr

easu

ryre

turn

.S

eeap

pen

dix

for

det

aile

dd

efin

itio

ns

ofco

ntr

olva

riab

les.

Th

et–

stat

isti

csar

eco

rrec

ted

for

clu

ster

ing

ofth

eob

serv

ati

on

sat

issu

erle

vel

an

dare

rep

orte

din

the

par

enth

eses

bel

owco

effici

ent

esti

mat

es.

Sig

nifi

can

ceat

10%

,5%

,an

d1%

are

mar

ked

by

*,**,

an

d**

*re

spec

tive

ly.

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Un

affec

ted

Insu

rer

Ind

icat

or

=1

if<

Med

ian

(∆ExpectedClaim

0)

0.16

***

0.03

**0.

31**

*0.

24**

*0.

24**

*0.

14**

*

(6.1

3)(1

.97)

(6.1

3)(7

.06)

(4.5

4)(4

.28)

Inte

rcep

t-0

.31*

**-0

.13*

*-0

.13*

-0.3

6***

-0.4

5***

-0.6

5***

(-5.

69)

(2.1

7)(1

.68)

(-3.

65)

(-4.

96)

(-7.

13)

Bon

dC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

Mark

etC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

R2

0.01

0.58

0.01

0.69

0.03

0.79

Clu

ster

s(i

ssu

ers)

1,75

650

51,

768

481

1,23

539

2

Ob

serv

atio

ns

57,1

2517

,768

48,5

6515

,786

32,4

2011

,146

58

Tab

le6

Conti

nu

es

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Bon

dC

on

trols

Log(

Off

erin

gA

mou

nt)

-0.1

7***

-0.0

80.

00

(4.1

5)(1

.36)

(0.0

1)

Mat

uri

ty(i

nye

ars

)0.

01**

*0.

01**

0.01

***

(3.0

4)(2

.07)

(4.5

4)

Illi

qu

idit

y*10

00.

36**

*0.

51**

*0.

42**

*

(5.3

5)(7

.75)

(5.0

7)

Rat

ing

0.20

***

0.23

***

0.21

***

(16.

06)

(14.

99)

(15.

89)

Inve

stm

ent

Gra

de

0.20

***

0.23

***

0.21

***

(16.

06)

(14.

99)

(15.

89)

Mark

etC

on

trols

Aggr

egate

Liq

uid

ity

-4.5

6***

-3.3

1***

-3.4

3***

(-6.

66)

(-2.

96)

(-5.

17)

Tre

asu

ryR

etu

rn-1

.18*

*-0

.52

-5.0

8***

(-1.

95)

(-0.

56)

(-5.

23)

Sto

ckM

arke

tR

etu

rn-4

.01*

**-5

.80*

**-4

.91*

**

(-9.

25)

(-14

.38)

(-8.

98)

59

Table

7:

Liq

uid

ity

Hoard

ing

and

Yie

ldSpre

ad:

Seco

ndary

Mark

et

Th

ista

ble

rep

orts

the

resu

lts

of

diff

eren

ce-i

n-d

iffer

ence

sre

gres

sion

sth

atin

ves

tiga

teth

ese

con

dar

ym

arke

tp

erfo

rman

cefo

rco

rpor

ate

bon

ds

hel

dby

aff

ecte

dan

du

naff

ecte

din

sure

rs.

Sp

ecifi

call

y,I

esti

mat

eth

efo

llow

ing

form

,

∆Spreadk jih

=β0

+β1∗

Un

aff

ecte

dIn

sure

rIn

dic

ato

r j,i,h

+β2B

j,i,h

+β3M

h+

Ψjih

Th

ed

epen

den

tva

riab

les

are

even

t-qu

arte

rk

chan

gein

yie

ldsp

read

for

bon

dj

hel

dby

insu

reri

du

rin

gd

isas

terh

per

iod

.T

he

yie

ldsp

read

isre

por

ted

inp

erce

nta

ge

an

dis

esti

mat

edas

the

med

ian

yie

ldto

mat

uri

tyon

the

last

trad

ing

day

ofth

equ

arte

rre

por

ted

by

TR

AC

Em

inu

sth

em

edia

nen

d-o

f-qu

art

eryie

ldon

the

trea

sury

bon

dm

atch

edon

mat

uri

ty.

Wh

ena

spre

adis

mis

sin

gfr

omT

RA

CE

,I

esti

mat

eit

usi

ng

the

yie

ldcu

rve

imp

lied

by

oth

ersp

read

sre

por

ted

atth

esa

me

tim

e.I

exam

ine

chan

ges

inyie

ldsp

read

sfr

omqu

arte

rk

=-1

toqu

arte

rk

=0

inco

lum

n(1

)an

d(2

),ch

ange

sin

yie

ldsp

read

sfr

omqu

arte

rk

=0

toqu

arte

rk

=+

1in

colu

mn

(3)

and

(4),

and

chan

ges

inyie

ldsp

read

sfr

omqu

arte

rk

=+

1to

k=

+2

inco

lum

n(5

)an

d(6

).U

naff

ecte

dIn

sure

rIn

dic

ato

r j,i,h

equ

als

one

ifin

sure

ri

that

hol

ds

bon

dj

has

chan

ges

inex

pec

ted

clai

ms

that

are

low

erth

anm

edia

nin

the

dis

aste

rqu

arte

rk

=0

of

dis

aste

rh.

Iteq

uals

zero

for

all

affec

ted

insu

rers

.B

j,i,h

isa

vect

orof

tim

e-va

ryin

gan

dti

me-

inva

rian

tch

arac

teri

stic

sof

bon

dj

hel

dby

insu

reri

for

dis

ast

erh

per

iod

.T

he

char

acte

rist

ics

incl

ud

elo

gged

issu

esi

ze,

bon

dm

atu

rity

(in

year

s),

bon

dil

liqu

idit

y(F

eld

hu

tter

(201

2)IR

Cs

com

pu

ted

usi

ng

TR

AC

Ed

ata

),an

dis

suer

s’cr

edit

rati

ngs

.T

he

cred

itra

tin

gis

mea

sure

din

nu

mer

ical

term

ssc

alin

gfr

om1

(AA

Aby

Fit

chan

dS

&P

’s,

and

Aaa

by

Mood

y’s

)to

22(l

ower

than

Cby

Fit

ch,

S&

P’s

and

Mood

y’s

).T

he

low

est

rati

ng

isu

sed

wh

enan

issu

erh

asm

ult

iple

rati

ngs

from

diff

eren

tra

tin

gagen

cies

.M

his

ave

ctor

of

chan

ges

inm

arke

tco

nd

itio

ns

du

rin

gd

isas

ter

hp

erio

d,

incl

ud

ing

Pas

tor

and

Sta

mb

augh

(200

3)m

easu

reof

aggre

gate

liqu

idit

y,C

RS

Pva

lue-

wei

ghte

dst

ock

mar

ket

retu

rn,

and

trea

sury

retu

rn.

See

app

end

ixfo

rd

etai

led

defi

nit

ion

sof

contr

olva

riab

les.

Th

et–

stat

isti

csare

corr

ecte

dfo

rcl

ust

erin

gof

the

obse

rvat

ion

sat

issu

erle

vel

and

are

rep

orte

din

the

par

enth

eses

bel

owco

effici

ent

esti

mat

es.

Sig

nifi

can

ceat

10%

,5%

,an

d1%

are

mark

edby

*,

**,

an

d**

*re

spec

tive

ly.

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Un

affec

ted

Insu

rer

Ind

icat

or

=1

if<

Med

ian

(∆ExpectedClaim

0)

-0.0

3-0

.01

0.37

***

0.14

***

0.37

***

0.09

***

(-0.

90)

(-0.

32)

(4.7

5)(4

.17)

(5.0

0)(2

.81)

Inte

rcep

t-0

.15*

-0.1

3**

-0.0

6-0

.05

-0.3

4***

-0.3

0***

(-1.

92)

(-2.

17)

(-0.

53)

(-0.

57)

(-3.

11)

(-3.

87)

Bon

dC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

Mark

etC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

R2

0.01

0.54

0.01

0.58

0.03

0.59

Clu

ster

s(i

ssu

ers)

621

572

570

527

469

428

Ob

serv

atio

ns

26,7

5318

,204

22,0

2015

,270

14,6

1510

,396

60

Tab

le7

Conti

nu

es

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Bon

dC

on

trols

Log(

Off

erin

gA

mou

nt)

-0.1

3***

-0.0

5-0

.05

(3.0

7)(1

.03)

(0.0

1)

Mat

uri

ty(i

nye

ars

)0.

02**

*0.

02**

0.03

***

(5.2

7)(3

.47)

(6.8

7)

Illi

qu

idit

y*10

00.

47**

*0.

57**

*0.

51**

*

(7.4

4)(7

.55)

(4.7

2)

Rat

ing

0.22

***

0.23

***

0.21

***

(22.

38)

(15.

31)

(11.

53)

Mark

etC

on

trols

Aggr

egate

Liq

uid

ity

-4.0

8***

-4.2

7***

-3.1

4***

(-6.

04)

(-4.

35)

(-3.

82)

Tre

asu

ryR

etu

rn-2

.83*

*-2

.78*

**-3

.04*

*

(-4.

76)

(-2.

77)

(-2.

50)

Sto

ckM

arke

tR

etu

rn-4

.56*

**-5

.94*

**-4

.80*

**

(-11

.40)

(-13

.08)

(-5.

61)

61

Table

8:

Corp

ora

teB

ond

Perf

orm

ance

for

Insu

rers

wit

hZ

ero

Ex-a

nte

Hurr

icane

Exp

osu

re

Th

ista

ble

rep

eats

the

an

aly

sis

inT

ab

le6

and

Tab

le7

usi

ng

affec

ted

insu

rers

and

un

affec

ted

insu

rers

that

had

zero

ex-a

nte

exp

ecta

tion

abou

thu

rric

an

ecl

aim

s.S

pec

ifica

lly,

Ies

tim

ate

the

foll

owin

gfo

rm,

∆Spreadk jih

=β0

+β1∗

Un

aff

ecte

dU

nex

pose

dIn

dic

ato

r j,i,h

+β2B

j,i,h

+β3M

h+

Ψjih

Ire

port

coeffi

cien

tes

tim

ate

sfo

rβ0

andβ1

inth

ista

ble

.D

efin

itio

ns

ofco

ntr

olva

riab

les

and

dep

end

ent

vari

able

sar

ed

efin

edin

the

sam

em

ann

eras

inT

able

6an

dT

ab

le7.

Inb

oth

pan

els,

Un

aff

ecte

dU

nex

pose

dIn

dic

ato

r j,i,h

equ

als

one

ifin

sure

ri

that

hol

ds

bon

dj

has

chan

ges

inex

pec

ted

clai

ms

that

are

low

erth

anm

edia

nin

the

dis

ast

erqu

arte

rk

=0

ofd

isas

ter

han

dh

adze

roex

-ante

exp

ecta

tion

abou

thu

rric

ane

clai

ms

atk

=-1

.It

equ

als

zero

for

all

affec

ted

insu

rers

.T

he

insu

rer-

level

ex-a

nte

exp

ecta

tion

abou

thu

rric

ane

clai

ms

ises

tim

ated

asfo

llow

s.A

tk

=-1

,I

pre

dic

ton

e-qu

arte

rah

ead

stat

e-le

vel

hu

rric

an

ep

rob

abil

ity

usi

ng

pas

t15

0-ye

arhu

rric

ane

dat

a(e

.g.

seve

rity

,lo

cati

on,

tim

ing,

etc.

).I

then

esti

mat

eth

ein

sure

r-st

ate-

leve

lhu

rric

an

eex

pos

ure

asth

est

ate

-lev

elin

sura

nce

mar

ket

shar

eof

the

insu

rer,

mu

ltip

lied

by

the

pre

dic

ted

stat

e-le

vel

hu

rric

ane

pro

bab

ilit

y.In

sure

r-le

vel

hu

rric

an

eex

posu

reth

enaggr

egate

sin

sure

r-st

ate-

leve

lhu

rric

ane

exp

osu

reov

eral

lth

est

ates

inw

hic

hth

ein

sure

rop

erat

es.

Pan

elA

and

Pan

elB

exam

ine

yie

ldsp

read

sin

the

pri

mary

an

dth

ese

con

dar

yco

rpor

ate

bon

dm

arke

tre

spec

tivel

y.T

he

t–st

atis

tics

are

corr

ecte

dfo

rcl

ust

erin

gof

the

obse

rvati

on

sat

issu

erle

vel

and

are

rep

ort

edin

the

par

enth

eses

bel

owco

effici

ent

esti

mat

es.

Sig

nifi

can

ceat

10%

,5%

,an

d1%

are

mar

ked

by

*,**

,an

d**

*re

spec

tivel

y.

Pan

el

A:

Pri

mary

Mark

et

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Un

affec

ted

Un

exp

osed

Ind

icato

r0.3

5***

0.08

***

0.17

**0.

17**

0.24

**0.

17*

(8.1

7)(3

.49)

(2.4

8)(1

.99)

(2.5

1)(1

.84)

Inte

rcep

t-0

.31**

*-0

.13*

*-0

.13*

-0.3

6***

-0.4

5***

-0.6

5***

(-5.

69)

(2.1

7)(1

.68)

(-3.

65)

(-4.

96)

(-7.

13)

Bon

dC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

Mar

ket

Con

trols

NO

YE

SN

OY

ES

NO

YE

S

R2

0.0

10.

380.

010.

380.

030.

45

Clu

ster

s(i

ssu

ers)

1,695

504

1,65

947

51,

145

387

Ob

serv

ati

on

s42,8

2018

,594

29,8

8713

,823

22,5

7110

,728

62

Pan

el

B:

Secon

dary

Mark

et

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Un

affec

ted

Un

exp

osed

Ind

icato

r0.1

2***

0.05

*0.

42**

*0.

32**

0.47

**0.

36*

(2.8

5)(1

.91)

(5.7

0)(4

.43)

(5.8

5)(5

.26)

Inte

rcep

t-0

.15*

-0.1

3**

-0.0

6-0

.05

-0.3

4***

-0.3

0***

(-1.

92)

(-2.

17)

(-0.

53)

(-0.

57)

(-3.

11)

(-3.

87)

Bon

dC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

Mar

ket

Con

trols

NO

YE

SN

OY

ES

NO

YE

S

R2

0.0

10.

40.

010.

370.

020.

38

Clu

ster

s(i

ssu

ers)

607

572

544

521

442

422

Ob

serv

ati

on

s20,2

7819

,474

14,2

3213

,836

10,5

3110

,361

63

Tab

le9:

Corp

ora

teB

ond

Perf

orm

ance

for

Insu

rers

wit

hP

osi

tive

Ex-a

nte

Hurr

icane

Exp

osu

re

Th

ista

ble

rep

eats

the

an

aly

sis

inT

ab

le6

and

Tab

le7

usi

ng

affec

ted

insu

rers

and

un

affec

ted

insu

rers

that

had

pos

itiv

eex

-ante

exp

ecta

tion

abou

thu

rric

an

ecl

aim

s.S

pec

ifica

lly,

Ies

tim

ate

the

foll

owin

gfo

rm,

∆Spreadk jih

=β0

+β1∗

Un

aff

ecte

dE

xpose

dIn

dic

ato

r j,i,h

+β2B

j,i,h

+β3M

h+

Ψjih

Ire

por

tco

effici

ent

esti

mate

sfo

rβ0

an

dβ1

inth

ista

ble

.D

efin

itio

ns

ofva

riab

les

are

defi

ned

inth

esa

me

man

ner

asin

Tab

le6

and

Tab

le7,

exce

pt

for

Un

aff

ecte

dE

xpose

dIn

dic

ato

r j,i,h

.In

both

pan

els,

Un

aff

ecte

dE

xpose

dIn

dic

ato

r j,i,h

equ

als

one

ifin

sure

ri

that

hol

ds

bon

dj

has

chan

ges

inex

pec

ted

claim

sth

atare

low

erth

anm

edia

nin

the

dis

aste

rqu

arte

rk

=0

ofd

isas

ter

han

dh

adpo

siti

veex

-ante

exp

ecta

tion

abou

thu

rric

ane

clai

ms

atk

=-1

.It

equ

als

zero

for

all

aff

ecte

din

sure

rs.

Th

ein

sure

r-le

vel

ex-a

nte

exp

ecta

tion

abou

thu

rric

ane

clai

ms

ises

tim

ated

asfo

llow

s.A

tk

=-1

,I

pre

dic

tone-

quar

ter

ahea

dst

ate

-lev

elhu

rric

ane

pro

bab

ilit

yu

sin

gp

ast

150-

year

hu

rric

ane

dat

a(e

.g.

sever

ity,

loca

tion

,ti

min

g,et

c.).

Ith

enes

tim

ate

the

insu

rer-

state

-lev

elhu

rric

ane

exp

osu

reas

the

stat

e-le

vel

insu

rance

mar

ket

shar

eof

the

insu

rer,

mu

ltip

lied

by

the

pre

dic

ted

stat

e-le

vel

hu

rric

ane

pro

bab

ilit

y.In

sure

r-le

vel

hu

rric

ane

exp

osu

reth

enag

greg

ates

insu

rer-

stat

e-le

vel

hu

rric

ane

exp

osu

reov

eral

lth

est

ates

inw

hic

hth

ein

sure

rop

erat

es.

Pan

elA

and

Pan

elB

exam

ine

yie

ldsp

read

sin

the

pri

mar

yan

dth

ese

con

dar

yco

rpor

ate

bon

dm

arke

tre

spec

tive

ly.

Th

et–

stat

isti

csar

eco

rrec

ted

for

clu

ster

ing

ofth

eob

serv

atio

ns

at

issu

erle

vel

and

are

rep

orte

din

the

par

enth

eses

bel

owco

effici

ent

esti

mat

es.

Sig

nifi

can

ceat

10%

,5%

,an

d1%

are

mark

edby

*,**

,an

d***

resp

ecti

vely

.

Pan

el

A:

Pri

mary

Mark

et

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Un

affec

ted

Exp

osed

Ind

icat

or0.

030.

010.

37**

0.48

**0.

070.

04

(0.4

1)

(0.2

1)(6

.41)

(7.0

5)(0

.82)

(0.3

5)

Inte

rcep

t-0

.31**

*-0

.13*

*-0

.13*

-0.3

6***

-0.4

5***

-0.6

5***

(-5.

69)

(2.1

7)(1

.68)

(-3.

65)

(-4.

96)

(-7.

13)

Bon

dC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

Mar

ket

Con

trols

NO

YE

SN

OY

ES

NO

YE

S

R2

0.01

0.41

0.01

0.42

0.04

0.51

Clu

ster

s(i

ssu

ers)

1,6

76

499

1,73

048

61,

136

380

Ob

serv

ati

on

s33,8

1215

,447

37,0

6816

,465

18,4

938,

912

64

Pan

el

B:

Secon

dary

Mark

et

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Un

affec

ted

Exp

osed

Ind

icat

or0.

020.

010.

32**

*0.

10**

*0.

020.

01

(1.0

2)

(0.5

6)(4

.54)

(4.4

4)(0

.66)

(0.1

6)

Inte

rcep

t-0

.15*

-0.1

3**

-0.0

6-0

.05

-0.3

4***

-0.3

0***

(-1.

92)

(-2.

17)

(-0.

53)

(-0.

57)

(-3.

11)

(-3.

87)

Bon

dC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

Mar

ket

Con

trols

NO

YE

SN

OY

ES

NO

YE

S

R2

0.01

0.41

0.01

0.38

0.07

0.43

Clu

ster

s(i

ssu

ers)

600

566

561

530

433

406

Ob

serv

ati

on

s16,7

3416

,076

16,2

4115

,755

8,04

37,

873

65

Table

10:

Robust

ness

1–

Sin

gle

-sta

teSta

nd-a

lone

Insu

rers

Th

ista

ble

rep

ort

sth

ere

sult

sof

diff

eren

ce-i

n-d

iffer

ence

sre

gres

sion

sth

atre

pea

tth

ean

alysi

sin

Tab

le4

usi

ng

only

sin

gle-

stat

est

and

-alo

ne

sub

-sam

ple

insu

rers

.S

pec

ifica

lly,

Ies

tim

ate

the

foll

owin

gfo

rm,

∆Holding i

,t=δ 0

+δ 1Time t

+δ 2

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+δ 3Time t∗

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+δ II i,t

+δ M

Mt+ε

Ire

portδ 3

inth

eta

ble

.A

sin

dic

ate

dby

the

titl

eof

each

colu

mn

,th

ed

epen

den

tva

riab

les

are

qu

arte

rly

mar

ket

valu

ech

ange

sin

cash

,tr

easu

ryb

ond

,co

rpora

teb

ond

,m

un

icip

al

bon

d,

com

mon

stock

,an

dot

her

asse

th

old

ings

,sc

aled

by

the

cash

bal

ance

atth

eb

egin

nin

gof

the

qu

arte

r.T

he

exp

lan

ator

yva

riab

le,

Un

aff

ecte

dIn

sure

rIn

dic

ato

r,eq

ual

son

eif

insu

rer

ih

asex

pec

ted

clai

mch

ange

sth

atar

elo

wer

than

med

ian

du

rin

gth

ed

isas

ter

qu

arte

rt

=0

and

zero

oth

erw

ise.Time t

isa

tim

ed

um

my

vari

able

that

equ

als

one

for

even

t-quar

ter

t,w

her

et

isan

inte

gral∈

[−1,

+2]

.I i,t

isa

vect

or

ofin

sure

r-le

vel

contr

ol

vari

ab

les,

incl

ud

ing

logg

edca

pit

alan

dsu

rplu

s,lo

gged

RB

Cra

tio,

chan

gein

real

ized

clai

ms,

and

tax

shie

ld.M

tis

ave

ctor

of

qu

art

erly

chan

ges

inm

ark

etco

ndit

ion

s,in

clu

din

gP

asto

ran

dS

tam

bau

gh(2

003)

mea

sure

ofag

greg

ate

liqu

idit

y,C

RS

Pva

lue-

wei

ghte

dst

ock

mar

ket

retu

rn,

an

dtr

easu

ryre

turn

.F

or

det

aile

dd

efinit

ion

sof

contr

olva

riab

les,

see

Ap

pen

dix

.T

he

t–st

atis

tics

are

corr

ecte

dfo

rcl

ust

erin

gof

the

ob

serv

ati

on

sat

insu

rer

level

and

are

rep

ort

edin

the

par

enth

eses

bel

owco

effici

ent

esti

mat

es.

Sig

nifi

can

ceat

10%

,5%

,an

d1%

are

mar

ked

by

*,**

,an

d***

resp

ecti

vel

y.

∆Cash

t

Cash

t−1

∆Treasury

t

Cash

t−1

∆Corporate

t

Cash

t−1

∆Municipal t

Cash

t−1

∆Stock

t

Cash

t−1

∆OtherAsset

t

Cash

t−1

Time −

1-0

.13

-0.0

30.

010.

050.

040.

05

(-0.8

2)

(-0.

76)

(0.1

3)(0

.60)

(1.5

3)(1

.02)

Time 0

-0.1

6**

*-0

.08*

**0.

05*

0.07

**0.

05*

0.06

*

(-3.

53)

(-3.

01)

(1.9

1)(2

.34)

(1.7

7)(1

.75)

Time +

1-0

.24*

*-0

.08*

**0.

06**

*0.

07**

0.09

*0.

04**

(-2.

19)

(-3.

33)

(2.8

3)(2

.21)

(1.9

5)(2

.13)

Time +

2-0

.21

-0.2

1***

0.04

0.10

*0.

070.

01

(-0.3

1)

(-2.

76)

(0.6

3)(1

.87)

(1.2

1)(0

.69)

Insu

rer

Contr

ols

YE

SY

ES

YE

SY

ES

YE

SY

ES

Mar

ket

Con

trols

YE

SY

ES

YE

SY

ES

YE

SY

ES

66

Tab

le11:

Robust

ness

2–

Pro

pensi

tySco

reM

atc

hed

Sub-s

am

ple

Th

ista

ble

per

form

sro

bu

stn

ess

analy

sis

usi

ng

pro

pen

sity

scor

em

atch

edsu

b-s

amp

lein

sure

rs.

Sp

ecifi

call

y,I

esti

mat

eth

efo

llow

ing

form

,

∆Holding i

,t=δ 0

+δ 1Time t

+δ 2

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+δ 3Time t∗

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+δ II i,t

+δ M

Mt+ε

Ire

portδ 3

inth

eta

ble

.P

rop

ensi

tysc

ore

sare

com

pute

das

foll

ows:

Ies

tim

ate

alo

gist

icre

gres

sion

by

assi

gnin

gth

ed

epen

den

tva

riab

lefo

ru

naff

ecte

din

sure

rsa

du

mm

yof

on

e,an

dall

aff

ecte

din

sure

rsze

ro.

Th

ein

dep

end

ent

vari

able

sof

the

logi

stic

regr

essi

onin

clu

de

real

ized

clai

ms,

logg

edva

lue

ofass

ets,

logg

edva

lue

ofsu

rplu

s,an

dlo

gged

RB

Cra

tio.

For

each

un

affec

ted

insu

rer,

Ifi

nd

au

niq

ue

pai

rfr

omaff

ecte

dsa

mp

leby

one-

to-o

ne

mat

chin

g.T

he

pro

pen

sity

score

mat

chin

gp

roce

du

regen

erat

es3,

488

un

iqu

ep

airs

.P

anel

Bre

por

tsdes

crip

tive

stat

isti

csof

the

mat

chin

gva

riab

les.

Pan

elA

rep

orts

the

resu

lts

of

diff

eren

ce-i

n-d

iffer

ence

sre

gres

sion

sth

atre

pea

tth

ean

alysi

sin

Tab

le4

usi

ng

the

mat

ched

sub

-sam

ple

insu

rers

.A

sin

dic

ated

by

the

titl

eof

each

colu

mn,

the

dep

end

ent

vari

able

sar

equ

arte

rly

mar

ket

valu

ech

ange

sin

cash

,tr

easu

ryb

ond

,co

rpor

ate

bon

d,

mu

nic

ipal

bon

d,

com

mon

stock

,an

dot

her

ass

eth

old

ings,

scal

edby

the

cash

bal

ance

atth

eb

egin

nin

gof

the

quar

ter.

Th

eex

pla

nat

ory

vari

able

,U

naff

ecte

dIn

sure

rIn

dic

ato

r,eq

ual

son

eif

insu

rer

ih

asex

pec

ted

clai

mch

ange

sth

atar

elo

wer

than

med

ian

du

rin

gth

ed

isas

ter

qu

arte

rt

=0

and

zero

oth

erw

ise.Time t

isa

tim

ed

um

my

vari

able

that

equ

als

on

efo

rev

ent-

qu

arte

rt,

wh

eret

isan

inte

gral∈

[−1,+

2].I i,t

isa

vect

orof

insu

rer-

leve

lco

ntr

olva

riab

les,

incl

ud

ing

logg

edca

pit

al

an

dsu

rplu

s,lo

gged

RB

Cra

tio,

chan

gein

real

ized

clai

ms,

and

tax

shie

ld.M

tis

ave

ctor

ofqu

arte

rly

chan

ges

inm

arke

tco

nd

itio

ns,

incl

ud

ing

Past

or

and

Sta

mbau

gh(2

003)

mea

sure

ofag

greg

ate

liqu

idit

y,C

RS

Pva

lue-

wei

ghte

dst

ock

mar

ket

retu

rn,

and

trea

sury

retu

rn.

For

det

ail

edd

efin

itio

ns

ofco

ntr

olva

riab

les,

see

Ap

pen

dix

.T

he

t–st

atis

tics

are

corr

ecte

dfo

rcl

ust

erin

gof

the

obse

rvat

ions

atin

sure

rle

vel

and

are

rep

ort

edin

the

pare

nth

eses

bel

owco

effici

ent

esti

mat

es.

Sig

nifi

cance

at10

%,

5%,

and

1%ar

em

arke

dby

*,**

,an

d**

*re

spec

tive

ly.

Pan

el

A:

Resu

lts

usi

ng

Matc

hed

Su

b-s

am

ple

∆Cash

t

Cash

t−1

∆Treasury

t

Cash

t−1

∆Corporate

t

Cash

t−1

∆Municipal t

Cash

t−1

∆Stock

t

Cash

t−1

∆OtherAsset

t

Cash

t−1

Time −

10.1

10.

030.

070.

130.

080.

03

(1.2

7)

(0.2

4)(0

.05)

(0.2

3)(0

.90)

(0.8

6)

Time 0

-0.0

7**

-0.3

7**

0.52

**0.

47**

*0.

20**

0.29

(2.2

3)

(-2.

23)

(2.0

5)(2

.84)

(2.1

5)(3

.16)

Time +

1-0

.47*

*-0

.48*

*0.

84**

0.36

**0.

04**

*0.

10**

(-1.

99)

(-1.

98)

(2.0

4)2.

36(2

.66)

(2.4

8)

Time +

2-0

.36*

-0.3

9*0.

54*

0.38

*0.

01**

0.03

*

(-1.7

5)

(-1.

73)

(1.7

2)(1

.85)

(2.3

4)(0

.85)

Insu

rer

Contr

ols

YE

SY

ES

YE

SY

ES

YE

SY

ES

Mar

ket

Con

trols

YE

SY

ES

YE

SY

ES

YE

SY

ES

67

Pan

el

B:

Desc

rpti

ve

Sta

tist

ics

of

Matc

hin

gV

ari

ab

les

25th

Per

centi

leM

edia

n75

thP

erce

nti

lep-v

alu

eof

Tes

tof

Equ

alit

yof

Med

ian

s

p-v

alu

eof

Tes

tof

Equ

alit

yof

Dis

trib

uti

ons

Un

aff

ecte

dA

ffec

ted

Un

affec

ted

Aff

ecte

dU

naff

ecte

dA

ffec

ted

Insu

rers

Insu

rers

Insu

rers

Insu

rers

Insu

rers

Insu

rers

Matc

hin

gV

ari

abl

es

∆R

eali

zed

Cla

im0.2

30.2

40.

360.

370.

600.

590.

310.

11

Log(

Ass

et)

16.

9816

.89

18.2

318

.27

19.7

219

.65

0.54

0.22

Log(

Su

rplu

s)16.

0915

.98

17.2

617

.30

18.7

018

.70

0.44

0.14

Log(

RB

C)

1.2

91.

301.

571.

571.

791.

790.

890.

47

68

Table

12:

Robust

ness

3–

Ex-p

ost

Dis

ast

er

Exp

osu

reof

Insu

rers

Th

ista

ble

rep

orts

the

resu

lts

ofd

iffer

ence

-in

-diff

eren

ces

regr

essi

ons

that

rep

eat

the

anal

ysi

sin

Tab

le4

usi

ng

ex-p

ost

dis

aste

rex

pos

ure

asan

alte

rnat

ive

mea

sure

ofex

pec

ted

claim

s.S

pec

ifica

lly,

Ies

tim

ate

the

foll

owin

gfo

rm,

∆Holding i

,t=δ 0

+δ 1Time t

+δ 2

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+δ 3Time t∗

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+δ II i,t

+δ M

Mt+ε

Ire

por

tδ 3

inth

eta

ble

.T

he

sam

ple

only

incl

ud

esin

sure

rsth

ath

adp

osit

ive

dir

ect

pre

miu

mw

ritt

enfo

rth

ed

isas

ter

stat

eson

equ

arte

rb

efor

eth

ed

isast

er.

Dis

aste

rst

ates

are

hig

hligh

ted

inre

din

Fig

ure

2.I

esti

mat

eex

-pos

td

isas

ter

exp

osu

reat

insu

rer-

leve

las

foll

ows.

Fir

st,

Ies

tim

ate

the

insu

rer-

stat

e-le

vel

dis

ast

erex

posu

reas

the

stat

e-le

vel

insu

ran

cem

arke

tsh

are

ofth

ein

sure

r.In

sure

r-le

vel

dis

aste

rex

pos

ure

then

aggr

egat

esin

sure

r-st

ate-

leve

ld

isast

erex

posu

reov

erall

the

state

sin

wh

ich

the

insu

rer

oper

ates

.A

sin

dic

ated

by

the

titl

eof

each

colu

mn

,th

ed

epen

den

tva

riab

les

are

qu

arte

rly

mar

ket

valu

ech

an

ges

inca

sh,

trea

sury

bon

d,

corp

orat

eb

ond

,m

un

icip

alb

ond

,co

mm

onst

ock

,an

dot

her

asse

th

old

ings

,sc

aled

by

the

cash

bal

ance

atth

eb

egin

nin

gof

the

qu

art

er.

Th

eex

pla

nat

ory

vari

able

,U

naff

ecte

dIn

sure

rIn

dic

ato

r,eq

ual

son

eif

insu

reri

has

dis

aste

rex

pos

ure

that

islo

wer

than

med

ian

du

rin

gth

ed

isast

erqu

arte

rt

=0

and

zero

other

wis

e.Time t

isa

tim

edu

mm

yva

riab

leth

ateq

ual

son

efo

rev

ent-

qu

arte

rt,

wh

ere

tis

an

inte

gral∈

[−1,

+2]

.I i,t

isa

vect

orof

insu

rer-

leve

lco

ntr

olva

riab

les,

incl

ud

ing

logg

edca

pit

alan

dsu

rplu

s,lo

gged

RB

Cra

tio,

chan

gein

real

ized

clai

ms,

an

dta

xsh

ield

.M

tis

ave

ctor

ofqu

arte

rly

chan

ges

inm

arket

con

dit

ion

s,in

clu

din

gP

asto

ran

dS

tam

bau

gh(2

003)

mea

sure

ofag

greg

ate

liqu

idit

y,C

RS

Pva

lue-

wei

ghte

dst

ock

mark

etre

turn

,an

dtr

easu

ryre

turn

.F

ord

etai

led

defi

nit

ion

sof

contr

olva

riab

les,

see

Ap

pen

dix

.T

he

t–st

atis

tics

are

corr

ecte

dfo

rcl

ust

erin

gof

the

ob

serv

ati

on

sat

insu

rer

leve

lan

dar

ere

por

ted

inth

ep

aren

thes

esb

elow

coeffi

cien

tes

tim

ates

.S

ign

ifica

nce

at10

%,

5%,

and

1%

are

mar

ked

by

*,**,

an

d***

resp

ecti

vel

y.

∆Cash

t

Cash

t−1

∆Treasury

t

Cash

t−1

∆Corporate

t

Cash

t−1

∆Municipal t

Cash

t−1

∆Stock

t

Cash

t−1

∆OtherAsset

t

Cash

t−1

Time −

10.0

2-0

.09

-0.2

1-0

.09

-0.0

6-0

.09

(0.0

8)

(-0.

80)

(-0.

73)

(-1.

11)

(-0.

95)

(-0.

80)

Time 0

-0.1

8**

*-0

.23*

*0.

56**

*0.

21*

0.02

0.05

*

(-2.

64)

(-2.

02)

(3.0

9)(1

.72)

(0.2

6)(0

.63)

Time +

1-0

.42*

*-0

.19*

0.20

**0.

36*

0.10

**0.

09**

(-2.

47)

(-1.

79)

(2.0

1)(1

.70)

(2.0

6)(2

.01)

Time +

2-0

.15*

*-0

.07

0.11

*0.

11*

0.40

*0.

23*

(-2.

03)

(-0.

43)

(1.7

3)(1

.15)

(1.6

9)(1

.94)

Insu

rer

Contr

ols

YE

SY

ES

YE

SY

ES

YE

SY

ES

Mar

ket

Con

trols

YE

SY

ES

YE

SY

ES

YE

SY

ES

69

Tab

le13:

Rob

ust

ness

4–

Case

Stu

die

s(P

ort

foli

oA

lloca

tions

of

Insu

rers

)

Th

ista

ble

rep

orts

the

resu

lts

of

diff

eren

ce-i

n-d

iffer

ence

sre

gres

sion

sth

atre

pea

tth

ean

alysi

sin

Tab

le4

usi

ng

diff

eren

tca

ses.

Sp

ecifi

call

y,fo

rea

chca

se,

Ies

tim

ate

the

follow

ing

form

,

∆Holding i

,t=δ 0

+δ 1Time t

+δ 2

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+δ 3Time t∗

Un

aff

ecte

dIn

sure

rIn

dic

ato

r i,0

+δ II i,t

+δ M

Mt+ε

Ire

portδ 3

inth

eta

ble

.P

anel

Aan

dB

exam

ine

earl

yd

isas

ter

seas

ons

that

star

tin

qu

arte

rtw

oof

aye

ar.

Pan

elA

focu

ses

onco

asta

ld

isas

ter

stat

esin

thes

eea

rly

dis

ast

erse

aso

ns

(Qu

arte

r2,

200

1;

Qu

arte

r2,

2003

),an

dP

anel

Bfo

cuse

son

inla

nd

dis

aste

rst

ates

inth

ese

earl

yd

isas

ter

seas

ons

(Qu

arte

r2,

200

2;

Qu

art

er2,

2006;

Qu

art

er2,

2008)

.S

eeF

igu

re2

for

geog

rap

hy

ofd

isas

ters

.P

anel

Cex

amin

esth

ew

ild

fire

inC

alif

orn

iain

qu

arte

r4

of2003

.A

sin

dic

ated

by

the

titl

eof

each

colu

mn

,th

ed

epen

den

tva

riab

les

are

qu

arte

rly

mar

ket

valu

ech

ange

sin

cash

,tr

easu

ryb

ond

,co

rpor

ate

bon

d,

mu

nic

ipal

bon

d,

com

mon

stock

,an

dot

her

asse

th

old

ings

,sc

aled

by

the

cash

bal

ance

atth

eb

egin

nin

gof

the

qu

arte

r.T

he

exp

lan

ator

yva

riab

le,

Un

aff

ecte

dIn

sure

rIn

dic

ato

r,eq

ual

son

eif

insu

rer

ih

asex

pec

ted

clai

mch

ange

sth

atar

elo

wer

than

med

ian

du

rin

gth

ed

isas

ter

qu

arte

rt

=0

and

0oth

erw

ise.Time t

isa

tim

ed

um

my

vari

able

that

equ

als

one

for

even

t-qu

arte

rt,

wh

eret

isan

inte

gral∈

[−1,

+2]

.I i,t

isa

vect

orof

insu

rer-

leve

lco

ntr

olva

riab

les,

incl

ud

ing

logge

dca

pit

alan

dsu

rplu

s,lo

gged

RB

Cra

tio,

chan

gein

real

ized

clai

ms,

and

tax

shie

ld.M

tis

ave

ctor

ofqu

arte

rly

chan

ges

inm

ark

etco

nd

itio

ns,

incl

ud

ing

Past

or

and

Sta

mb

augh

(200

3)m

easu

reof

aggr

egat

eli

qu

idit

y,C

RS

Pva

lue-

wei

ghte

dst

ock

mar

ket

retu

rn,

and

trea

sury

retu

rn.

For

det

aile

dd

efin

itio

ns

of

contr

olva

riab

les,

see

Ap

pen

dix

.T

he

t–st

atis

tics

are

corr

ecte

dfo

rcl

ust

erin

gof

the

obse

rvat

ion

sat

insu

rer

leve

lan

dar

ere

por

ted

inth

ep

aren

thes

esb

elow

coeffi

cien

tes

tim

ates

.S

ign

ifica

nce

at10

%,

5%,

and

1%ar

em

arked

by

*,**

,an

d**

*re

spec

tive

ly.

Case

1:

Earl

yD

isast

er

Seaso

ns

(Coast

al

Dis

ast

er

Sta

tes)

∆Cash

t

Cash

t−1

∆Treasury

t

Cash

t−1

∆Corporate

t

Cash

t−1

∆Municipal t

Cash

t−1

∆Stock

t

Cash

t−1

∆OtherAsset

t

Cash

t−1

Time −

1-0

.01

-0.0

60.

060.

010.

050.

04

(-0.0

1)

(-0.

82)

(1.2

1)(0

.13)

(1.3

4)(0

.69)

Time 0

-0.6

7**

-0.3

4**

0.10

0.04

**0.

020.

02

(-2.

02)

(-2.

27)

(1.0

5)(2

.33)

(0.4

4)(0

.19)

Time +

1-0

.28*

-0.2

20.

09**

0.12

**0.

030.

16**

*

(-1.7

3)

(-1.

21)

(2.1

00(2

.50)

(0.4

0)(2

.95)

Time +

2-0

.37*

-0.0

80.

050.

04**

*0.

020.

03*

(-1.7

4)

(-0.

90)

(0.5

9)(2

.70)

(0.2

9)(0

.38)

Insu

rer

Contr

ols

YE

SY

ES

YE

SY

ES

YE

SY

ES

Mar

ket

Con

trols

YE

SY

ES

YE

SY

ES

YE

SY

ES

70

Case

2:

Earl

yD

isast

er

Seaso

ns

(In

lan

dD

isast

er

Sta

tes)

∆Cash

t

Cash

t−1

∆Treasury

t

Cash

t−1

∆Corporate

t

Cash

t−1

∆Municipal t

Cash

t−1

∆Stock

t

Cash

t−1

∆OtherAsset

t

Cash

t−1

Time −

1-0

.06

-0.0

50.

050.

010.

010.

04

(-1.2

2)

(-1.

54)

(1.1

7)(0

.18)

(1.0

5)(1

.61)

Time 0

-0.0

6-0

.09*

*0.

05**

*0.

03**

0.01

0.07

***

(-1.0

6)

(-2.

11)

(2.8

3)(2

.28)

(1.1

0)(2

.59)

Time +

1-0

.09*

-0.1

2***

0.03

*0.

01**

*0.

02*

0.05

*

(-1.7

0)

(3.4

2)(1

.67)

(3.2

8)(1

.67)

(1.7

6)

Time +

2-0

.13*

-0.0

6-0

.03

-0.0

10.

030.

03

(-1.7

5)

(1.3

3)(-

0.70

)(-

1.07

)(1

.02)

(0.7

0)

Insu

rer

Contr

ols

YE

SY

ES

YE

SY

ES

YE

SY

ES

Mar

ket

Con

trols

YE

SY

ES

YE

SY

ES

YE

SY

ES

Case

3:

Califo

rnia

Wild

Fir

e

∆Cash

t

Cash

t−1

∆Treasury

t

Cash

t−1

∆Corporate

t

Cash

t−1

∆Municipal t

Cash

t−1

∆Stock

t

Cash

t−1

∆OtherAsset

t

Cash

t−1

Time −

10.0

7-0

.09

0.06

0.01

0.03

0.07

(0.6

3)

(-0.

72)

(0.4

9)(1

.42)

(0.6

0)(0

.68)

Time 0

-0.4

6**

*-0

.10

0.18

*0.

18*

0.07

*0.

05

(-3.

10)

(-1.

49)

(1.7

1)(1

.71)

(1.6

7)(0

.47)

Time +

1-0

.17*

-0.2

1***

0.19

**0.

07*

0.05

*0.

05

(1.7

9)

(-3.

12)

(2.3

8)(1

.71)

(1.6

5)(1

.24)

Time +

2-0

.14*

-0.0

5-0

.05

0.01

0.01

0.01

(-1.7

1)

(-0.

89)

(-0.

71)

(0.5

8)(0

.17)

(0.0

4)

Insu

rer

Contr

ols

YE

SY

ES

YE

SY

ES

YE

SY

ES

Mar

ket

Con

trols

YE

SY

ES

YE

SY

ES

YE

SY

ES

71

Table

14:

Robust

ness

4–

Case

Stu

die

s(C

orp

ora

teB

ond

Perf

orm

ance

)

Th

ista

ble

rep

orts

the

resu

lts

of

diff

eren

ce-i

n-d

iffer

ence

sre

gres

sion

sth

atre

pea

tth

ean

alysi

sin

Tab

le6

usi

ng

diff

eren

tca

ses.

Sp

ecifi

call

y,fo

rea

chca

se,

Iru

nth

efo

llow

ing

form

,

∆Spreadk jih

=β0

+β1∗

Un

aff

ecte

dIn

sure

rIn

dic

ato

r j,i,h

+β2B

j,i,h

+β3M

h+

Ψjih

Ire

por

tβ0

an

dβ1

inth

eta

ble

.P

anel

Aan

dB

exam

ine

earl

yd

isas

ter

seas

ons

that

star

tin

qu

arte

rtw

oof

aye

ar.

Pan

elA

focu

ses

onco

asta

ldis

aste

rst

ates

inth

ese

earl

yd

isast

erse

ason

s(Q

uar

ter

2,20

01;

Qu

arte

r2,

2003

),an

dP

anel

Bfo

cuse

son

inla

nd

dis

aste

rst

ates

inth

ese

earl

yd

isas

ter

seas

ons

(Qu

arte

r2,

2002;

Qu

art

er2,

200

6;

Qu

art

er2,

2008

).See

Fig

ure

2fo

rge

ogra

phy

ofd

isas

ters

.P

anel

Cex

amin

esth

ew

ild

fire

inC

alif

orn

iain

qu

arte

rfo

ur

ofyea

r20

03.

Th

ed

epen

den

tva

riab

les

are

even

t-qu

arte

rk

chan

gein

yie

ldsp

read

for

bon

dj

hel

dby

insu

reri

du

rin

gd

isas

terh

per

iod.

Th

eyie

ldsp

read

inth

ep

rim

ary

mark

etis

rep

orte

din

per

centa

gean

dis

esti

mat

edas

the

yie

ldd

iffer

ence

bet

wee

nth

eoff

erin

gyie

ldto

mat

uri

tyan

da

matc

hed

trea

sury

bon

d,

rep

ort

edby

Mer

gent

FIS

D.

Th

eyie

ldsp

read

inth

ese

con

dar

ym

arke

tis

esti

mat

edas

the

med

ian

yie

ldto

mat

uri

tyon

the

last

trad

ing

day

of

the

qu

art

erre

por

ted

by

TR

AC

Em

inu

sth

em

edia

nen

d-o

f-qu

arte

ryie

ldon

the

trea

sury

bon

dm

atch

edon

mat

uri

ty.

Wh

ena

spre

adis

mis

sin

g,

Ies

tim

ate

itu

sin

gth

eyie

ldcu

rve

imp

lied

by

oth

ersp

read

sre

por

ted

atth

esa

me

tim

e.In

bot

hp

anel

s,I

exam

ine

chan

ges

inyie

ldsp

read

sfr

om

qu

arte

rk

=-1

toqu

art

erk

=0

inco

lum

n(1

)an

d(2

),ch

ange

sin

yie

ldsp

read

sfr

omqu

arte

rk

=0

toqu

arte

rk

=+

1in

colu

mn

(3)

an

d(4

),an

dch

ange

sin

yie

ldsp

read

sfr

om

qu

arte

rk

=+

1to

k=

+2

inco

lum

n(5

)an

d(6

).U

naff

ecte

dIn

sure

rIn

dic

ato

r j,i,h

equ

als

one

ifin

sure

ri

that

hol

ds

bon

dj

has

chan

ges

inex

pec

ted

claim

sth

atar

elo

wer

than

med

ian

inth

ed

isas

ter

qu

arte

rk

=0

ofd

isas

ter

h.

Iteq

ual

sze

rofo

ral

laff

ecte

din

sure

rs.B

j,i,h

isa

vect

or

of

tim

e-va

ryin

gan

dti

me-

inva

rian

tch

arac

teri

stic

sof

bon

dj

hel

dby

insu

reri

for

dis

aste

rh

per

iod.M

his

ave

ctor

ofch

an

ges

inm

arke

tco

nd

itio

ns

du

rin

gd

isas

ter

hp

erio

d.

See

app

end

ixfo

rd

etai

led

defi

nit

ion

sof

contr

olva

riab

les.

Th

et–

stat

isti

csar

eco

rrec

ted

for

clu

ster

ing

ofth

eob

serv

atio

ns

at

issu

erle

vel

and

are

rep

orte

din

the

par

enth

eses

bel

owco

effici

ent

esti

mat

es.

Sig

nifi

can

ceat

10%

,5%

,an

d1%

are

mark

edby

*,**

,an

d***

resp

ecti

vely

.

Case

1:

Earl

yD

isast

er

Seaso

ns

(Coast

al

Dis

ast

er

Sta

tes)

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Un

affec

ted

Insu

rer

Ind

icat

or0.

130.

000.

03-0

.07

0.08

0.07

(1.3

6)

(0.0

0)(0

.52)

(-0.

79)

(1.0

4)(0

.79)

Inte

rcep

t-0

.22**

*-0

.01

-0.0

4-0

.06

-0.5

0***

0.06

(-4.5

3)

(-0.

05)

(-0.

33)

(-0.

25)

(-3.

15)

(0.2

5)

Bon

dC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

Mar

ket

Con

trols

NO

YE

SN

OY

ES

NO

YE

S

R2

0.0

10.

750.

010.

600.

020.

60

Clu

ster

s(i

ssu

ers)

507

1449

321

416

21

Ob

serv

ati

on

s13

,884

710

11,0

471,

139

7,58

71,

139

72

Case

2:

Earl

yD

isast

er

Seaso

ns

(In

lan

dD

isast

er

Sta

tes)

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Un

affec

ted

Insu

rer

Ind

icat

or-0

.02

0.02

-0.1

7***

-0.0

8***

0.43

***

0.45

***

(-0.6

9)

(0.7

3)(3

.85)

(-2.

60)

(6.1

7)(6

.78)

Inte

rcep

t0.2

2**

*0.

22**

*-0

.05

-0.1

7**

0.28

***

0.51

***

(2.6

8)

(2.7

4)(-

0.55

)(-

2.07

)(2

.62)

(4.4

1)

Bon

dC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

Mar

ket

Con

trols

NO

YE

SN

OY

ES

NO

YE

S

R2

0.0

20.

310.

010.

340.

060.

50

Clu

ster

s(i

ssu

ers)

703

289

671

283

601

249

Ob

serv

ati

on

s20

,303

10,5

4715

,586

8,92

713

,447

7,97

4

Case

3:

Califo

rnia

Wild

Fir

e

∆Spread0

∆Spread1

∆Spread2

(1)

(2)

(3)

(4)

(5)

(6)

Un

affec

ted

Insu

rer

Ind

icat

or0.

090.

010.

110.

01-0

.01

0.01

(0.9

0)

(0.2

0)(1

.43)

(0.4

8)(-

0.25

)(0

.30)

Inte

rcep

t0.

010.

060.

77**

*-0

.11

0.54

***

0.08

(0.0

4)

(0.7

1)(-

5.41

)(-

1.58

)(2

.97)

(1.4

5)

Bon

dC

ontr

ols

NO

YE

SN

OY

ES

NO

YE

S

Mar

ket

Con

trols

NO

YE

SN

OY

ES

NO

YE

S

R2

0.0

10.

550.

010.

510.

050.

55

Clu

ster

s(i

ssu

ers)

431

8136

272

260

64

Ob

serv

ati

on

s6,

495

2,77

16,

145

2,88

54,

431

2,48

1

73