This paper proposes a model to conduct macro stress test of credit risk for the banking sector based on scenario analysis. We employ an original bank-level data set that splits bank credit portfolios in 21 granular categories, covering household and corporate loans. The results corroborate the presence of a strong procyclical behavior of credit quality, and show a robust negative relationship between the logistic transformation of non-performing loans (NPLs) and GDP growth, with a lag response of up to three quarters. The results also indicate that the procyclical behavior of loan quality varies across credit types. This is novel in the literature and suggests that banks with larger exposures to highly procyclical credit types and economic sectors would tend to undergo sharper deterioration in the quality of their credit portfolios during an economic downturn. Lack of sufficient portfolio granularity in macro stress testing fails to capture these effects and thus introduces a source of bias that tends to underestimate the tail losses stemming from the riskier banks in a system.

Size matters in banking. In this paper, we explore whether shocks originating at large banks affect the probability of distress of smaller banks and thus the stability of the banking system. Our analysis proceeds in two steps. In a first step, we follow Gabaix [Gabaix, X., 2008a. The Granular Origins of Aggregate Fluctuations. Available at SSRN: http://ssrn.com/abstract=1111765] and construct a measure of idiosyncratic shocks at large banks, the so-called Banking Granular Residual. This measure documents the importance of size effects for the German banking system. In a second step, we incorporate this measure of idiosyncratic shocks at large banks into an integrated stress-testing model for the German banking system following De Graeve et al. (2008). We find that positive shocks at large banks reduce the probability of distress of small banks.

Machine learning (ML) could strengthen banks’ resilience through improved credit risk screening and ultimately benefit financial stability. Yet, ML adoption in banking remains limited, with simpler linear models still predominating. We argue that the emergence of highly flexible ML models has created a new challenge for forecasting tasks: ‘model multiplicity’—where equally accurate ML models at the aggregate level produce divergent individual-level predictions (‘predictive multiplicity’) or differ in their decision surfaces (‘procedural multiplicity’). These issues raise fundamental questions: Why should an individual or firm be subject to an adverse credit risk model outcome when there is an equally accurate model that treats them more favorably? Using the world’s largest loss database of corporate defaults, we examine these two phenomena in recovery rate ( RR ) modeling and propose heterogeneous ML ensembles as a natural solution. By combining predictions and decision surfaces from multiple well-performing ML models, ensembles mitigate risks associated with predictive multiplicity by ensuring that borrowers are not subject to the fluctuations of a single model, and reduce procedural multiplicity by providing a robust measure of features that ultimately improve out-of-sample RR predictions. By addressing the ‘multiplicity of good models’ problem, our study emphasizes the importance of model stability and provides new insights for the future development of ML models.

This study constructs a financial stress index for Bulgaria, the Czech Republic, Hungary, Poland, and Russia and examines the relationship between financial stress and economic activity. The financial stress index incorporates banking sector fragility, time varying stock market return volatility, sovereign debt spreads, an exchange market pressure index, and trade credit. These variables seem to capture key aspects of financial stress in sample countries as the index peaks at known financial crises in these countries. We then examine the relationship between financial stress and economic activity. Impulse response functions based on bivariate VARs show a significant relationship between financial stress and some measures of economic activity. Overall, the constructed financial stress index provides valuable information on the state of the economy and economic activity.

This article reviews empirical studies on Islamic banking and concentrates on their main findings while highlighting future research directions. The earlier literature on Islamic banking built a foundation using normative judgment, descriptive analysis, theoretical development, and appraisal of country experiences. The paper discusses scholars’ concerns that have led to a paradigm shift in the system and highlight practitioners’ disquiet about recent practices. Subsequent research focuses on empirical investigations without extensive analytical and theoretical exploration in the area. Recent studies focus on the financial crisis, solvency, maqasid, disclosure and financial inclusion, and regulations. Even with the spillover effect on the Islamic banks after the crisis, a few pieces of evidence show that the system performs below its conventional counterpart. The paper discusses issues that are relevant to Islamic banking and identifies other avenues for future research.

Regulatory forbearance in times of corporate distress has been a common practice in many countries to achieve bank stability, particularly so in the absence of a unified bankruptcy code, yet very little is known in the context of emerging market economies. Exploiting variation of membership across banks in a corporate debt restructuring programme (CDR) sponsored by the central bank in India, this paper finds that the banks that made use of regulatory forbearance (RF) on the restructured corporate loans could increase their stability significantly due to the extension of low provisioning on restructured loans. However, the positive effect of RF diminishes at higher levels of market power, highlighting that member banks with higher market power tend to originate riskier assets (as reflected in their risk-weighted assets) under the auspices of this programme. Our results remain robust to different estimators (including propensity score matching), ownership structure, and alternative measures of bank stability.

Using dynamic conditional correlations and network theory, this study brings a novel interdisciplinary framework to define the integration and segmentation of emerging countries. The individual EMBI+ spreads of 13 emerging countries from January 2003 to December 2013 are used to compare their interaction structure before (phase 1) and after (phase 2) the global financial crisis. Accordingly, the unweighted average of dynamic conditional correlations between cross country bond returns significantly increases in phase 2. At first glance, the increased co-movement degree suggests an integration of the sample countries after the crisis. However, using correlation based stable networks, we show that this is not enough to make such a strong conclusion. In particular, we reveal that the increased average correlation is more likely to be caused by clusters of countries that exhibit high within-cluster co-movement but not between-cluster co-movement. Potential reasons for the post-crisis segmentation and important implications for international investors and policymakers are discussed.

We analyze the relationship between the stance of monetary policy and the implicit risk aversion in European Stock market prices in an international open-economy framework. We use a structural vector autoregression (SVAR) model that incorporates the effect of a factor that reflects the global monetary policy stance. We use shocks in the US Fed monetary policy stance as a proxy of this global factor. Our results indicate mixed evidence depending on whether simultaneity between domestic monetary policy stance and the stock market behavior is taken into full account. When this simultaneity is not allowed we confirm previous evidence found in the literature, extended to the international field: a lax monetary policy, both domestic and global, decreases risk aversion. However, when we take this into account, results indicate that a lax monetary policy increase in the short-run the risk aversion of the domestic representative investor.

We employ a theoretical model to interpret the liquidity and moral hazard effects of IMF support during a financial crisis. We then estimate the response of forward exchange markets to IMF-related announcements, using data on the 3-, 9-, and 12-month forward exchange rates. Our results indicate that the announcement of IMF negotiations is associated with a premium on the baht and the rupiah, where the premium is much larger on the latter. This result is largely consistent with the responses of stock and bond markets, especially when country-specific data are employed.