sameh jouida isg, sousse university, tunisia …
TRANSCRIPT
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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
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.