Abstract
This paper studies the links between of financial soundness indicators and financial crisis episodes controlling for several macroeconomic and fiscal variables in 20 OECD countries. We focus our attention on aggregate capital adequacy, asset quality and bank profitability indicators compiled by the IMF. Our key findings suggest that in times of severe financial crisis regulatory capital to risk weighted assets is increased (by about 0.5–0.6 % points; p.p.) to abide by regulatory and supervisory demands, non performing loans (NPLs) to total loans increase dramatically (by about 0.5–0.6 p.p.), but loan loss provisions lag behind NPLs (they fall by about 12.3–18.8 p.p.) and profitability deteriorates dramatically (returns on assets (equity) fall by about 0.3–0.4 (5.0–7.0) p.p.).
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Notes
As stated by Peter Praet “Public debt is commonly held as a low-risk asset by financial institutions and it is also used as collateral in refinancing operations... When the financial markets doubt the sustainability of public debt, the liquidity and even the solvency of financial institutions can deteriorate, in turn potentially destabilising the financial sector.” See Bank for International Settlements (BIS 2011) and discussion therein on the implication that fiscal policy has on monetary and financial stability.
As discussed in Galati and Moessner (2011) the origin of the term macro-prudential traces back to the late 1970s, and became much more commonly used in the post 2007 period. Goodhart et al. (2004, 2005, 2006) have examined theoretically the importance of financial fragility/stability and its consequence for economic policy.
The Greek and the Irish debt problems are very useful examples of inter-linkages between the government and the financial sectors. Despite the fact that the two problems are interrelated, the origin of each one of them is different. The Irish public debt problem stems from the fact that the Irish government assumed the debt and vulnerabilities of the private and banking sector created by the 2008–2009 financial crisis; while, in the case of Greece the sovereign debt problem was passed on to the local banking sector, which had been unaffected by the 2008–2009 financial crisis (see IMF 2010a, b). As stated in IMF (2010b) “sovereign downgrades, increasing loan impairment, and the deteriorating economic outlook have undermined confidence in the Greek banking sector.”
In this study we investigate the effects of financial crisis on banking sector stability. Several previous studies have examined the impact of financial and banking crisis instability on public finance. For example, some previous studies have investigated the direct fiscal implications of past banking system support schemes the determinants of fiscal recovery rates as well as whether costly fiscal interventions reduce output loss (Honohan and Klingebiel 2003). Other studies have investigated the effect of financial crisis on the debt to GDP ratio and GDP growth (Reinhart and Rogoff 2009; Tagkalakis 2013).
Gropp and Heider (2009) find that unobserved time invariant bank fixed effects are ultimately the most important determinant of banks’ capital structures.
As pointed out by Goodhart et al. (2004) there may be a trade-off between efficiency and financial stability, not only for regulatory policies, but also for monetary policy. However, in subsequent research Goodhart et al. (2011) show that interest rate setting is an appropriate instrument in order to maintain financial stability, because in times of a panic or financial crisis the Central Bank automatically satisfies the increased demand for money, i.e. preventing sharp losses in asset values and enhanced asset volatility.
A supplementary material appendix provides additional information.
See Data Appendix. The IMF has created a website (http://fsi.imf.org/) disseminating data and metadata on selected FSI provided by several countries (IMF 2011).
The second drawback which is also stated in Babihuga (2007), Cihak and Schaeck (2007) and IMF (2009) is that FSI metadata is sourced from national sources, implying that due to differences in national accounting, taxation, and supervisory regimes, FSI data might not be strictly comparable across countries. However, contrary to Babihuga (2007) and Cihak and Schaeck (2007) we decide to focus on a smaller sample, i.e., 20 industrialized countries (excluding other emerging market and developing economies for which the IMF reports analogous data). This way we try to avoid major problems in terms of data quality, as well as in terms of non comparability or great diversity and heterogeneity of national definitions.
Poghosyan and Cihak (2009) present a new database on individual bank distress across the European Union from mid-1990s to 2008. Building on this dataset, they analyze the causes of banking distress and identify a set of indicators and thresholds that can help distinguish sound from vulnerable banks and can help as an early warning system. The authors estimate a logistic random effects model robust to heteroskedasticity to identify the determinant of the probability of distress. According to the findings the probability of distress is negatively associated with the level of bank capitalization and earnings. Moreover, the probability of distress is inversely related to asset quality, i.e., the higher loan loss provision profile implies a riskier loan portfolio.
Several authors have developed theories of optimal bank capital structure, in which capital requirements are not necessarily binding (see e.g. Diamond and Rajan 2000). Based on the market discipline view, banks’ capital structures are the outcome of pressures emanating from shareholders, debt holders and depositors which implies that regulatory intervention is non-binding and of secondary importance (see e.g. Flannery and Sorescu 1996; Flannery and Rangan 2008).
We consider the following OECD countries: Australia, Austria, Belgium, Canada, Switzerland, Germany, Denmark, Spain, Finland, France, UK, Greece, Ireland, Italy, Japan, Netherlands, Norway, Portugal, Sweden, and the US, see Data Appendix.
Allen and Gale in a series of contributions have investigated the root causes of financial crisis. For example, Allen and Gale (1998) describe a model where financial crisis are caused by exogenous asset returns shocks, whereas Allen and Gale (2004) describe endogenous crises, where small or negligible shocks set off self-reinforcing and self-amplifying price changes, with a key role played by liquidity (which affects asset prices).
See Table 1 in the supplementary material appendix.
Borio and Drehmann (2009) show that indicators based on asset price and credit developments provide a fairly successful signal for subsequent banking system stability issues.
This definition captures the real share prices falls in late 1990s, early 2000s and in 2008–2009. The exclusion of the 2008–2009 financial crisis would have resulted in having less pronounced effects on FSIs in weak crisis episodes. At the same time it would have implied that we would have to reduce our sample to 1997–2007 and lose valuable information. In any case the main message comes through i.e., that in severe or extreme event financial crisis the effects are bigger than in more normal or reoccurring weaker financial crisis events.
See Table 2 in the supplementary material appendix. Note that in the severe financial crisis definition the average per year fall in real share prices was more pronounced than in the weak financial crisis definition in all OECD countries considered (except in Canada where the average per year fall of real share prices is about the same; however, even in Canada’s case during the 2008–2009 financial crisis the debt ratio increased by much more, real GDP fell to a greater extent and the unemployment rate increase was bigger).
As discussed by Adrian and Shin (2008, 2009) price changes contributed to financial contagion in the post 2007 financial crisis era. As the authors point out “when balance sheets are marked to market, asset price changes show up immediately on balance sheets and elicit response from financial market participants. Even if exposures are dispersed widely throughout the financial system, the potential impact of a shock can be amplified many-fold through market price changes”.
During the 2008–2009 financial crisis bank profitability ratios (returns on assets and on equity) were substantially lower that in the other financial crisis episodes. At the same time capital to asset ratio was lower, but regulatory capital to risk-weighted assets were higher. Non performing loans to total loans were slightly lower that in past financial crisis, but this time loan loss provisioning to non-performing loans was significantly lower (see Tables 3, 4 in the supplementary material appendix).
The underlying specification assumes that changes to bank capital reflect (partial) adjustment towards a target capital rate and exogenous factors (see Shrieves and Dahl 1992). Diamond and Rajan (2000) and Allen et al. (2011) suggest that banks are different and that we should be looking for bank specific factors to explain bank leverage. While Bertrand and Schoar (2003) indicate that managers’ preferences have a direct impact on capital structure, with less risk averse managers choosing a more aggressive strategy and higher leverage. See Guegan and Tarrant (2012) for a discussion of risk management measures that are relevant for the determination of banks’ capital requirements.
Goodhart et al. (2006) develop a theoretical model that allows one to assess the role and impact of capital requirements for the soundness of the financial system and the macroeconomy. As the authors highlight “it is important for crisis management and prevention to study situations where banks’ capital depletes and capital requirements are ‘biting’.”
Jokipii and Milne (2011) and Allen and Gale (2004) discuss how liquidity affects capital and risk. Jokipii and Milne (2011) claim that banks with higher liquidity can decrease their capital and increase their levels of risk. However, banks may hold liquidity as self-insurance against liquidity shocks. In turn, high levels of liquidity expose banks, mainly small ones, to risk-taking (Allen and Gale 2004) leading to increasing levels of capital in order to control risk-taking. In some cases liquidity requirements can be as effective as capital requirements—in this case, the effect of liquidity on capital will be positive the effect on risk will be ambiguous.
The system GMM estimator is less affected by the weak instrument problem compared to the differenced GMM (Arellano and Bond 1991). Omitting the more distant lags might not lead to significant loss of information, see Bond (2002) and Roodman (2009a) on the implication of using too many instruments. Moreover, the two-system GMM estimator is more efficient than the one-step system GMM estimator. The finite-sample correction to the two-step covariance matrix derived by Windmeijer (2005) is implemented.
In all specifications, the test on overidentifying restrictions indicates that the hypothesis that instruments are valid cannot be rejected and that there is no higher-order autocorrelation.
On balanced panels, GMM estimators based on the two transformations return numerically identical coefficient estimates, holding the instrument set fixed (Arellano and Bover 1995). As a robustness test we consider also the two-step difference GMM estimator where we apply the forward orthogonal deviations transformation. We shall refer to these findings in the next sections but we do not present them here due to space limitation. However, they are available in the supplementary material appendix.
Apart from the other macroeconomic and financial crisis variables, in columns 1 and 5 we control for the real short term interest rate, in columns 2 and 6 we control for the real long term interest rate, in columns 3 and 7 we control for the real short term interest rate and the debt ratio, while in columns 4 and 8 we control for the real short term interest rate and the change in debt ratio (see Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12).
A similar result is found when considering the two step difference GMM estimator (with the findings being more robust in the weak financial crisis estimations); see supplementary material appendix.
Saurina (2009) finds that the effect of provisioning in Spain had only a small effect on credit growth while strengthening the solvency of banks through countercyclical buffers. Drehmann et al. (2011) find that the gap between the ratio of credit-to-GDP and its long-term backward-looking trend performs best as an indicator for the accumulation of capital as this variable captures the build-up of system-wide vulnerabilities that typically lead to banking crises. As these authors point out “credit spreads are better in indicating the release phase as they are contemporaneous signals of banking sector distress that can precede a credit crunch”.
However, the long run effects are not statistically significant.
Gropp and Heider (2009) find that unobserved time invariant bank fixed effects are ultimately the most important determinant of banks’ capital structures. While in others studies banks’ capital structures are the outcome of pressures emanating from shareholders, debt holders and depositors which makes regulatory intervention non-binding and of secondary importance (see e.g. Flannery and Rangan 2008).
When considering the two step difference GMM estimator (see supplementary material appendix) we find statistically significant evidence that the real effective exchange rate has a negative impact effect on the capital-to-assets ratio (i.e., a real appreciation lowers the capital to assets ratio) in line with Babihuga (2007). Moreover, we find that an increase in the level of financial intermediation (domestic credit to private sector over GDP) reduces in a significant manner the capital to assets ratio. Finally, we find that an increase in real share prices leads to lower capital-to-assets ratio.
The specification reflects the inter-linkages between the financial sector and the real economy, focusing mostly on the macroeconomic determinants of NPLs. The financial accelerator theory lies behind the modeling of both NPL and its interaction with macroeconomic performance (see e.g Kiyotaki and Moore 1997).
As a robustness test we consider also the two-step difference GMM estimator where we apply the forward orthogonal deviations transformation.
It is worth noting that in the two-step difference GMM estimation the coefficient estimate of financial intermediation is positive and significant across all specifications, i.e., the higher the ratio of domestic credit to private sector to GDP, the bigger the ratio of NPLs to total loans (see supplementary material appendix).
Nkusu (2011) finds that an increase in the policy rate increases NPLs.
We do not account for differences in supervisory practices and quality of banking supervision, but the wider explanatory variable set used and the presence of country effects control for unaccounted country characteristics.
The long run effects of both severe and weak financial crisis are not statistically significant.
Interestingly, contrary to the two step system GMM findings in the two step difference GMM estimation the unemployment rate has a positive and at times significant effect on provisions to NPLs ratio.
In the two step difference GMM estimation we find a positive response following an increase in real short term interest rate (see supplementary material appendix).
We find a negative and quite statistically significant coefficient estimate in case of credit growth and financial intermediation in the two step difference GMM estimation (see supplementary material appendix).
As a robustness test we consider also the two-step difference GMM estimator where we apply the forward orthogonal deviations transformation.
Berger (1995) finds a positive association between capital to assets and return on equity ratios.
However, as Demirguc-Kunt and Huizinga (1999) point out inflation is associated with higher realized interest margins and greater profitability. Inflation brings higher costs–more transactions and generally more extensive branch networks–and also more income from bank float. Bank income increases more with inflation than bank costs do.
Cihak and Schaeck (2010) report that return on equity fall dramatically after the crisis, but at the time of the crisis profits do not deteriorate dramatically.
In the two-step difference GMM estimation returns on equity respond negatively (and statistically significant) to increases in real short and long term interest rates.
As noted by Demirguc-Kunt and Huizinga (1999) “well-capitalized banks have higher net interest margins and are more profitable. This is consistent with the fact that banks with higher capital ratios have a lower cost of funding because of lower prospective bankruptcy costs. ”
As shown in Aspachs et al. (2007) when banks do not have to comply with capital adequacy requirements, shocks that induce a decline in banks profits and an increase in banks’ default rates also produce a fall in GDP. When capital adequacy requirements are in place, most shocks do not result in a fall in bank profits. The reason for this is that banks need to maintain or top up their capital, and they do this by choosing (riskier) investments that raise their profits. The authors also show that a shock to banks’ probability of default and equity induces welfare and output losses.
In this context Adrian and Shin (2009) point out that shadow banking and, in particular the role of securitization, should be examined more thoroughly considering also more stringent financial regulation and the recognition of the importance of preventing excessive leverage and maturity mismatching (which undermines financial stability).
The descriptive statistics of the variables used in the analysis are shown in Table 5 of the supplementary material appendix.
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We would like to thank the Editor Anne Villamil and the reviewers of Annals of Finance. The views of the paper are our own and do not necessarily reflect those of the Bank of Greece.
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A. Data Appendix
A. Data Appendix
We used a yearly unbalanced panel data set (1997–2009) of 20 OECD economies: Australia, Austria, Belgium, Canada, Switzerland, Germany, Denmark, Spain, Finland, France, UK, Greece, Ireland, Italy, Japan, Netherlands, Norway, Portugal, Sweden, and the US.
1.1 A.1 Financial soundness indicators
The financial soundness indicators are taken from successive issues of the IMF’s Global Financial Stability Report (GFSR) from March 2002 to October 2010 (see e.g. the statistical appendix of the October 2010 GFSR, tables 22–27; IMF 2010c).
Capital adequacy is measured by the following variables: Bank capital to assets, bank regulatory capital to risk-weighted assets. We measure asset quality with the ratio on bank non performing loans to total loans and bank provisions to non performing loans. Return on assets and Return on equity measure bank profitability.
The FSI data start in 1997, reflecting the fact that many countries began collecting FSI data in the context of the IMF’s Financial Sector Assessment Programme (Babihuga 2007), which began in 1999. Despite the short time dimension of the dataset (1997–2009), the sample size (20 countries) is sufficient to allow for consistent estimators by taking into account the asymptotic properties (of the relatively larger sample of countries).
The second drawback which is also stated in Babihuga (2007); Cihak and Schaeck (2007) is that FSI metadata is sourced from national sources, implying that due to differences in national accounting, taxation, and supervisory regimes, FSI data might not be strictly comparable across countries. However, contrary to Babihuga (2007) and Cihak and Schaeck (2007) we decided to focus on a smaller sample, i.e., 20 industrialized countries (excluding other emerging market and developing economies for which the IMF reports analogous data). This way we try to avoid major problems in terms of data quality, as well as in terms of non comparability or great diversity and heterogeneity of national definitions.
1.2 A.2 Macroeconomic variables
The fiscal and macroeconomic variables used extent from 1997 to 2009 and are taken from the Economic Outlook of the OECD (OECD 2011).
The change in the debt ratio is calculated as the change in the debt ratio between t and t \(-\) 1. The inflation rate is the percentage change in the GDP deflator (GDP deflator based inflation rate). The real short term interest rate is calculated as nominal short term interest rate minus the GDP deflator based inflation rate. The real long term interest rate is calculated as nominal long term interest rate minus the GDP deflator based inflation rate. The percentage change in real share prices is calculated as the percentage change in share prices minus the percentage change in GDP deflator. The real effective exchange rate variable used is the percentage change in the real effective exchange rate. As financial intermediation we use domestic credit to private sector as a percent of nominal GDP. Credit growth is the percentage change in the domestic credit to private sector.Footnote 53
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Kasselaki, M.T., Tagkalakis, A.O. Financial soundness indicators and financial crisis episodes. Ann Finance 10, 623–669 (2014). https://doi.org/10.1007/s10436-013-0233-6
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DOI: https://doi.org/10.1007/s10436-013-0233-6