اثرات نامتقارن چرخه کسب و کار در ریسک اعتباری بانک

Prior empirical research on the relation between credit risk and the business cycle has failed to properly investigate the presence of asymmetric effects. To fill this gap, we examine this relation both at the aggregate and the bank level exploiting a unique dataset on Italian banks’ borrowers’ default rates. We employ threshold regression models that allow to endogenously establish different regimes identified by the thresholds over/below which credit risk is more/less cyclical. We find that not only are the effects of the business cycle on credit risk more pronounced during downturns but cyclicality is also higher for those banks with riskier portfolios.

In the recent banking literature, the relation between credit risk and the business cycle (so-called cyclicality of credit risk) has been analyzed for both macro financial stability and micro risk management purposes. Indeed, the potential impact of economic developments on banks’ portfolios is relevant for both policy makers, interested in forecasting and preventing banks’ instability due to unfavorable economic conditions, and risk managers, who pay attention to the robustness of their capital allocation plans under alternative scenarios. These different perspectives are not mutually exclusive. In fact, the reform of the Basel Accord on banks’ capital requirements made it clear the need to match the micro and macro dimensions. Focusing on the latter, this paper analyzes the relation between credit risk and the business cycle allowing explicitly for asymmetries, which have been almost always neglected so far. In fact, we seek empirical evidence for the asymmetric behavior of credit risk cyclicality not only through the business cycle but also across different credit risk regimes, a completely unexplored issue so far. Previous work on this topic has focused on the macro prudential perspective trying to quantify the effects of macroeconomic conditions on banks’ asset quality in some countries. For example, Pesola (2001) shows that shortfalls of GDP growth below forecast contributed to the banking crises in the Nordic countries, while Salas and Saurina (2002) demonstrate that macroeconomic shocks are quickly transmitted to Spanish banks’ portfolio riskiness. Similarly, Meyer and Yeager, 2001 and Gambera, 2000 argue that a small number of macroeconomic variables are good predictors for the share of non-performing loans in the US, while Marcucci and Quagliariello (2008b) find that Italian banks’ borrowers’ default rates increase in downturns. Likewise, Hoggarth et al. (2005) provide evidence for the UK of a direct link between the state of the business cycle and banks’ write-offs. Analogous evidence is provided in cross-country comparisons by Bikker and Hu, 2002, Laeven and Majoni, 2003 and Valckx, 2003. However, researchers have not explored the possibility of asymmetric effects, for which the impact of macroeconomic conditions on banks’ portfolio riskiness is dissimilar in different phases of the business cycle. This is very important since bank supervisors are inherently more concerned about downturns rather than expansions. Also, assuming linear relationships may hinder some important characteristics of banks’ riskiness. To the best of our knowledge, the only exception is the paper by Gasha and Morales (2004) who apply a self-exciting threshold autoregressive (SETAR) model to country-level data showing that GDP growth affects non-performing loans only below a certain threshold in a group of Latin American countries. Asymmetries are somehow taken into account in a related strand of literature on credit risk management and structural credit risk models. In particular, some studies on the properties of credit rating transition matrices over the cycle have analyzed whether transition probabilities are affected to a larger (smaller) extent by recessionary (expansionary) conditions. Quite often regime-switching models are used for this kind of investigations. For example, on the basis of GDP growth, Nickell et al. (2000) divide the business cycle into three categories (peaks, normal times and troughs) finding that in peaks low-rated bonds are less prone to downgrades. The impact of the business cycle appears therefore to be asymmetric and dependent on borrowers’ creditworthiness. Likewise, in their analysis of the linkage between macroeconomic conditions and migration matrices, Bangia et al. (2002) find that downgrading probabilities, particularly in the extreme classes, increase significantly in recessions. Pederzoli and Torricelli (2005) adopt a similar framework to assess the impact of the business cycle on capital requirements under Basel 2. However, in this line of research the identification of expansions/recessions is based on some external sources. Most studies in this field employ the NBER business cycle classifications, but some authors like Lucas and Klaassen (2006) cast serious doubts on their use. A further shortcoming of this approach is that it completely ignores the possibility that asymmetries might also depend on the severity of the recession, rather than on the dichotomy expansion/recession. Finally, another gap in this literature is that it does not test the hypothesis that the effects of the business cycle on credit risk are different depending on banks’ portfolios riskiness. In this paper, we address all these issues of asymmetries in credit risk cyclicality. Using threshold regression models and both aggregate and bank level data, we test whether banks which are more exposed to credit risk are affected by the business cycle to a greater extent than those with less-risky portfolios (i.e., whether riskier banks are more cyclical than those less-risky). We also test whether the cyclicality of credit risk is stronger in severe recessions rather than in mild recessions or, a fortiori, during expansionary phases. We start with a standard threshold regression approach at the aggregate level on the time series of the Italian default rates. We then move ahead adopting panel threshold regression models with one threshold variable, which exploit data on borrowers’ default rates at the bank level. These models can be interpreted as regime-switching panel data models where each regime is determined endogenously through one observable threshold variable. We also add to the previous literature suggesting an innovative four-regime panel approach with two threshold variables which allows us to provide a more comprehensive picture of the behavior of default rates over changing economic and credit risk conditions. Our results show that for those banks with lower asset quality, the increase in default rates due to one percentage point decrease in the output gap (our measure of the business cycle) is almost four times higher than the effect for those banks with better portfolios. Furthermore, for models with two or more regimes with one threshold variable, we find that the impact of the business cycle on credit risk is stronger the lower banks’ asset quality. In the four-regime model, where we combine credit risk and the business cycle regimes, we find that (i) during economic slowdowns, the impact of the business cycle on portfolio riskiness for banks with lower asset quality (the a priori riskier ones) is more than three times higher than that for less-risky banks. Also, (ii) the impact of the business cycle on credit risk for banks with lower asset quality during recessions is more than four times higher than what we have during booms. In addition, (iii) during slowdowns the impact of the business cycle on credit risk for banks with better asset quality is almost the double of that during expansions. Finally, (iv) for riskier banks the impact of the business cycle on their riskiness during expansionary phases is about 50% more than that for less-risky banks. In sum, we conclude that riskier banks’ portfolios are more cyclical (i.e., more sensitive to the business cycle) than less-risky ones and cyclicality is more pronounced in bad economic times. Under the Basel 2 new Capital Accord, which introduces risk sensitive capital requirements, this evidence may provide some guidance to banks and supervisors in the choice of adequate capital buffers over different phases of the business cycle. Indeed, the identification of those banks that are more likely to be affected by recessionary conditions – and that therefore should build higher capital buffers in expansion – may help smooth the fluctuations of capital requirements, thus reducing Basel 2 cyclicality (Jokipii and Milne, 2008). For example, using either macroeconomic forecasts or judgmental future scenarios, supervisors may carry out stress tests in order to assess the evolution of banks’ portfolio riskiness should the scenario actually materialize. The reminder of the paper proceeds as follows. Section 2 describes the data on Italian banks’ portfolios. Section 3 presents the single threshold model at the aggregate level with two regimes. In Section 4 we describe the panel data model with single threshold variable and multiple regimes (both credit risk and business cycle regimes). Section 5 delineates the panel data model with two different threshold variables and four regimes. Finally, Section 6 draws some concluding remarks and directions for further research.

Prior research on the macroeconomic determinants of credit risk and its evolution over the business cycle indicates that banks’ portfolio riskiness is cyclical. However, most of these studies often neglect the presence of asymmetric behavior over different phases of the business cycle. This paper examines credit risk cyclicality allowing explicitly for asymmetries not only through the business cycle but also across credit risk regimes. We thus allow for a different degree of cyclicality across banks with dissimilar levels of riskiness. Using a threshold regression approach combined with panel data and exploiting data on Italian banks’ borrowers’ default rates, we find that the impact of the business cycle on banks’ riskiness is significantly more pronounced not only during economic slowdowns but in particular when credit risk levels are higher. In addition, we endogenously identify the risk threshold(s) over/below which such impact is different, thus providing a powerful tool for financial stability monitoring. Among the other results, in the two-regime panel threshold regression model, we find that both less-risky and riskier banks are significantly affected by the business cycle, but the impact is stronger for the latter banks. In particular, the increase in the default rate as a result of one percentage point decrease in the output gap is almost four times higher for riskier banks. This evidence is robust to the use of different proxies for the overall economic conditions and holds at various levels of aggregation. In addition, we find a certain degree of monotonicity in the impact of macroeconomic conditions on credit risk that is higher for riskier banks as well as during recessions. The evidence arising from our panel threshold regression model with two different threshold variables and four regimes is similar. Overall, our results may provide some guidance to banks and supervisors in the assessment of capital buffers in the various phases of the business cycle. Furthermore, the methodology we propose may be employed by supervisors for selecting institutions that – being more cyclical – are more prone to capital requirements fluctuations and thus should be required to build higher capital buffers in good times.