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Wei Xiong, Princeton University
5th Annual CARISMA Conference
London, February 3, 2010
Post-Crisis Perspectives: Bubbles, Financial Institutions, and Asset
Markets
Wei Xiong 2
Important Lessons in Three Fronts
1. Understanding asset bubbles is vital for understanding the recent crisis.
– Intricate interaction between the housing bubble and credit market.
2. Financial institutions are vulnerable to bubbles.– Various forms of externalities when the housing bubble went
bust.
3. Financial institutions have profound impacts on asset markets and asset prices.
– Unprecedented volatility and illiquidity across asset markets when financial institutions ran into crisis.
Wei Xiong 3
Part I: Bubbles as the Seed of the Crisis
• The crisis followed two great bubbles: the Internet bubble and the housing bubble.– The burst of the Internet bubble led to an expansionary US monetary
policy for a prolonged period.– The low interest rate together with financial innovations such as
securitization of subprime mortgages had fueled housing speculation and led to a great housing bubble.
– The burst of the housing bubble eroded the balance sheets of many financial institutions, and eventually dragged down the world economy.
Wei Xiong 4
The Housing Bubble
Mar-75 Jun-77 Sep-79 Dec-81 Mar-84 Jun-86 Sep-88 Dec-90 Mar-93 Jun-95 Sep-97 Dec-99 Mar-02 Jun-04 Sep-06 Dec-080
50
100
150
200
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Real Home Price Indices
US
Arizona
California
Nevada
Florida
Wei Xiong 5
What Do We Know About Bubbles?
• It is common to see price bubbles in new technologies, such as railroad, radio, biotech, Internet, securitization, …
• New concepts stimulate dispersion of opinions and therefore speculation between optimists and pessimists.
• Miller (1977)– When investors have heterogeneous beliefs about asset fundamentals
and are constrained from short-selling, optimists determine price and drive overvaluation.
– Supported by evidence that measures of belief dispersion tend to predict lower stock returns, e.g., Diether, Malloy, and Scherbina (2002), and Chen, Hong and Stein (2002).
• Harrison and Kreps (1978) – In a dynamic setting with fluctuating beliefs, asset prices could be higher
than the current optimists’ valuation, because of the option to resell to even more optimistic buyers in the future.
– The value of the resale option represents a speculative component.
Wei Xiong 6
Overconfidence and Speculative Bubbles (Scheinkman and Xiong, 2003)
• Overconfidence (or inefficiencies in learning) provides a reasonable way to parameterize heterogeneous beliefs.– Belief dispersion persists and fluctuates with information flow.
• Volatility of investors’ disagreement drives both asset trading and bubble component in price, thus correlating the two.– Capturing the widely observed phenomenon that historical bubbles
were all associated with trading frenzies, e.g., Ofek and Richardson (2003), Lamont and Thaler (2003), and Cochrane (2003).
– Consistent with the large number of investment-home purchases in 2004-2006 in FL, NV, and AZ.
• Transaction cost has a second-order effect on price bubbles.– Bubbles could emerge in housing markets despite high trading cost.– Tobin’s tax may not work as intended in curbing bubbles.
Wei Xiong 7
Credit Booms and the Housing Bubble
• Credit expansion, e.g., securitization of innovative subprime mortgages, played an important role in fueling the housing bubble.– Mian and Sufi (2009)– Consistent with the view of Kindleberger (1978).
• A wave of effort to incorporate effects of credit in asset prices – Geanakoplos (2009) and Brunnermeier and Pedersen (2009)
• When optimists need to borrow from pessimists, belief divergence can cause credit to tighten and therefore asset prices to fall. – Simsek (2009):
• How did optimistic housing speculators manage to get credit from (not so optimistic) creditors during the housing bubble?
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Wei Xiong 8
Financing Bubbles (He and Xiong, 2009)
• Consider an asset (house) with a two-period fundamental tree:– Optimists believe the up probability is π;– Pessimists believe the probability is ρ< π.
• Long-term collateralized debt allows optimists totrade promises across the three final states for aninitial loan from pessimists.
• Short-term debt, via contingent refinancing, makes it possible to trade promises across all four possible paths.
– The increased contract flexibility allows optimists to take a higher leverage
• Higher belief dispersion leads to a higher leverage and a shorter debt maturity by optimists, together with a higher asset price.
• Examples:– Short-term hybrid mortgages used by over 75% of subprime borrowers in 2003-2007.– Repos and CPs used by financial institutions.
u2f
uf
f udf
df
d2f
Wei Xiong 9
US Primary Dealer Mean Leverage
• Source: Adrian and Shin (2009)
Wei Xiong 10
Repos, Financial CP, and M2O vernight repos , F inanc ia l C P and M 2
(weekly, J u ly 6 1994 as base da te )
A pr 29 2009
M ar 19 2008
A ug 8 2007
2 .37
0 .0
1 .0
2 .0
3 .0
4 .0
5 .0
6 .0
7 .0
8 .0
Jul 6 1994
Jan 18 1995
Aug 2 1995
Feb 14 1996
Aug 28 1996
Mar 12 1997
Sep 24 1997
Apr 8 1998
Oct 21 1998
May 5 1999
Nov 17 1999
Jun 7 2000
Dec 20 2000
Jul 4 2001
Jan 16 2002
Jul 31 2002
Feb 12 2003
Aug 27 2003
Mar 10 2004
Sep 22 2004
Apr 6 2005
Oct 19 2005
May 3 2006
Nov 15 2006
May 30 2007
Dec 12 2007
Jun 25 2008
Jan 7 2009
O vernight repo
F inanc ia l C P
M 2
Wei Xiong 11
How to Detect the Next Bubble?
• Central banks and regulators have increasingly recognized the danger of asset bubbles to financial markets.
• But thirty-years of academic research only finds weak evidence of predictability in financial prices, i.e., it is hard to predict bubbles!• Even ex post identification of bubbles is often controversial because of the
difficulty in measuring asset fundamentals. • Debates on each historical bubble.
• It is useful to extract information from dimensions beyond prices.• Trading frenzy; • Heavy participation of inexperienced investors;• Large leverage taken by both investors and institutions; • Short-term credit booms;• And, more …
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Wei Xiong 12
The Chinese Warrants Bubble (Xiong and Yu, 2008)
• China introduced a new warrants market in 2005-2008. • The life of Wuliang put warrant (strike price 8 Yuan):
0
10
20
30
40
50
60
70
80
0
1
2
3
4
5
6
7
8
9
Ap
r-06
Jun-0
6
Au
g-06
Oc
t-06
De
c-06
Fe
b-07
Ap
r-07
Jun-0
7
Au
g-07
Oc
t-07
De
c-07
Fe
b-08
Sto
ck p
rice
(Y
ua
n)
Pu
t p
rice
(Y
ua
n)
Put price Stock price
Wei Xiong 13
The Chinese Warrants Bubble (Xiong and Yu, 2008)
• Identifying the fundamental of Wuliang put warrant:
0
1
2
3
4
5
6
7
8
9
Ap
r-06
May
-06
Jun-
06
Jul-0
6
Au
g-06
Sep-
06
Oct-
06
No
v-06
De
c-06
Jan-
07
Feb-
07
Mar
-07
Ap
r-07
May
-07
Jun-
07
Jul-0
7
Au
g-07
Sep-
07
Oct-
07
No
v-07
De
c-07
Jan-
08
Feb-
08
Mar
-08
Pri
ce (Y
uan
)
Put price Strike price Fundamental upper bound Black-Scholes price
Wei Xiong 14
The Chinese Warrants Bubble (Xiong and Yu, 2008)
• In a sample of 16 put warrants, the price bubbles are associated with trading frenzies.
Wei Xiong 15
The Last Day of WanHua Put Warrant
Wei Xiong 16
Lessons from the Chinese Warrants Bubble
• The non-zero price traded at the very last minute of each put warrant indicates the existence of naïve investors in the market.
• The clear downward trend in prices indicates smart investors who recognize that as maturity approaches, there is less time to resell their warrants.
• The interaction between the naïve and smart investors ultimately drive the spectacular warrants bubble.
Wei Xiong 17
Speculative Behavior in the US Housing Bubble
20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 200.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1Share of Investment Property
National
Arizona
California
Florida
Nevada
Wei Xiong 18
Speculative Behavior in the US Housing Bubble
200501
200503
200505
200507
200509
200511
200601
200603
200605
200607
200609
200611
200701
200703
200705
200707
200709
200711
200801
200803
200805
200807
200809
200811
200901
200903
200905
2009070
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
Share of Second Homes
NationalArizonaCaliforniaFloridaNevada
Wei Xiong 19
Speculative Behavior in the US Housing Bubble
200501
200503
200505
200507
200509
200511
200601
200603
200605
200607
200609
200611
200701
200703
200705
200707
200709
200711
200801
200803
200805
200807
200809
200811
200901
200903
200905
2009070
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Foreclosure Rate of Investment Property
USAZCAFLNV
Wei Xiong 20
Speculative Behavior in the US Housing Bubble
200501
200503
200505
200507
200509
200511
200601
200603
200605
200607
200609
200611
200701
200703
200705
200707
200709
200711
200801
200803
200805
200807
200809
200811
200901
200903
200905
2009070
0.02
0.04
0.06
0.08
0.1
0.12
Foreclosure Rate of Second Homes
USAZCAFLNV
Wei Xiong 21
Part II: The Fragility of Financial Institutions
• The shadow banking system, e.g., investment banks, SIVs, hedge funds, grew rapidly in the last decade. – Financial innovations, such as repos and other short-term debt
contracts, allowed many optimistic institutions to obtain large amount of credit from the rest of the economy.
• The burst of the housing bubble eroded the balance sheets of these institutions.– In particular, their asset-liability maturity mismatches expose
them to runs and other externalities during the down turns.– Insights from corporate finance are very useful in understanding
financial institutions and asset markets.
Wei Xiong 22
Runs on Asset-Backed Commercial Paper
• Source: Federal Reserve Release
Wei Xiong 23
Dynamic Debt Runs (He and Xiong, 2009)
• A model of runs that integrates fundamental concerns and panics.• A firm invests in a long-term illiquid asset by using staggered short-term
debt.– Each creditor is locked in during his contract period, during which others’ contracts
could mature.– Debt run externality.
• Fear of a firm’s future rollover risk could lead to preemptive runs.– Trigger of rollover risk: deterioration of firm fundamental.– Coordination problem between creditors amplifies this concern.
• A unique debt run equilibrium– Each maturing creditor chooses to run at a high fundamental threshold.– The threshold depends on fundamental volatility, market liquidity, and debt
maturity.
• A tractable framework for dynamic coordination problems.– Features time-varying fundamental and a unique equilibrium.– No need for asymmetric information.
Wei Xiong 24
Debt Overhang on Financial Institutions
• When firms face difficulties in rolling over maturing debt, why don’t they issue equity instead?– Debt overhang and bankruptcy externality.
• He and Xiong (2009) on ``Rollover Risk and Credit Risk”– A firm can issue equity at market price to pay off rollover losses at any time.– The firm defaults when its equity price, which is endogenously determined by
firm fundamental and future rollover losses, drops to zero, a la Leland (1994).
• Deteriorating market liquidity exacerbates the conflict between debt and equity holders, causing earlier endogenous default by equity holders. – It not only increases liquidity premium, but also default premium.
• Short-term debt further amplifies the conflict.– A tradeoff in using short-term debt: cheaper financing cost and higher future
bankruptcy cost.
Wei Xiong 25
Other Externalities in the Financial System
• Liquidation externality– Morris and Shin (2004), Bernardo and Welch (2004), Brunnermeier
and Pedersen (2009)
• Leverage externality– Lorenzoni (2008)
• Network externality– Zawadowski (2009)
• The existence of these different forms of externalities in the financial system prompts macro-prudent risk management rather than over-reliance on micro-prudent risk management.– Brunnermeier, Crockett, Goodhart, and Shin (2009)– Acharya and Richardson (2009)
Wei Xiong 26
Part III: Impact of Financial Institutions on Asset Markets
• The standard finance theories attribute asset prices to rational expectations of assets’ future cash flow and systematic risk.
• The crisis again demonstrates the great influence of financial institutions on asset price dynamics.• Confirming the view advocated by Allen and Gale (2007) and
others.
Wei Xiong 27
The Boom and Bust of Commodities in 2008
Wei Xiong 28
Index Investing and the Financialization of Commodities (Tang and Xiong, 2009)
• Two popular views about the boom and bust of commodities:– A matter of supply & demand, e.g., Krugman (2008) and Hamilton (2008).– Financial speculation has distorted commodity prices, e.g., CFTC (2008) and
Masters (2008). • The debate ignores a fundamental financialization process of
commodities.– Prior to early 2000s, commodities were segmented from the broader
financial markets and from each other.– The rapid growth of index investment in commodities made commodities
behave much more like stocks, i.e., they are increasingly exposed to market-wide shocks and shocks to other commodities.
– As a result, the spillover effects of the recent financial crisis have contributed substantially to the large increase in commodity price volatility in 2008.
Wei Xiong 29
Return Correlation: Soybean-Oil
Wei Xiong 30
Return Correlation: Cotton-Oil
Wei Xiong 31
Return Correlation: Live Cattle-Oil
Wei Xiong 32
Return Correlation: Copper-Oil
Wei Xiong 33
Return Correlation between Soybean Complex and Oil
Wei Xiong 34
The Difference-in-Difference Analysis
• Significant differences in the exposures to shocks to stock markets, US dollar, and oil between individual non-energy commodities in the Goldman Sachs Commodity Index and Dow Jones Commodity Index and those off the indices.
Wei Xiong 35
Volatility of Commodities
Wei Xiong 36
Decomposing Oil VolatilityIM
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Wei Xiong 37
Decomposing Non-energy Commodity Volatility
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Wei Xiong 38
Many Important Questions Remaining
• Understanding bubbles:– Relationship between bubbles and credit markets
– How to predict bubbles?
• Understanding financial institutions– Externalities
– Compensation and incentives
– Macro-prudent risk management
• Impact of financial frictions on asset prices and the economy. – Volatility and risk premium
– Firm investment and economic growth