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Title:

Leverage, Risk-Based Capital Regulation and SRISK across Bank Ownership

Types and Financial Crisis: Panel VAR Approach

Sameh Jouida ISG, Sousse University, Tunisia

samehjouida@gmail.com

Abstract: (Your abstract must use 10pt Arial font and must not be longer than this box)

This paper analyzes the simultaneous and dynamic multi-directional interrelationships

between Leverage, Risk-Based Capital (RBC) Regulation and SRISK across the bank

ownership type —foreign and domestic banks— and the financial crisis. To overcome

econometric problems (endogeneity and causality), we build a Panel Vector Auto-

Regression (PVAR). A negative bidirectional relationship between SRISK and RBC has

been found for domestic banks. Forecast Error Variance Decompositions (FEVD)

chooses leverage as the most endogenous variable. Impulse-Response Functions (IRF)

separates RBC from SRISK that affects leverage in banking sector. In a crisis period,

we find that response of leverage and RBC to SRISK shock is negative.

Keywords: Panel VAR; Leverage Ratio; Risk-Based Capital Regulation: RBC Ratio;

SRISK; Bank Ownership Type; Financial Crisis.

JEL classification: G21, G32, G38, L51, N23

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Introduction

The financial crisis has a strong impact on the financial system, especially the banks, which may also

affect financial stability. This crisis generates losses and even bankruptcy of large banks, which requires

government intervention to stabilize the financial system. The failure of highly levered banks during the

financial crisis period has caused a renewed attention in bank capital structure. The financial crisis has

affected the capital structure1 in the banking industry more than in the unregulated industrial sector,

Schoenmaker (2015). Therefore, the banking sector is considered as vulnerable and can suffer from a

decrease in benefit and growth opportunity offered by intensification of internationalization. Besides, during

the recent financial crisis, foreign subsidiaries have reduced their lending earlier and faster than domestic

banks, which affects the capital structure (De Haas et al., 2011). The foreign equity entry may increase the

possibility of contagion and sensitivity to any financial crisis (Chen et al., 2009). Thus, the outflow of

foreign investors during the financial crisis has led to a drop in the share price of these banks and posed a

risk to the financial system.

Few studies consider the capital structure across ownership type. Sajid and Sizhong (2014) confirm a

negative impact of foreign presence on the leverage of domestic firms in China's manufacturing sector. The

authors show that the increase in foreign presence affects domestic firms that may shift to debt financing

because raising equity is too difficult. Buckley et al. (2007) find that the foreign firm’s presence affects the

domestic firm’s profitability and increased competition and growth opportunities. These factors are of a great

importance in determining the firm’s capital structure (Margaritis and Psillaki, 2010). Marques and Santos

(2003) confirm a little consensus in previous studies on the bank’s capital structure. To our knowledge,

existing studies on bank capital structure have not directly addressed the potentially role of bank’s ownership

type—foreign and domestic Banks—. In addition, very few empirical studies have been applied to this

relationship before and during the financial crisis. Our paper, therefore, uses the results drawn from the

corporate literature to investigate this topic.

During the financial crisis, attention to the regulatory capital of financial2 institutions has been also

renewed due to the social cost of bank failures. Bank regulatory standards have changed several times in

recent decades, and most significantly in response to the last banking crisis, Barth and Miller (2017).

Stanhouse and Stock (2016) support the idea of regulating banks equity in the financial sector. Banks and

financial institutions are characterized by specific capital requirements and deposit agreements (Harding et

al., 2013 and Gropp and Heider, 2010). Graf (2010) proved that regulators restrict the capital structure of

banks. Although, banking is a regulated industry, banks are exposed to the same type of agency costs and

other influences on behavior as other industries. Most banks are well above the regulatory capital

minimums3, which might differ in bank ownership type. On that account, a possible bidirectional relationship

is between the regulation and capital structure of domestic and foreign banks.

1 Throughout this paper, capital structure, debt ratio and leverage have been used interchangeably. 2 In this paper, the Basel Risk-Based Capital Regulation is also recognized as a Capital requirement, regulatory capital or capital regulation. 3 Given as the capital amount of bank or other financial institution has to keep as required by its financial regulator.

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The current financial crisis shows that highly leveraged capital structure is a significant source of risk for

financial institutions and for society as a whole (Harding et al., 2012). Laeven and Levine, (2009) confirm

that during the financial crisis, the bank’s risk can have a first-order effect on financial and economic

stability. Thus, regulations are implemented to limit bank risk and avoid future financial crises, Berger

(2013). The authors show that the difference in risk-taking is more pronounced during the financial crisis

than normal times. Coleman et al. (2017) confirm that the individual risk of failure, which can be contained

without harming the entire system. However, the systemic risk is the risk of collapse of an entire financial

system or market. Acharya et al. (2012) corroborate that it is sensible to regulate ex-ante financial

institutions whose their failure is likely to have major impacts on the financial and real sectors of the

economy. Brownlees and Engle (2017) and Acharya et al. (2012) propose a systemic risk measure, SRISK,

which is defined as the expected capital shortfall of an institution during a financial crisis. The use of the

SRISK methodology for the French banking sector reveals that systemic risk has significantly increased at

the onset of the 2007-2009 financial crises. However, as far as we know, no previous studies considered the

direct relationship between foreign and domestic banks in terms of SRISK. In addition, such studies have

little to say about the dynamic interrelationship between SRISK, capital regulation and capital structure

before and during financial crises. This paper aims to fill these gaps in the literature.

Literature review reveals that domestic and foreign banks operating in the French market did not benefit

from empirical studies. The choice of the French market is motivated by several factors. Firstly, this market

is characterized by a large and a sophisticated financial system. Internationalization of French banking

provides access to growth opportunities. Therefore, the globalization of the French banking sector is related

to the importance of foreign banks' presence. Secondly, the French banks have shown no evidence of de-

leveraging from their pre-crisis levels, an interesting phenomenon which contradicts the conventional

perception that banks would be obliged or inclined to decrease leverage because of the crisis. Outstanding

loans granted by French banks increased by +94% between 2000 and 2013. Outstanding loans rose by a

further 1.7%, despite slower economic growth. Hence, the French banks are exposed to an international

fierce competition and are more vulnerable at the time of economic tensions (financial crisis, etc.). The

French banking sector seems to be a favorable context to study domestic and foreign banks.

Our sample includes 170 banks operating in the French market and 2029 yearly observations

covering 105 domestic banks and 65 foreign banks from 12 “Developed markets” and 6 “Emerging

markets”4 over the period 2000-2015. We contribute to this literature in the following ways. Firstly, we

account for the inherent simultaneity between SRISK, capital regulation and bank capital structure. In the

research context, we did not limit our study to the capital structure determinants but, we investigated the

dynamic bi-directional interrelationship. In this paper, we examine and compare the two sub-samples of

domestic and foreign banks. We add to the growing empirical literature on banks an examination of whether

these interrelationships between foreign banks are significantly different from the domestic ones. Secondly,

we assess these interrelationships before and during the financial crisis. We point out that few studies have

examined this topic, and to our knowledge, it is the first time that these interrelationships have been explored

4 Developed markets countries include banks from Belgium, Germany, Ireland, Italy, Luxembourg, Netherland, Portugal, United Kingdom, USA,

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in the banking sector. We also divide our sample into two sub-periods: the pre-crisis period (2000-2007) and

the crisis years (2008-2015) as Chronopoulos et al. (2015). Thirdly, the empirical studies have resorted to

micro-econometric techniques in testing hypotheses. The use of various capital structure determinants and

control variables in previous studies may also explain the variation in the findings. To overcome this

problem, we use Panel Vector Auto-Regression (PVAR) model. There are several reasons that explained this

choice. The PVAR is used to study simultaneous and multi-directional dynamic relationships by estimating

an equation system. This model treats the variables as endogenous and allows the efficient estimation of

parameters. The PVAR has combined the advantages of standard VAR adding a cross-sectional dimension

and a structural time variation. The PVAR is a much more powerful tool to address interesting questions,

Canova and Ciccarelli (2013). Thus, we apply this dynamic method to our large panel data. We build a

PVAR that runs on a Generalized Method of Moment (GMM) framework following Abrigo and Love

(2015). The dynamic analysis is based on the Forecast Error Variance Decomposition (FEVD) and

completed by the Impulse Response Functions (IRF). Finally, to provide a rich basis for our analysis, we

choose the financial industry because it has a higher level of diversification resulting from deregulation,

technological advancement and consolidation. Thus, in general, the debt choice can affect financial stability

in general. As discussed above, we investigate these issues due to their crucial roles in the financial industry.

They provide credit to firms and stability to the economy as a whole (Berger and DI Patti, 2006). Relying on

the agency theory, banks have an opaque informational nature and they hold private information on their

loan customers and other credit counterparts. Thus, our main purpose is to add to the burgeoning empirical

literature by studying the dynamic interrelationships in the financial sector.

Our empirical analysis reveals significant dynamic interrelationship between Bank Capital Structure,

Capital Requirements and SRISK over a whole period. All these findings are sensitive to ownership type as

well as to crisis period. There is a bi-directional relationship between leverage, Capital ratio and SRISK for

all banks over a full period. Our findings emphasize the importance of debts in a domestic bank financing.

French domestic banks are more financed by debt compared to foreign banks. There is a difference between

the leverage ratio between foreign (low negative value) and domestic (high positive value) banks, especially

in the post-financial crisis. We show that the bank leverage is sensitive to the systemic risk SRISK especially

in a financial crisis of the banking sector. That appears to be the case with proposals to toughen the leverage

ratio. There are bi-directional interrelationships between leverage and SRISK and between Capital ratio and

SRISK. However, we find negative interrelationships in the crisis period. There is bidirectional relationship

negative between SRISK and capital ratio for domestic banks. However, the reverse result is shown for

foreign banks in the crisis period. This finding may be explained by the fact that the regulatory framework

differs between the home and the host country for foreign banks, Barth et al. (2006). The majority of the

foreign banks is incentivized to reduce their international operations in the wake of the crisis. It may be

caused by the need to meet stiffer capital requirements and other regulatory changes aimed at strengthening

banking systems as confirmed by Claessens and Horen (2015). The stability condition is not confirmed for

foreign banks and in crisis periods, but it is proved for the two factors observed simultaneously (leverage and

South Korea, Spain and Switzerland. Emerging markets countries includes banks from Lebanon, Morocco, Poland, Qatar, Russia and Tunisia.

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SRISK). However, the forecast error variance decompositions (FEVD) validate the choice of the leverage as

the most endogenous variable. Impulse response functions (IRF) illustrate significant dynamic

interrelationships in the French financial sector. In a crisis period, we find that the response of leverage and

Capital ratio to SRISK shock is negative. Barth et al. (2006) also show that the international framework

affects the regulation imposed on foreign banks by the domestic banking sector. The SRISK is sensitive to

the capital ratio for the French commercial banks.

The remainder of the paper is organized as follows: In Section 2 we review the relevant financial

literature of capital structure across bank’s ownership type -domestic and foreign-. Section 3 describes data,

variables and methodologies used in the paper. The results are discussed in section 4. Finally, section 5

concludes the paper.

Literature review

In corporate finance, the topic of the capital structure remains controversial and little attention has

been paid especially to the capital structure across bank ownership type—foreign and domestic banks—.

Berger et al., (2000) reveal that the difference between domestic and foreign banks is due to culture,

regulation, language and other explicit and implicit barriers. The foreign bank presence enhances efficiency,

decreases income and costs of domestic banks, Claessens, et al. (2001).

Moreover, the previous empirical corporate literature considers the capital structure across

ownership type. Previous empirical studies on capital structure determinants are characterized by the absence

of the overall structural theoretical model. However, this leads to a large number of potential determinants,

the effects of debt can change from one theory to another (Trade-Off Theory, Pecking Order Theory ...).

Akhtar (2015) proves that the capital structure determinants are different depending on whether they are

multinational or domestic corporations. Compared to U.S. firms, Australian, Japanese, English and

Malaysian MCs hold significantly less long-term debt. DCs and MCs that operate under an imputation tax

system hold significantly less short- and long-term debt and DCs and MCs operating under common law

have significantly less short-term debt and significantly higher long-term debt. In addition to the identifying

the determinants of capital structure, other studies such as Chkir and Cosset (2001) examine capital

structures between US-based multinational corporations (MCs) and domestic corporations (DCs). They find

that US MCs have less debt than US DCs. They point out that capital market imperfections and international

operations complexity for MCs lead to lower debt levels. Singh and Nejadmalayeri (2004) find that multi-

nationality is positively associated with higher leverage for a French corporation’s sample. However, they do

not present any explanation for their findings. Akhtar and Oliver (2009) demonstrate that Japanese

multinationals have a significantly lower leverage than domestic ones. They indicate that multi-nationality is

an important aspect of leverage for Japanese firms. Thus, multinationals have better opportunities than

domestic peers to earn more benefits principally because of their access to more than one earnings source.

They have better chances for favorable business conditions in foreign countries.

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Several studies have considered the foreign bank’s presence has an important repercussion on a local

banking system. Sajid and Sizhong (2014) point out that the foreign presence decrease the leverage of

domestic ones. Bruno and Hauswald (2014) point out that foreign banks act as an indicator of financial and

economic expansions. They show that foreign lending reduces financial constraints and raise the growth of

the competitive reaction of local lenders and that foreign banks supply stable access to credit. Bush

and Golder, (2001) also add that foreign banks motivate the evolution of supervision, banking regulations

and allow access to the international capital market. Levine (1997) show that foreign banks improve the

quality and accessibility of financial services in the domestic financial market by the insertion of a new

technological development which increases competitiveness. However, the same authors emphasise that the

foreign bank’s presence may have negative implications in the domestic banking sector. The foreign banks

role remains controversial, Bruno and Hauswald (2014). Bush and Golder (2001) indicate that foreign banks

are the main failure cause for less competitive domestic banks. Levine, (1997) confirms that a local investor

has not the same financing access as foreign banks, which usually operate with multinational markets and

that government cannot supervise all funding of foreign banks.

Isaiah (2017) corroborates that the importance of having sufficient regulatory capital has brought

great attention since the 2008 financial crisis. Stanhouse and Stock (2016) result’s recorded a reduction in

optimal capital under non-binding capital requirements, while, in contrast, when equity is constrained, an

increase in the optimal capital. In the financial sector, the regulation has an important role in the way banks

organize their activities. Thus, the minimums for equity capital and other types of regulatory capital that has

been set by regulators affect the bank capital structure in order to limit excessive risk-taking (Berger and DI

Patti, 2006). McKee and Kagan (2017) confirm that the changes in regulation have affected the efficiency

with which these banks can perform their services within a rapidly changing banking environment. The bank

managers often hold less capital than it is required by regulation in order to avoid the high costs of holding

capital. An alternative view of the bank’s capital structure is proposed by Diamond and Rajan (2000). They

argue that banks, like firms, optimize their capital structure, relegating regulatory capital to a second order

importance. The market discipline theories also relegate capital requirement for a second order importance.

Flannery and Rangan (2008) suggest that banks’ capital structures are the outcome of pressures arising from

shareholders, debt holders and depositors and that capital requirements may be non-binding and of second

order importance. Gropp and Heider, (2010) conclude that capital regulation was a second order in

determining the capital structure of large U.S. and European banks during the period of 1991 -2004. The

bank-specific factors had an effect on the bank’s share of equity in excess of the regulatory minimum. The

regulation is not a first order determinant of bank’s capital structure as confirmed by Teixeira et al., (2014).

However, Harding et al. (2012) confirm that banks, being financial intermediaries, are different from other

firms. Significantly, banks have the unique benefit of being able to issue federally insured debt; but they also

bear the cost of capital regulations, including the threat of being placed in receivership which would likely

wipe out the investment of the shareholders. Banks also manage financial, rather than physical, assets

implying lower bankruptcy costs than industrial firms, Harding et al. (2012).

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Additionally, many risks are jointly managed in the banking sector. Although there are several advantages

in entering foreign markets, the continued foreign expansion has increased risks, Kemper et al. (2012).

Akhtar and Oliver (2009) find that foreign exchange risks are not significant for modeling the capital

structure of multinationals but are significant for domestic firms. Business risks are negatively related to

leverage for multinationals. The authors argue that it has significant positive leverage effects of foreign

exchange risks. Akhtar and Oliver (2009) show that the foreign exchange risks of multinationals can be

managed through derivatives and other risk management operations but they do not reduce leverage. The

banking environment is characterized by uncertainty and a multitude of risks. Barth and Miller (2017)

confirm that the regulatory capital focuses especially, on systemic risk to promote a more stable banking

system. Bank regulators corroborate that the minimum capital standards guaranties well-functioning banking

systems. The main reasons for the growing interest in risk are the several financial institutions’ failures in the

recent financial crisis, Acharya (2006). Other recent investigations have looked into the financial crisis. They

find that the bank's risk has increased. Thus, the financial crisis affects the banking sectors, although, bank

capital requirements. After the crisis, bank capital requirements have increased and become more complex.

The majority of previous studies used foreign exchange risks, Business risks and risk management. Our

study contributes to this literature by using the systemic risk measure "SRISK" as proposed by Brownlees

and Engle (2017) and applied to the Canadian Banking by Coleman et al. (2017).

To the best of our knowledge, the empirical investigation on capital structure did not examine the French

financial sector. The French banking sector is characterized by a small number of universal banks. The

foreign bank’s introduction into the French market has increased regarding several deregulation measures.

The foreign bank’s number continued to rise until 2000. The globalization of the French banking sector

accounts for the importance of the presence of foreign institutions in France. The domestic banks are in their

majority retail banking while the foreign banks are essentially operating in wholesale banking and securities

trading. Indeed, the development of the foreign presence among the commercial banks in the French market

has been a result of deregulation5

and modernization of the banking sector. The foreign banks have risen

during the nineties and decreased since 1997. The first explanation is the creation of subsidiaries as the

opening of branches in free establishments. The second explanation is the growth of the French commercial

bank’s number in recent years, which have confirmed a downward trend since 1997. The data show that 63%

of commercial banks are mostly foreign banks and they keep increasing. This seems to be a suitable context

to study domestic and foreign banks (see appendix A).

Generally, the potential effects of the agency costs hypothesis in banking raise important research and

policy questions, given some of the recent problems in the financial industry such as the informational

opacity of firms, the regulation and vital roles of this industry in the economy. This banking industry also

provides a particularly good laboratory for testing the hypotheses because of the micro-data quality and

because of the previous evidence that links bank efficiency with leverage. As emphasized by Laeven and

Levine (2009) and as demonstrated by the recent financial crisis, the systematic risk affects financial and

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economic stability. They corroborate that international and national agencies propose an array of regulations

to shape bank risk. They prove that bank regulations are related to the systematic risk. Although banks are

regulated, we will focus on differences among banks that are driven by differences in regulation. On the one

hand, there is a link between capital structure and capital regulation for banks. On the other hand, there is a

relationship between SRISK and capital requirements which has received considerable attention.

Previous studies have advertised the value of simultaneously and dynamic examining Leverage, Capital

Regulation, SRISK across bank ownership type. To fill in the gap in studies about domestic and foreign

banks, we check the dynamic interrelationships between the capital structure, capital regulation and SRISK

that should be more important for banks before and during the financial crisis. We are the first to investigate

this analysis by using PVAR in order to check dynamics bi-directional interrelationships. This analysis

provides a first glance at the potential effect of capital regulation on domestic and foreign banks. The capital

regulation should be a little explanatory power of bank’s specific factors that determine the capital structure

(Gropp and Heider, 2010). We also base our analysis on the corporate finance literature and the buffer view

of the capital.

Methodology and Data

The aim of this study is to examine the simultaneous multi-directional relationship between Bank

capital structure, capital ratio and SRISK. To do so, we see a need to study the dynamic of the

interrelationships and causality factors. The lack of consensus is the result of the lack of a rigorous statistical

treatment. We intend, however, to study simultaneously the three factors that may exert different effects on

the financial sector with macro-econometric approach. Most of the empirical studies that address the issue of

capital structure and ownership type are limited at least to our knowledge to the capital structure determinant

in the industrial sector.

Empirical Methodology

In the empirical analysis, we use the Panel-data Vector Auto-Regression (PVAR) methodology to

overcome the above econometric problems. This method represents a hybrid econometric methodology that

combines the traditional VAR approach which considers all the variables in the structure as endogenous,

with the panel-data technique, which allows for an explicit inclusion of a fixed effect in the model, (Shank

and Vianna, 2016). Canova and Ciccarelli (2013) confirm that the PVAR is able to capture both static and

dynamic interdependencies. It permits to treat the links across units in an unrestricted fashion. It incorporates

easily time variations in the coefficients and in the variance of the shocks. PVAR accounts also for cross-

sectional dynamic heterogeneities. Similar to the conventional VAR model, which was first introduced by

Sims (1980), the PVAR methods examine the time-series property of each variable via the panel unit-root

test.

As Abrigo and Love (2015), we consider the following k-variable homogeneous panel VAR of order p, with

panel-specific fixed effects represented by the following system of linear equations:

5 It is especially after the entry into force of the Banking Act of 1984.

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(1)

Where is a vector of Risk (SRISK); is a vector of Capital Regulation (Capital

Ratio) covariates; is a vector of Capital Structure (LEVERAGE) covariates; and are

vectors of dependent variable-specific panel fixed-effects and idiosyncratic errors, respectively. The

matrices and the matrix ( , ) are parameters to be estimated. We

assume that the innovations have the following characteristics: and

for all . Following Love and Zicchino (2006), we employ Choleski decomposition to

ensure identification. Choleski decomposition requires an ordering of the variables from least to most

endogenous, such that variables ordered first in the system have a contemporaneous and a lag effect on the

subsequent variables, whereas variable order later in the system has only a lag effect on the preceding

variables6. The specific causal ordering imposed on the system is SRISK, CAPITAL RATIO and

LEVERAGE.

Abrigo and Love, (2015) demonstrate that the PVAR in (1) presents a problem with dynamic

interdependencies and cross-sectional heterogeneities. Therefore, the heterogeneity between different units is

captured exclusively by the fixed effects variable μi. Thus, the ordinary least squares (OLS) method cannot

be applied because the individual effect term Ai is correlated with the error term in dynamic panels and the

estimation through OLS leads to biased coefficients (cf. Nickell, 1981). In order to remedy this problem, the

PVAR models may be determined from equations estimated with the GMM7 which is applied in this paper for

170 banks over 13 years. The advantages of this approach are manifold. We estimate unbiased fixed-effects

average coefficients for short panels (N > T) by using Arellano-Bond. The findings also control all of the

time-invariant features that are usually considered in the empirical literature. Each equation has the first

difference of an endogenous variable on the left-hand side and p lagged first differences of all endogenous

variables on the right-hand side.

We use the equation-by-equation GMM estimation yields which are consistent estimates of panel VAR as

Abrigo and Love (2015). They show that estimating the model as a system of equations may result in

efficiency gains. They suppose the common set of instruments is given by the row vector ,

where , and equations are indexed by a number in superscript. They propose the following

transformed panel VAR model based on equation (1) but represented in a more compact form:

(2)

6 (see, for details, Hamilton, 1994)

7 GMM is developed by Arellano and Bond (1991) and extended by Arellano and Bover (1995) and Blundell and Bond (1998)

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The stability condition of the PVAR model implies that this model is invertible and has an infinite-order

vector moving-average (VMA) representation, providing a known interpretation to estimated impulse-

response functions (IRF). The evidence from this analysis is mostly based on the results of the simple

impulse-response function which may be computed by rewriting the model as an infinite vector moving-

average, where are the VMA parameters.

(3)

The Impulse responses are presented along with their 5% and 95% percentile bounds that have been

produced by Monte Carlo simulations with 200 and 1000 replications. Therefore, whenever the zero line lies

outside the confidence bands, there is evidence of a statistically significant response to the shock inflicted.

The same condition of stability is required to the FEVD8 and the confidence intervals may be derived

analytically or estimated using various re-sampling techniques. The FEVD is a measure of the effect of the

innovations in variable k on variable i, Lütkepohl, (2007). This method measures the fraction of the error in

forecasting variable i after h periods that is attributable to the orthogonal innovations in variable k. The

FEVD is always predicated upon a choice of P and demands a specific order of the variables because the first

variable affects all the other contemporaneously and with a lag as well. In our study, we first treat the

leverage as the most endogenous and in the end we treat the risk as the most exogenous. The order may be

sensitive in case there are high residual correlations.

In this paper, we apply the STATA programs implemented by Abrigo and Love (2015) to estimate

the PVAR model. They propose a Helmert transformation to address the orthogonality problem. A

theoretical framework could also help guide an appropriate empirical ordering of the variable from least to

most endogenous. Theoretically, the banks face systematic risk. The bank regulations are a second order

importance in determining the capital structure, Gropp and Heider, (2010). Thus, we examine empirically the

interrelationship between SRISK, Capital Regulation and Bank Capital Structure simultaneously. We select

the measures of Bank specific SRISK, Capital Ratio and LEVERAGE. Data used in the estimation process

will be presented in the next section.

Data Description

Our paper includes 170 banks operating in the French market divided between 105 domestic banks

and 65 foreign banks, available in the Bankscope database of Bureau van Dijk cover the period of 2000-2015

and 2209 yearly observations. We chose the single country study because the sample exhibits considerable

heterogeneity when we use more than a country with different regulations (Gropp and Heider 2010). Frank

and Goyal (2004) explain the controversial results by the use of heterogeneous samples across time and

8 The h-step ahead forecast-error is: / , where is the observed vector at time and is

the -step ahead predicted vector made at time .

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countries. Our panel is weakly unbalanced mainly because foreign banks did not have complete data for the

sample period. The OECD report shows that there were 73 foreign commercial banks in the French market in

2007 and that number fell to only 54 banks by 2009. We investigate the two-period pre-crisis 2000-2007 and

crisis years 2008-2015. Additional data for nation-specific and market-specific data were drawn from

Banque de France. We collect our data on bank-specific variables from the financial statements (balance

sheets and income statements). The classification of domestic and foreign banks is also extracted from the

annual reports of each bank.

Empirical studies have not reached a consensus on what is the most suitable indicator to measure

Bank Capital Structure, Regulation and Risk. In this paper, we refer to the existing literature to choose the

variables used to investigate this topic.

A. Capital structure measure

As Gropp and Heider (2010), we use the Book Leverage (LEVERAGE) measure:

We use the book value for commercial banks because the capital regulation is imposed on book

value and not on the market one. The debt ratio is also considered as a risk measure. A high ratio is related to

a low bankruptcy risk. Thus, the access to funds is done at a low cost that results in the increase of profits.

The financial debt agency costs between shareholders and lenders may have a negative impact. Gropp and

Heider (2010) prove that riskier banks that are close to the regulatory minimum do not adjust their capital

structure towards more equity, as the buffer view would predict.

B. Capital Requirements

Berger et al. (1995) point out that the social cost of bank failures justifies the existence of capital

requirements for commercial banks. Schoenmaker (2015) emphasize that regulators, as well as rating

agencies, are now stressing the use of Common Equity Tier 1 capital, which consists of common shares

issued (including share premium) and retained earnings. Tier 1 capital is seen as the best form of capital and

which is being the predominant form of Tier 1 capital. Thus, we are rightly returning to straight accounting

equity capital for regulatory purposes. Schoenmaker (2015) corroborate that the basic purpose of regulatory

capital is to absorb losses in order to protect other claimholders, especially deposit holders. Banks react to

these crises by holding higher levels of capital. An analysis of bank capital shows that banks adjusted their

Tier 1 capital ratios according to the risks that they were taking. The concept of regulatory capital, often

described as capital requirements, was only introduced in the 1970s and moved to a risk-weighted capital

ratio in the 1975’s. The aim of risk-weighted assets is to move from a static capital requirement to a

requirement based on the riskiness of a bank’s asset class. Gropp and Heider, (2010) measure regulation by

the Tier 1 capital ratio which is composed of the book value of equity over assets weighted by risk, as

specified in Basel I. Risk-weighted assets are the total of all assets held by the bank weighted by credit risk,

(4)

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according to a formula determined by the regulator, generally the country’s central bank. The majority of

banks pursue the Basel Committee on Banking Supervision guidelines for the formulae of asset risk weights.

The risk-based capital (RBC) regulations are based on the international Basel Accords. The RBC ratio

dictates the minimum amount of equity capital that must be maintained by a bank based on the riskiness of

the bank’s asset holdings, Hogan et al. (2016). The Capital Ratio is:

As Barth and Miller (2017) 9 we take into account the changing measurement of capital requirements

and the risk-weighting of assets over time, under Basel I, II, 2.5 and Basel III in the analysis. Gropp and

Heider (2010) conclude that bank capital requirements do introduce a non-linearity in the behaviour of banks

when capital falls to levels that are very close to the regulatory minimum. We examine the dynamic

interrelationships between SRISK, bank capital regulation and bank’s leverage.

C. SRISK Measure

Acharya et al. (2012) and Brownlees and Engle (2017) proposed the SRISK measure. It is defined as

the expected capital shortfall of a firm conditional on a prolonged market decline. Coleman et al. (2017)

confirm that the simplicity and transparency of the SRISK measure make it particularly attractive for

analyzing the systemic risk of financial institutions. As explained by Brownlees and Engle (2017), SRISK is

a function of the institution's size, leverage, and expected equity loss conditional on the market decline which

is referred to as Long Run Marginal Expected Shortfall (LRMES). SRISK also takes balance sheet

information into account. Engle et al. (2015) apply the SRISK methodology to the European financial

institutions. The Basel risk weights attempt to capture the risk of the assets an institution holds, and thus

determine the capital requirement for the institution. Acharya et al. (2012) emphasize that the LRMES

component of SRISK incorporates the risks of underlying assets and the SRISK as a whole complements the

risk weight approach by taking into account the risk contribution of the institution itself.

Brownlees and Engle (2017) corroborate that SRISK predicts the Capital Shortfall (CS) of a firm in

case of a systemic event:

9 “Basel I was finalized in July 1988 and implemented over the period 1988–1992. Basel II was finalized in June 2004 and

implemented over the period 2007–2010. Basel II.5 was finalized in July 2009 and meant to be implemented no later than December

31, 2011. Basel II.5 enhanced the measurements of risks related to securitization and trading book exposures. Basel III was finalized

in December 2010 and meant to be implemented over the period 2013–2018. The phasing works by capping the amount that can be

included in capital from 90 percent on January 1, 2013, and reducing this cap by 10 percent in each subsequent year. The leverage

ratio is calculated as the ratio of Tier 1 capital to balance-sheet exposures plus certain off-balance-sheet exposures."

(5)

= (Prudential Capital) – (Stressed MV of Equity)

= k (Debt + Stressed MV of Equity) – (Stressed MV of Equity)

= k(Debt) − (1 − k)(Stressed MV of Equity)

= ( ) – ( – ) ( – ) y (6)

13

SRISK is a modification of the Capital Shortfall equation: The capital reserves (ex. regulatory) less

the firm’s equity: = ( ) − 1 − (Equity)

Where: is the Prudential capital ratio and set to 8% for US and Canadian companies and 5.5% for

European ones; (Long-Run Marginal Expected Shortfall) is the expected percentage loss of a firm’s

equity value during a crisis scenario. It is estimated by averaging the fractional returns of the firm’s equity in

simulated crisis scenarios. It also captures the co-movement of the firm’s equity with the market during a

crisis.

In this paper, we choose the systemic risk measure because the regulatory capital focuses especially,

on systemic risk to promote a more stable banking system. We use the "SRISK" measure as proposed by

Brownlees and Engle (2017) and as applied by Coleman et al. (2017) to the Canadian Banking.

The research hypotheses are set for the bank-specific effect. These are formulated as follows:

Hypothesis 1. There is a significant dynamic interrelationship between bank capital structure, capital

regulation and risk for all banks and over a whole period.

D. Bank Ownership type

We distinguish between foreign and domestic banks (the omitted category). A bank’s ownership may

affect its access to strategies needed for financing. Berger et al., (2008) corroborate that foreign banks may

have cheaper financing overseas or via their parent firm. However, domestic banks may secure financing

from government agencies directly or gain access indirectly by virtue of an implicit government guarantee.

The Bank Ownership type (foreign) is measured by a dummy variable equal to 1 if the main bank is a foreign

bank and 0 otherwise10

as defined by Berger et al., (2008). Besides, we examine whether the interrelationship

between bank risk, bank capital regulations and leverage depends on each bank’s ownership type. These

dynamic interrelationships must be checked across bank ownership type —foreign and domestic banks—.

Hypothesis 2. There is a significant dynamic interrelationship between capital structure, regulation and

SRISK taking for Foreign Banks and for Domestic Banks.

Once the null hypothesis is rejected, we check the secondary hypotheses:

Hypothesis 2.1. There is a significant dynamic interrelationship between Capital Structure and

Regulation for Foreign Banks and for Domestic Banks.

Hypothesis 2.2. There is a significant dynamic interrelationship between Capital Structure and

SRISK for Foreign Banks and for Domestic Banks.

Hypothesis 2.3. There is a significant dynamic interrelationship between Regulation and SRISK for

Foreign Banks and for Domestic Banks.

E. Financial crisis

10 The Source used is RBI.

14

Bandt et al. (2014) emphasize that the financial crisis has renewed attention to the role of bank capital

because many highly levered banks’ failed or had to be bailed-out by governments. Moreover, the SRISK

affects financial and economic fragility as emphasized by the financial crisis. The financial crisis is

characterized by a recession as a result of a liquidity shortfall in the French banking system that affects the

stability and the competition. In addition, the crisis changes the banking sector characteristics and the strict

regulation may cause a lower profitability, Maudos (2017). The author also shows that the excessive debt

will lead to a hard adjustment in the post-crisis period. Isaiah (2017) corroborates that the 2008 financial

crisis has brought great implications, mainly on regulatory capital.

This paper conducts an empirical study to analyze the dynamic interrelationships between Bank

Capital Structure, Regulation and Risk, and partitions the sample, first by ownership type and second by pre-

and post- financial crisis. The stated motivation for the study is that previous studies have not directly

considered whether domestic or foreign ownership capital structure – regulation – risk interrelationships, and

whether it changes with the financial crisis. Referring to Isaiah (2017) we divided our study period (2000–

2015) into two sub-periods before the financial crisis (2000–2007) and during the financial crisis (2008–

2015).

Hypothesis 3. There is a significant dynamic interrelationship between Capital Structure, Regulation and

SRISK before and during the financial crisis.

Once the null hypothesis is rejected, we check the secondary hypotheses:

Hypothesis 3.1. There is a significant dynamic interrelationship between capital structure and capital

regulation before and during the financial crisis.

Hypothesis 3.2. There is a significant dynamic interrelationship between Capital Structure and

SRISK before and during the financial crisis.

Hypothesis 3.3. There is a significant dynamic interrelationship between capital regulation and

SRISK before and during the financial crisis.

Panel unit root test

Initially, we check the data stationarity11

. The results reveal the fact that leverage, bank capital

regulation and the SRISK series are stationarity or integrated of order zero. We use the well-known panel

unit-root test developed by Im et al. (IPS, 2003). Numerous test models based on Dickey and Fuller (1979)

works’ have been developed despite little consensus on whether the time trend should be included and on the

selection of lag length. In the panel data test, the autoregressive coefficient is restricted to be homogenous

across all units. The first difference is used for the removal of the panel-specific fixed effects.

11 To ensure that the variables in our system are stationary, we conducted a Fisher-type panel unit-root test based on an augmented Dickey–Fuller test for each

variable (see Choi (2001) for details). The test rejected the null hypothesis that for each variable all panels contain a unit root in favour of at least one panel is

stationary. The test results are available upon request.

15

Descriptive statistics

Table 1 summarizes several descriptive statistics for leverage, capital ratio and SRISK. It presents

the distribution of French banks divided between domestic and foreign banks and displays the characteristics

and the distribution of the sample. The data show that the commercial banks are in their majority foreign

banks 63%, which also keep increasing. Referring to the IMF, foreign banks operating in France are from 12

“Developed markets” and 6 “Emerging markets”. Developed countries include banks from Belgium,

Germany, Ireland, Italy, Luxembourg, Netherland, Portugal, the United Kingdom, USA, South Korea, Spain

and Switzerland. Emerging countries includes banks from Lebanon, Morocco, Poland, Qatar, Russia and

Tunisia (see appendix A).

-------------------------------------------------------

Insert Appendix A here

--------------------------------------------------------

Appendix A reports the evolution of the number of banks present in our sample from 2000 to 2015.

The number of commercial banks in 2000 was 157 and it increased each year to reach 165 banks in 2005.

Then, the number decreased to reach in 2015 a number of 109 commercial banks. The number of domestic

banks varied between 96 and 102 from 2000 to 2007. In 2008, this number decreased to reach 71 from 2000

to 2005. The number of foreign banks varies between 61 and 63. There is an increase in the local presence of

foreign banks’ before the crisis period. Since 2006, the French commercial banks have supported a number

of modifications related in part to the change in the macroeconomic environment (crisis…) and to new

regulatory requirements regarding liquidity and their own funds. The sample size decreases drastically (from

152 to 110 banks) starting from 2008. The main explanation is that French banking sector was characterized

by the mergers and acquisitions in the crisis period12

. The commercial banks’ have been forced to adjust their

capital in terms of exposure to risk. Thus, the increase of risk level is related to their banking business.

Claessens and Horen (2015) also show that since the crisis, foreign bank presence has declined much less.

Banks from countries facing systemic crises exited markets and curtailed their subsidiaries' growth. Banks

were more likely to sell smaller, more recent investments and enter closer and more important trading

partners, shunning crisis and the euro area countries.

There is a large variation in capital structure ratio of the commercial banks that is way capital

structure deserves further investigation. The majority of French commercial banks had a capital structure

ratio under 5%, Jouida (2017).

-------------------------------------------------------

Insert Table 1 here

--------------------------------------------------------

Besides, the average and the standard deviation of leverage, capital ratio and SRISK vary on each

bank’s ownership type. The average value and the standard deviation of domestic banks leverage are above

those of the full sample and those of foreign banks are under them. The reverse phenomenon is observed for

the measures of capital regulation and SRISK.

12

For instance, Fortis bank is acquired by BNP Paribas and Société Générale acquired the foreign bank “Ikar Bank of Ukraine” in 2008.

16

The leverage and capital ratio are correlated only for the all banks and domestic banks (respectively

0,692* and 0,617*). Further, Leverage and risk are negatively related to foreign banks (-0,696*).

Results and Discussion

We assess the first-order panel VAR model by using the GMM estimation. Abrigo and love (2015)

prove that according to the moment and model selection criteria (MMSC) used by Andrews and Lu (2001)

and the overall coefficient of determination (CD), the first-order panel VAR is the preferred model. The

model selection criterion is based on Hansen’s J statistic of over-identifying restrictions and compared to the

model and moment selection criteria by Andrews and Lu (2001). Table 2 presents the results of the tests

applied to measure the Bank Capital Structure, Capital Requirement and SRISK.

-------------------------------------------------------

Insert Table 2 here

--------------------------------------------------------

The used model has the smallest likelihood-based criteria (MBIC, MAIC and MQIC values). This

model requires that the number of moment conditions has to be larger than the number of endogenous

variables. The results of this test and the post-estimation test prove that the first lag model is more stable than

the other potential models as Abrigo and love (2015). The panel VAR analysis is used to study simultaneous

and multi-directional dynamic relationships between LEVERAGE, CAPITAL RATIO and SRISK. We

present the PVAR model coefficients by using “GMM-style” instruments. In this model, all variables are

treated as endogenous.

In Table 3, we separate our period in order to explore the effect of the financial crisis. The first three

columns of table 3 report the findings for all banks over a whole period, the three next columns for a pre-

crisis period (2000–2007) and the three last columns for a crisis period (2008–2015).

-------------------------------------------------------

Insert Table 3 here

--------------------------------------------------------

We show that there is a positive and significant dynamic interrelationship between LEVERAGE,

CAPITAL RATIO and SRISK for all French commercial banks and over a whole period (at the 1% level).

However, the relationship between CAPITAL RATIO and SRISK is negative and significant. Before a crisis

period (2000–2007), a bidirectional dynamic interrelationship between LEVERAGE, CAPITAL RATIO and

SRISK is negative. But, a reverse significant causal relationship has been found between LEVERAGE and

CAPITAL RATIO. After a crisis period (2008–2015), the negative and significant relationship between

CAPITAL RATIO and SRISK has been confirmed.

The regulatory policy is related to systematic risk. This result is coherent with Coleman et al. (2017)

confirming that SRISK can be very sensitive to the choice of prudential capital ratio. The main explanation

for this finding is that after the crisis, the bank capital requirements have increased and become more

complex. Barth and Miller (2017) confirm that capital requirements are important as the first line of defence

in ensuring safer and sounder banking industries.

17

In Table 4, we present the PVAR model coefficients of the interrelationships between LEVERAGE,

CAPITAL RATIO and SRISK for foreign banks. The first three columns of table 4 report the findings over a

whole period, the three next columns for a pre-crisis period (2000–2007) and the three last columns for a

crisis period (2008–2015).

-------------------------------------------------------

Insert Table 4 here

--------------------------------------------------------

We find that there is a positive and significant dynamic relationship between LEVERAGE,

CAPITAL RATIO and SRISK for foreign banks over a whole period (at the 1% level). However, the bi-

directional relationship between CAPITAL RATIO and SRISK is negative and significant. These findings

are similar to those of all French commercial banks. Before a crisis period (2000–2007), there is a negative

relationship between CAPITAL RATIO and SRISK but it is not significant. After a crisis period (2008–

2015), there is a reverse significant relationship between CAPITAL RATIO and SRISK. We find a low

negative value of leverage ratio for the foreign banks. This finding may be explained by the fact that many of

the foreign banks are incentivized to reduce their international operations in the wake of the crisis. It may be

caused by the need to meet stiffer capital requirements and other regulatory changes aimed at strengthening

banking systems as confirmed by Claessens and Horen (2015). But such analyses can also validate

regulatory policy. That appears to be the case with proposals to toughen the leverage ratio. Barth et al. (2006)

also show that the international framework affects the regulation imposed on foreign banks by the domestic

banking sector.

In Table 5, we present the PVAR model coefficients for the interrelationships between LEVERAGE,

CAPITAL RATIO and SRISK for domestic banks. The first three columns of table 5 report the findings over

a whole period, the three next columns for a pre-crisis period (2000–2007) and the three next columns for a

crisis period (2008–2015).

-------------------------------------------------------

Insert Table 5 here

--------------------------------------------------------

We find that there is a positive and significant dynamic relationship between LEVERAGE and

SRISK for domestic banks over a whole of a period (at the 1% level). However, the bi-directional

relationship between CAPITAL RATIO and SRISK is negative and significant. These findings are similar to

those of the French commercial banks. Before a crisis period (2000–2007), there is only a reverse significant

relationship between LEVERAGE and SRISK. After a crisis period (2008–2015), there is a negative

significant relationship between CAPITAL RATIO and SRISK and between LEVERAGE and SRISK. We

find a high positive value of leverage ratio for the foreign banks.

Thus, the findings depend on each bank’s first by ownership type -foreign and domestic banks- and

second by pre- and post financial crisis. The regulatory framework differs between the home and the host

country of foreign banks, Barth et al. (2006). There is a difference between the leverage ratio between

foreign (low negative value) and domestic (high positive value) banks, especially in the post-financial crisis.

18

The main explanation proposed is that the French banks reduce their lending to foreign banks and to non-

bank private institutions affected by the economic and regulatory context13

. These findings are in line with

those of Laeven and Levine, (2009) using the non-dynamic method for regulation and SRISK.

We use both post-estimation tests PVAR Granger causality Wald test and the eigenvalue stability

condition. In table 6, we present the finding of the Granger causality tests for a first-order panel VAR below.

We show that the tests for all the variables are considered endogenous at the usual confidence levels except

the direction of the interrelationships between LEVERAGE and SRISK or CAPITAL RATIO, which are not

significant.

-------------------------------------------------------

Insert Table 6 here

--------------------------------------------------------

We also check the stability condition of the estimated PVAR model. The empirical studies are

interested in the impact of exogenous changes for each endogenous variable with other variables in the panel

VAR system. The PVAR stability requires the eigenvalue module of the dynamic matrix to lie within the

unit circle. The findings table and graph of eigenvalue confirm that the estimate is stable. In table 6 the

modulus of each eigenvalue is strictly less than 1, the estimates satisfy the eigenvalue stability condition. In

figure 1 and 2, we specify that the graph option produced a graph of the eigenvalues with the real

components of the x axis and the complex components of the y axis. The graph below indicates visually that

these eigenvalue are well inside the unit circle. The PVAR satisfies the stability condition for the

LEVERAGE, CAPITAL RATIO and SRISK. The stability condition is not verified only in the post-crisis

period for all French banks and for ownership type - foreign and domestic banks. For this relationship, the

dynamic matrix results are relatively sensitive to the bank’s ownership type and financial crisis.

-------------------------------------------------------

Insert Figure 1 here

--------------------------------------------------------

This result may be explained by the confirmations presented in CB’s 2008 annual report. The

financial crisis is characterized by a recession as a result of a liquidity shortfall in the French banking system

that affects the stability and the competition. The French banks try to increase their holdings of liquid assets

which generate lower capital requirements.

-------------------------------------------------------

Insert Figure 2 here

--------------------------------------------------------

We use the causal ordering, as Abrigo and Love (2015) and we calculate the implied IRF and the

implied FEVD. The IRF confidence intervals are computed using 200 Monte Carlo draws based on the

estimated model. Standard errors and confidence intervals for the FEVD estimates are likewise available not

shown here but, are available upon request.

13 As presented in the Report of Banque de France 2014.

19

-------------------------------------------------------

Insert Table 7 here

--------------------------------------------------------

Based on the FEVD estimates in table 7, we see that as much as 21 percent of the variation in

LEVERAGE can be explained by SRISK and only 4% is explained by the CAPITAL RATIO. The

CAPITAL RATIO explains 9 percent of the variation in LEVERAGE and the same value in SRISK. The

SRISK explains 16 percent of the variation in LEVERAGE and 3% is explained par CAPITAL RATIO. A

dynamic-multiplier function or transfer function measures the impact of a unit increase in an exogenous

variable on the endogenous variables over time. The results from the FEVD identify the LEVERAGE as the

most endogenous variable.

The impulse-response functions describe the reaction of one variable to the innovations in another

variable in the system while holding all other shocks equal to zero, Love and Zicchino (2006). The graphs of

the differences between the impulse responses of the model with three variables are shown in Figs. 3, 4 and

5. There was no significant difference in the response of LEVERAGE to a CAPITAL RATIO shock in either

case for all French banks in pre- and post-financial crisis period.

-------------------------------------------------------

Insert Figure 3 here

--------------------------------------------------------

The main result confirms a significantly different impact of LEVERAGE shocks on CAPITAL

RATIO rather than the risk level. In terms of levels, the IRF plot shows that the SRISK shocks create a

negative and significant response in CAPITAL RATIO and create a positive and significant response in

LEVERAGE and falls to zero very quickly. The same response is observed from LEVERAGE to SRISK

shock of all French commercial banks for one period but, dies out very quickly. However, CAPITAL RATIO

shocks create a smaller response on leverage, a significant negative response in SRISK, though once again

leads to zero very quickly.

-------------------------------------------------------

Insert Figure 4 here

--------------------------------------------------------

In Figure 4, the foreign banks have a negative response of LEVERAGE to a CAPITAL RATIO

shock in the pre-crisis period. There is a negative fluctuation in the response of LEVERAGE and CAPITAL

RATIO to SRISK shock.

-------------------------------------------------------

Insert Figure 5 here

--------------------------------------------------------

The same response is observed for domestic banks and for all French commercial banks. However, there

is a positive LEVERAGE shock to SRISK. In terms of levels, the IRF plot shows that the SRISK shocks

create a positive and significant response in SRISK and create a positive and significant response in

LEVERAGE.

20

To conclude, the IRF and the FEVD resulting from the vector auto-regressions support our claim that in

the presence of regulation, which is more stringent in a crisis period, the SRISK affects leverage decisions.

Regulation is manifest not only in the higher response of leverage to SRISK but, also in the lower response

of leverage to regulation. Both of these effects imply that regulation adversely affects the dynamic leverage,

thus leading to an inefficient allocation of capital.

The finding is coherent with theory. Higher capital levels allow banks to absorb larger shocks and

alleviate the incentives of banks shareholders to take-on excessive risk. The findings of Granger causality

Wald test along with the time path of the impulse response. It provides strong statistical evidence for the

presence of the inverse bidirectional interrelationship between leverage and SRISK and between Capital

Ratio and SRISK for all banks and for the full period. This result emphasizes that the Capital Ratio of all

banks is relatively sensitive to the financial crisis. The regulatory capital factors influence financial stability

in banks. Another explanation is with regard to the lack of adequate regulatory supervision over the

valuations in the financial crisis period, Grosse (2017). The financial crisis is characterized by a recession as

a result of a liquidity shortfall in the French banking system that affects the stability and the competition. In

addition, the crisis changes the banking sector characteristics and the strict regulation may cause a lower

profitability, Maudos (2017). The author also shows that the excessive debt will lead to a hard adjustment in

the post-crisis period. We find the same result with the lower amplitude in crisis and for domestic banks. Our

results are sensitive after taking into account the bank’s ownership type and the financial crisis.

CONCLUSION

This paper analyzes the simultaneous and dynamic interrelationships between Bank Capital Structure,

Capital Requirements and the systemic risk (SRISK) across bank ownership type—foreign and domestic

banks— before and during the financial crisis. To overcome econometric problems (endogeneity and

causality), we build a Panel Vector Auto-Regression which combines the advantages of traditional VAR

modeling with the advantages of a panel-data approach for 170 banks operating in the French market over

the period 2000-2015. The panel VAR analysis is used to study dynamic and multi-directional

interrelationships.

The Tier1 capital ratio is used as a proxy for regulation. The majority of previous studies is interested in

the capital structure determinants and neglect capital requirements. This prudential capital ratio may capture

information about the financial stability. Thus, bank regulatory standards have changed several times in

response to the recent banking crisis, Barth and Miller (2017). Brownlees and Engle (2017) confirm that the

2007-2009 financial crisis highlighted the need for better tools to measure systemic risk. They prove that the

SRISK analysis provides useful insights for monitoring the financial system and, retrospectively, it captures

several of the early signs of the crisis.

Our findings are sensitive to the bank ownership type as well as to the crisis period. There is a bi-

directional relationship between leverage, Capital ratio and SRISK for all banks over a full period. These

results emphasize the importance of debts in a domestic bank financing. French domestic banks are more

financed by debt compared to foreign banks. There is a difference between the leverage ratio and foreign

(low negative value) as well as domestic (high positive value) banks, especially in the post-financial crisis.

21

Our results contribute to this literature by showing that the bank leverage is sensitive to the systemic risk

SRISK especially in a financial crisis of the banking sector. They also support the idea of regulating banks

equity and propose to toughen the leverage ratio in the financial sector.

Our paper complements earlier studies in finance literature by Berger et al., (2008) Laeven and Levine,

(2009) Gropp and Heider (2010) Harding et al., (2013) and others. There are bi-directional interrelationships

between leverage and SRISK and between Capital ratio and SRISK. However, we find negative

interrelationships in the crisis period. There is bidirectional relationship negative between SRISK and capital

ratio for domestic banks. However, the reverse result is shown for foreign banks in the crisis period. The

regulatory framework differs between the home and the host country of foreign banks, Barth et al. (2006).

The majority of foreign banks are incentivized to reduce their international operations in the wake of the

crisis. It may be caused by the need to meet stiffer capital requirements and other regulatory changes aimed

at strengthening banking systems as confirmed by Claessens and Horen (2015). Regulators enforce the rules

differently from domestic and foreign banks. Therefore, the insights gained from the model are useful in

guiding the discussion of financial regulatory reforms.

Further, we believe our paper contributes to the literature on capital structure decisions and regulation by

adopting a specific approach to separate the fundamental from the financial factors that have been affected

by systematic risk. The analysis of the impulse-response functions obtained from a panel VAR model

allowed us to get clear evidence of the importance of capital structure for regulation without having to

impose the strong structural assumptions. In conclusion, while supporting earlier results, our paper also

presents a methodology that could be used to further explore the differences in the dynamic bank behavior.

22

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26

Appendix A: Number of Domestic and Foreign commercial banks in France

Year All banks Domestic

banks

Foreign

Banks

Advanced

Economies

Emerging

Economies

2000 157 96 61 49 12

2001 158 96 62 50 12

2002 161 99 62 50 12

2003 164 101 63 51 12

2004 164 102 62 51 11

2005 165 102 63 52 11

2006 157 100 57 46 11

2007 152 96 56 45 11

2008 110 71 39 29 10

2009 110 71 39 29 10

2010 110 71 39 29 10

2011 110 71 39 29 10

2012 109 71 38 28 10

2013 109 71 38 28 10

2014 109 71 38 28 10

2015 109 71 38 28 10

Notes: We follow IMF classification to distinguish between foreign banks operating in France from “Advanced economies” and from “Emerging economies”. Advanced Economies group includes banks from Belgium, Germany, Ireland, Italy, Luxembourg, Netherland, Portugal, United Kingdom, USA, South Korea, Spain and Switzerland. Emerging Economies group includes banks from Lebanon, Morocco, Poland, Qatar, Russia and Tunisia

27

Table 1: Descriptive Statistics

Summary statistics

All Banks Domestic Banks Foreign Banks

Variable Obs. Mean Std. Dev. Obs. Mean Std. Dev. Obs. Mean Std. Dev.

LEVERAGE 2,828 89.09 16.04 1681 86.50 19.13 1,147 90. 63 13.66

CAPITAL RATIO 2,828 41.70 59.53 1681 39.55 48.47 1,147 42.98 65.20

SRISK 2,827 1.22 3.12 1680 0.92 2.65 1,147 1.39 3.35

Correlation Matrix

Variable LEVERAGE CAPITAL

RATIO SRISK LEVERAGE

CAPITAL

RATIO SRISK LEVERAGE

CAPITAL

RATIO SRISK

LEVERAGE 1

1

1

CAPITAL RATIO 0,692* 1

0,617* 1

0,675* 1

SRISK -0,244* -0,443* 1 0,337* -0,550* 1 -0,696* -0,360* 1

We use the variables: Book leverage measured as the ratio of , Tier 1 CAPITAL RATIO: and RISK measured by

SRISK. The sample includes 170 banks operating in the French market divided between 105 domestic banks and 65 foreign banks, available in the Bankscope database of Bureau van Dijk cover the

period of 2000-2015. P-values are given in parentheses.* denotes Statistical significance at the 10% level. ** denotes Statistical significance at the 5% level. *** denotes Statistical significance at the

1% level. They are all measured by taking the first log difference of the level variable.

28

Table 2: PVAR’s optimal moment and model selection criteria

Selection order criteria

Sample: 2002- 2015 No. of obs = 2349

No. of panels = 170

Ave. no. of T = 7.935

Lag CD J J pvalue MBIC MAIC MQIC

1 .9828275 46.89498 .0101729 -132.1691 -7.105019 -55.26798

2 .9824424 16.85423 .5331478 -102.5218 -19.14577 -51.25441

3 .9603252 13.37616 .1463111 -46.31186 -4.623845 -20.67817

29

Table 3 PVAR’s estimates for all Banks before and during the financial crisis

Panel Vector Auto-Regression

GMM Estimation

Initial weight matrix: Identity

GMM weight matrix: Robust

(1) (2) (3) (4) (5) (6) (7) (8) (9)

All Banks Full Period (2000–2015) Pre-Crisis Period (2000–2007) Post-Crisis Period(2008–2015)

Impulse Variables

Response Variable LEVERAGE CAPITAL

RATIO SRISK LEVERAGE

CAPITAL

RATIO SRISK LEVERAGE

CAPITAL

RATIO SRISK

L.LEVERAGE 0.637*** 0.049* 0.026*** 0.797*** 0.233* -0.025* -0.706** -0.043 -0.020 (0.000) (0.081) (0.009) (0.001) (0.068) (0.079) (0.011) (0.555) (0. 331) L.CAPITAL RATIO 0.679*** 0.873*** -0.066*** -0.077* 0.185 -0.020* -0.082* 0.125 -0.016* (0.000) (0.000) (0.000) (0.016) (0.354) (0.061) (0.084) (0.529) (0. 007) L.SRISK 1.457*** -0.247* 0.331*** -1.835*** -6.435*** -0.015 0.964* -0.094* 0.234 (0.007) (0.026) (0.000) (0.002) (0.002) (0.896) (0.041) (0.038) (0.286) Observations 2360 2360 2360 1 266 1266 1266 1170 1170 1170 No. of panels 170 170 170 140 140 140 170 170 170 Final GMM

CriterionQ(b) 0.068 0.068 0.068 0.929 0.929 0.929 340 340 340

Eigenvalue Stabily

Condition 0,973 0,690 0,179 0,823 0,454 0,309 0.059 0.059 0.059

We use the estimation of PVAR method proposed by Abrigo and Love (2015). The variables: Book leverage measured as the ratio of , CAPITAL RATIO:

and RISK measured by SRISK. The sample includes 170 banks operating in the French market, available in the BankScope database of Bureau van Dijk

cover the period of 2000-2015 divided into pre-crisis period (2000–2007) and the Post-crisis period (2008–2015). P-values are given in parentheses.* denotes Statistical significance at the 10% level. **

denotes Statistical significance at the 5% level. *** denotes Statistical significance at the 1% level. They are all measured by taking the first log difference of the level variable.

30

Table 4 PVAR’s estimates Foreign Banks before and during the financial crisis

Panel Vector Auto-Regression

GMM Estimation

Initial weight matrix: Identity

GMM weight matrix: Robust

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Foreign Banks Full Period (2000–2015) Pre-Crisis Period (2000–2007) Post-Crisis Period(2008–2015)

Impulse Variables

Response

Variable LEVERAGE

CAPITAL

RATIO SRISK LEVERAGE

CAPITAL

RATIO SRISK LEVERAGE

CAPITAL

RATIO SRISK

L.LEVERAGE 0.510** 0.046* 0.003 -0.244 -0.816*** -0.044** -0.250* 0.017 -0.005 (0.013) (0.096) (0.720) (0.134) (0.000) (0.031) (0.099) (0.182) (0.519) L.CAPITAL

RATIO 2.443*** 0.988*** -0.051* -0.267 -0.042 -0.046 -1.026** 0.794** 0.134***

(0.000) (0.000) (0.087) (0.156) (0.772) (0.154) (0.032) (0.029) (0.005) L.SRISK 2.387** -0.178* 0.427*** 0.079 -0.681 0.179 0.120 -0.368** -0.330***

(0.048) (0.061) (0.000) (0.730) (0.294) (0.245) (0.790) (0.046) (0.001)

Observations 1520 1520 1520 1095 905 905 910 910 910 No. of panels 65 65 65 65 65 65 65 65 65 Final GMM

CriterionQ(b) 0.075 0.075 0.075 0.043 0.034 0.042 0.065 0.055 0.057

Eigenvalue Stabily Condition

1.160 0.518 0.246 0.960 0. 158 0. 426 0.860 0. 851 0. 624

We use the estimation of PVAR method proposed by Abrigo and Love (2015). The variables: Book leverage measured as the ratio of , CAPITAL RATIO:

and RISK measured by SRISK. The sub-sample includes 65 foreign banks operating in the French market, available in the BankScope database of Bureau

van Dijk cover the period of 2000-2015 divided into pre-crisis period (2000–2007) and the Post-crisis period (2008–2015). P-values are given in parentheses.* denotes Statistical significance at the 10%

level. ** denotes Statistical significance at the 5% level. *** denotes Statistical significance at the 1% level. They are all measured by taking the first log difference of the level variable.

31

Table 5 PVAR’s estimates for Domestic Banks before and during the financial crisis

Panel Vector Auto-Regression

GMM Estimation

Initial weight matrix: Identity

GMM weight matrix: Robust

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Domestic Banks Full Period (2000–2015) Pre-Crisis Period (2000–2007) Post-Crisis Period(2008–2015)

Impulse Variables

Response Variable LEVERAGE CAPITAL

RATIO SRISK LEVERAGE

CAPITAL

RATIO SRISK LEVERAGE

CAPITAL

RATIO SRISK

L.LEVERAGE 0.745*** 0.019 0.041** 0.633*** -0.030 0.036*** 4.096*** -0.589** -0.979*** (0.000) (0.691) (0.011) (0.005) (0.547) (0.003) (0.000) (0.050) (0.000) L.CAPITAL RATIO 0.145** 0.743*** -0.048*** -0.039 0.169 0.008 0.065 -0.936*** -0.595*** (0.044) (0.000) (0.002) (0.791) (0.715) (0.681) (0.890) (0.010) (0.000) L.SRISK 0.589* -0.332* 0.334*** -0.943* 0.511 0.137 -1.572* -0.717* 0.407 (0.099) (0.094) (0.002) (0.016) (0.338) (0.136) (0.054) (0. 072) (0.114) Observations 1284 1284 1284 1035 1035 1035 935 935 935 No. of panels 105 105 105 105 105 105 105 105 105 Final GMM CriterionQ(b) 0.752 0.752 0.752 0.654 0.654 0.654 0. 562 0. 562 0. 562 Eigenvalue Stabily Condition 0.790 0.790 0.242 0.850 0.670 0.435 0.786 0.865 0.546

Instruments: l(1/4).( LEVERAGE, CAPITAL RATIO and SRISK)

We use the estimation of PVAR method proposed by Abrigo and Love (2015). The variables: Book leverage measured as the ratio of , CAPITAL RATIO:

and RISK measured by SRISK. The sub-sample includes 105 domestic banks operating in the French market, available in the BankScope database of

Bureau van Dijk cover the period of 2000-2015 divided into pre-crisis period (2000–2007) and the Post-crisis period (2008–2015). P-values are given in parentheses.* denotes Statistical significance at

the 10% level. ** denotes Statistical significance at the 5% level. *** denotes Statistical significance at the 1% level. They are all measured by taking the first log difference of the level variable.

32

Table 6: PVAR’s post-estimation tests

Panel VAR-Granger causality Wald test

Ho: Excluded variable does not Granger-cause Equation

variable

Ha: Excluded variable Granger-causes Equation variable

Panel VAR-Granger causality Wald test

Equation/Excluded chi2 df Prob > chi2

LEVERAGE

CAPITALRATIO 7.879 1 0.005

SRISK 15.147 1 0.000

ALL 15.677 2 0.000

CAPITAL RATIO

LEVERAGE 6.507 1 0.011

SRISK 8.888 1 0.003

ALL 10.052 2 0.007

SRISK

LEVERAGE 1.379 1 0.240

CAPITALRATIO 1.398 1 0.237

ALL 2.160 2 0.340

All the eigenvalues lie inside the unit circle.

PVAR satisfies stability condition.

Eigenvalue stability condition

Eigenvalue

Real Imaginary Modulus

0,980 0 0,980

0,609 0 0,609

-0,084 0 0,084

We use the estimation of PVAR method proposed by Abrigo and Love (2015). The variables: Book leverage measured as the

ratio of , CAPITAL RATIO: and RISK

measured by SRISK. The sample include 170 banks operating in the French market divided between 105 domestic banks and 65

foreign banks, available in the BankScope database of Bureau van Dijk cover the period of 2000-2015. P-values are given in

parentheses.* denotes Statistical significance at the 10% level. ** denotes Statistical significance at the 5% level. *** denotes

Statistical significance at the 1% level. They are all measured by taking the first log difference of the level variable.

33

Table 7: Forecast-Error Variance Decomposition

We use the estimation of PVAR method proposed by Abrigo and Love (2015). The variables: Book leverage measured as the ratio of

, CAPITAL RATIO: and RISK measured by

SRISK. The sample includes 170 banks operating in the French market divided between 105 domestic banks and 65 foreign banks, available in the

BankScope database of Bureau van Dijk cover the period of 2000-2015. P-values are given in parentheses.* denotes Statistical significance at the

10% level. ** denotes Statistical significance at the 5% level. *** denotes Statistical significance at the 1% level. They are all measured by taking the first log difference of the level variable.

Response variable

And Forecast

horizon

Impulse Variables

LEVERAGE CAPITAL RATIO SRISK

LEVERAGE

0 0 0 0

1 1 0 0

2 0,748 0,040 0,212

3 0,634 0,038 0,328

4 0,555 0,033 0,412

5 0,497 0,028 0,475

6 0,454 0,025 0,522

7 0,420 0,022 0,558

8 0,394 0,020 0,587

9 0,372 0,018 0,610

10 0,355 0,017 0,628 CAPITAL RATIO

0 0 0 0

1 0,085 0,915 0

2 0,097 0,814 0,090

3 0,094 0,793 0,113

4 0,092 0,782 0,126

5 0,091 0,776 0,133

6 0,090 0,772 0,137

7 0,090 0,769 0,141

8 0,090 0,767 0,143

9 0,090 0,764 0,146

10 0,090 0,763 0,148

SRISK

0 0 0 0

1 0,016 0,003 0,981

2 0,058 0,008 0,934

3 0,076 0,007 0,917

4 0,087 0,006 0,907

5 0,095 0,005 0,899

6 0,101 0,005 0,894

7 0,106 0,004 0,890

8 0,110 0,004 0,886

9 0,113 0,004 0,883

10 0,116 0,003 0,881

34

Figure 1: Graph of eigenvalue in the unit circle for all banks

LEVERAGE CAPITALRATIO RISK FULL PERIOD

-1-.

50

.51

Imag

inary

-1 -.5 0 .5 1Real

Roots of the companion matrix

LEVERAGE CAPITALRATIO RISK PRE-CRISIS

PERIOD

-1-.

50

.51

Imag

inary

-1 -.5 0 .5 1Real

Roots of the companion matrix

LEVERAGE CAPITALRATIO RISK

Post-CRISIS PERIOD

-1-.

50

.51

Imag

inary

-1 -.5 0 .5 1Real

Roots of the companion matrix

LEVERAGE RISK

Post-CRISIS PERIOD

-1-.

50

.51

Imag

inary

-1 -.5 0 .5 1Real

Roots of the companion matrix

The graph shows the eigenvalue stability condition of leverage, Tier1 capital ratio and RISK for the Panel Vector Auto-

regression using the PVAR approach (Abrigo and Love (2015) for the variables: Book leverage measured as the ratio of

, bank capital ratio: and RISK measured by

SRISK. The sample includes 170 banks operating in the French market, available in the BankScope database of Bureau van Dijk

cover the period of 2000-2015 divided into pre-crisis period (2000–2007) and the Post-crisis period (2008–2015). P-values are

given in parentheses.* denotes Statistical significance at the 10% level. ** denotes Statistical significance at the 5% level. ***

denotes Statistical significance at the 1% level. They are all measured by taking the first log difference of the level variable.

35

Figure 2: Graph of eigenvalue in the unit circle for Domestic banks and for Foreign banks

LEVERAGE CAPITALRATIO RISK

Domestic banks

-1-.

50

.51

Imag

inary

-1 -.5 0 .5 1Real

Roots of the companion matrix

Domestic banks

Pre-Crisis Period

-1-.

50

.51

Imag

inary

-1 -.5 0 .5 1Real

Roots of the companion matrix

Domestic banks

Post-Crisis Period

-1-.

50

.51

Imag

inary

-1 -.5 0 .5 1Real

Roots of the companion matrix

LEVERAGE CAPITALRATIO RISK

Foreign banks

-1-.

50

.51

Imag

inar

y

-1 -.5 0 .5 1Real

Roots of the companion matrix

Foreign banks

Pre-Crisis Period

-1-.

50

.51

Imag

inar

y

-1 -.5 0 .5 1Real

Roots of the companion matrix

Foreign banks

Post-Crisis Period

-1-.

50

.51

Imag

inar

y

-1 -.5 0 .5 1Real

Roots of the companion matrix

The graph shows the eigenvalue stability condition of leverage, Tier1 capital ratio and RISK for the Panel Vector Auto-regression using the PVAR approach (Abrigo and Love (2015) for the

variables: Book Leverage measured as the ratio of , Bank Capital Ratio: and RISK measured by SRISK. The

sample includes 170 banks operating in the French market divided between 105 domestic banks and 65 foreign banks, available in the BankScope database of Bureau van Dijk cover the period of 2000-

2015 divided into pre-crisis period (2000–2007) and the Post-crisis period (2008–2015). P-values are given in parentheses.* denotes Statistical significance at the 10% level. ** denotes Statistical

significance at the 5% level. *** denotes Statistical significance at the 1% level. They are all measured by taking the first log difference of the level variable.

Figure 3: Impulse-Response for the Panel Vector Auto-Regression using the PVAR approach for all French banks in pre- and post-financial crisis

period FULL PERIOD

0

1

2

3

-1

0

1

-.5

0

.5

-2

-1

0

1

2

-10

0

10

20

-5

0

5

10

-5

0

5

10

-20

0

20

40

-20

0

20

40

0 5 10 0 5 10 0 5 10

RISK : RISK

CAPITALRATIO : RISK

LEVERAGE : RISK

RISK : CAPITALRATIO

CAPITALRATIO : CAPITALRATIO

LEVERAGE : CAPITALRATIO

RISK : LEVERAGE

CAPITALRATIO : LEVERAGE

LEVERAGE : LEVERAGE

95% CI Orthogonalized IRF

step

impulse : response

PRE-CRISIS PERIOD

-1

0

1

2

3

-10

-5

0

5

10

-10

-5

0

5

10

-10

0

10

-100

0

100

200

-100

0

100

200

-50

0

50

-500

0

500

-500

0

500

0 5 10 0 5 10 0 5 10

RISK : RISK

CAPITALRATIO : RISK

LEVERAGE : RISK

RISK : CAPITALRATIO

CAPITALRATIO : CAPITALRATIO

LEVERAGE : CAPITALRATIO

RISK : LEVERAGE

CAPITALRATIO : LEVERAGE

LEVERAGE : LEVERAGE

95% CI Orthogonalized IRF

step

impulse : response

Post-CRISIS PERIOD

-.5

0

.5

1

-.4

-.2

0

.2

-1

-.5

0

.5

1

-10

-5

0

5

-2

0

2

4

6

-10

-5

0

5

10

-4

-2

0

2

4

-2

-1

0

1

2

-10

-5

0

5

10

0 5 10 0 5 10 0 5 10

RISK : RISK

CAPITALRATIO : RISK

LEVERAGE : RISK

RISK : CAPITALRATIO

CAPITALRATIO : CAPITALRATIO

LEVERAGE : CAPITALRATIO

RISK : LEVERAGE

CAPITALRATIO : LEVERAGE

LEVERAGE : LEVERAGE

95% CI Orthogonalized IRF

step

impulse : response

The figure reports the difference in impulse responses (low–high) of the Panel Vector Auto-regression (Abrigo and Love (2015) for a model with three variables: Book Leverage measured as the ratio of

, Bank Capital Ratio: and RISK measured by SRISK. Our sample include 170 banks operating in the French market, available in the

BankScope database of Bureau van Dijk cover the period of 2000-2015 divided into pre-crisis period (2000–2007) and the Post-crisis period (2008–2015). Errors are 5% on each side generated by Monte-Carlo with 1000 reps.

Figure 4: Impulse-Response for the Panel Vector Auto-regression using the PVAR approach for Foreign Banks in pre- and post-financial crisis period

Full period for Foreign Banks

-1

0

1

2

3

-20

-10

0

10

-10

-5

0

5

10

-10

-5

0

5

10

-200

-100

0

100

200

-100

0

100

200

-40

-20

0

20

40

-500

0

500

1000

-500

0

500

0 5 10 0 5 10 0 5 10

RISK : RISK

CAPITALRATIO : RISK

LEVERAGE : RISK

RISK : CAPITALRATIO

CAPITALRATIO : CAPITALRATIO

LEVERAGE : CAPITALRATIO

RISK : LEVERAGE

CAPITALRATIO : LEVERAGE

LEVERAGE : LEVERAGE

95% CI Orthogonalized IRF

step

impulse : response

Pre-Crisis Period for Foreign Banks

0

.5

1

1.5

-.6

-.4

-.2

0

.2

-1

-.5

0

.5

1

-3

-2

-1

0

1

-2

0

2

4

6

-10

-5

0

5

-.5

0

.5

1

-3

-2

-1

0

1

-5

0

5

10

0 5 10 0 5 10 0 5 10

RISK : RISK

CAPITALRATIO : RISK

LEVERAGE : RISK

RISK : CAPITALRATIO

CAPITALRATIO : CAPITALRATIO

LEVERAGE : CAPITALRATIO

RISK : LEVERAGE

CAPITALRATIO : LEVERAGE

LEVERAGE : LEVERAGE

95% CI Orthogonalized IRF

step

impulse : response

Post-Crisis Period for Foreign Banks

-4

-2

0

2

4

-50

0

50

100

-20

-10

0

10

20

-40

-20

0

20

40

-1000

-500

0

500

1000

-200

-100

0

100

200

-40

-20

0

20

40

-1000

-500

0

500

1000

-200

0

200

400

0 5 10 0 5 10 0 5 10

RISK : RISK

CAPITALRATIO : RISK

LEVERAGE : RISK

RISK : CAPITALRATIO

CAPITALRATIO : CAPITALRATIO

LEVERAGE : CAPITALRATIO

RISK : LEVERAGE

CAPITALRATIO : LEVERAGE

LEVERAGE : LEVERAGE

95% CI Orthogonalized IRF

step

impulse : response

The figure reports the difference in impulse responses (low–high) of the Panel Vector Auto-regression (Abrigo and Love (2015) for a model with three variables: Book Leverage measured as the ratio of

, Bank Capital Ratio: and RISK measured by SRISK. The sub-sample includes 65 foreign banks operating in the French market,

available in the BankScope database of Bureau van Dijk cover the period of 2000-2015 divided into pre-crisis period (2000–2007) and the Post-crisis period (2008–2015). Errors are 5% on each side generated by Monte-Carlo with 1000 reps.

Figure 5: Impulse-Response for the Panel Vector Auto-regression using the PVAR approach for Domestic banks in pre- and post-financial crisis period

Full period for Domestic banks

0

1

2

3

-1

-.5

0

.5

-1

-.5

0

.5

1

-3

-2

-1

0

1

-5

0

5

10

-4

-2

0

2

4

-5

0

5

-10

-5

0

5

10

-20

-10

0

10

20

0 5 10 0 5 10 0 5 10

RISK : RISK

CAPITALRATIO : RISK

LEVERAGE : RISK

RISK : CAPITALRATIO

CAPITALRATIO : CAPITALRATIO

LEVERAGE : CAPITALRATIO

RISK : LEVERAGE

CAPITALRATIO : LEVERAGE

LEVERAGE : LEVERAGE

95% CI Orthogonalized IRF

step

impulse : response

Pre-Crisis Period for Domestic Banks

-.5

0

.5

1

1.5

-2

0

2

4

-.5

0

.5

-10

-5

0

5

10

-100

-50

0

50

100

-20

-10

0

10

20

-4

-2

0

2

4

-20

-10

0

10

20

-20

-10

0

10

20

0 5 10 0 5 10 0 5 10

RISK : RISK

CAPITALRATIO : RISK

LEVERAGEs : RISK

RISK : CAPITALRATIO

CAPITALRATIO : CAPITALRATIO

LEVERAGEs : CAPITALRATIO

RISK : LEVERAGEs

CAPITALRATIO : LEVERAGEs

LEVERAGEs : LEVERAGEs

95% CI Orthogonalized IRF

step

impulse : response

Post-Crisis Period for Domestic Banks

-1

0

1

2

-10

-5

0

5

10

-.4

-.2

0

.2

.4

-20

0

20

40

-200

-100

0

100

200

-10

-5

0

5

10

-5

0

5

-40

-20

0

20

40

-10

-5

0

5

10

0 5 10 0 5 10 0 5 10

RISK : RISK

CAPITALRATIO : RISK

LEVERAGE : RISK

RISK : CAPITALRATIO

CAPITALRATIO : CAPITALRATIO

LEVERAGE : CAPITALRATIO

RISK : LEVERAGE

CAPITALRATIO : LEVERAGE

LEVERAGE : LEVERAGE

95% CI Orthogonalized IRF

step

impulse : response

The figure reports the difference in impulse responses (low–high) of the Panel Vector Auto-regression (Abrigo and Love (2015) for a model with three variables: Book leverage measured as the ratio of

, bank capital ratio: and RISK measured by SRISK. The sub-sample includes 105 domestic banks operating in the French market,

available in the BankScope database of Bureau van Dijk cover the period of 2000-2015 divided into pre-crisis period (2000–2007) and the Post-crisis period (2008–2015). Errors are 5% on each side generated by Monte-Carlo with 1000 reps.