In a neo-canonical monetary policy model, targeting of nominal GDP in terms of growth rates (not growing levels) is analytically equivalent to adoption of a policy that is optimal from a “timeless perspective,” in the sense developed by Woodford and widely utilized in recent monetary policy analysis.

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.

Evidence on central banks’ twin objective, monetary and financial stability, is scarce. We suggest an integrated micro–macro approach with two core virtues. First, we measure financial stability directly at the bank level as the probability of distress. Second, we integrate a microeconomic hazard model for bank distress and a standard macroeconomic model. The advantage of this approach is to incorporate micro information, to allow for non-linearities and to permit general feedback effects between financial distress and the real economy. We base the analysis on German bank and macro data between 1995 and 2004. Our results confirm the existence of a trade-off between monetary and financial stability. An unexpected tightening of monetary policy increases the probability of distress. This effect disappears when neglecting microeffects and non-linearities, underlining their importance. Distress responses are largest for small cooperative banks, weak distress events, and at times when capitalization is low. An important policy implication is that the separation of financial supervision and monetary policy requires close collaboration among members in the European System of Central Banks and national bank supervisors.

Using VAR and Local Projections models, enhanced with macroeconomic factors and monetary policy shocks, we investigate the underlying mechanisms through which the Fed’s and ECB’s react to bank reactions of geopolitical risks between January 1994 and March 2024. Our findings reveal that central banks react to geopolitical risk events by tightening monetary policy to fend off potential inflationary pressures. However, the effect is often temporary, as policymakers typically adopt accommodative measures during economic expansions and shift to tighter policies during contractions. Analyzing reactions based on central bank presidents' tenures, we find that while earlier responses were limited, in recent years, both central banks have reacted more strongly and immediately, reflecting their growing concern over geopolitical risks. Furthermore, we document that the Fed adopted a more accommodative stance in response to bilateral geopolitical risk shocks between the US and China, driven by changes in capital flows and trade activities. In contrast, the ECB’s responses were more consistently contractionary, particularly in periods of heightened inflation concerns or when geopolitical tensions threatened price stability within the euro area.

Based on the twin sovereign-banking crisis nexus evolution of the Euro debt crisis era, we address the (volatility) mitigation of credit risk, measured by Credit Default Swap spreads (CDS) in both the banking and sovereign sectors within the Eurozone and the US/UK. Secondly, we highlight the volatility interconnectedness or the risk pass-through between sovereign-bank CDS markets with reference to the core vs. periphery EMU. Moreover, we identify the regime states of crises and recovery periods based on the bivariate CDS dynamic correlation series, categorized as the endogenous EMU sovereign risk coherence index. Finally, we investigate the “efficient” (parity) sovereign credit risk pricing during the post-crisis spillover period identified by the CDS and bond markets. We find heterogeneity between markets in pricing the sovereign risk in the regional tier (core-periphery EMU), emphasized by the absence of long-term association. Cointegration results are country-dependent as well as maturity-dependent. Empirical results reject the “no arbitrage” approach.

Short sellers are routinely blamed for destabilizing stock markets by exacerbating deviations from fundamental values. In response, regulators periodically impose short sale constraints aimed at preventing excessive stock market declines. One explanation is that policy makers regard short sellers as behaving like positive feedback traders. Relying on the theoretical model put forward by Sentana and Wadhwani (1992), which stresses the conditional nature of returns’ persistence, bans on selected financial stocks in six countries during the 2008/2009 global financial crisis are examined. These provide us with a setting to analyze the impact of short sale restrictions on feedback trading. Our findings suggest that, in the majority of markets examined, restrictions of this kind amplify positive feedback trading during periods of high volatility and, hence, contribute to stock market downturns. On balance then, short selling bans do not contribute to enhancing financial stability.

In this paper, we provide empirical evidence on the impact of institutional investors on stock market returns dynamics. The Polish pension system reform in 1999 and the associated increase in institutional ownership due to the investment activities of pension funds are used as a unique institutional characteristic. Performing a Markov-switching-GARCH analysis we find empirical evidence that the increase of institutional ownership has temporarily changed the volatility structure of aggregate stock returns. The results are interpretable in favor of a stabilizing effect on index stock returns induced by institutional investors.

Previous work suggests that the charter value hypothesis is theoretically grounded and empirically supported, but not universally. Accordingly, this paper aims to perform an analysis of the relations between charter value, risk taking, and supervision, taking into account the relations’ complexity. Specifically, using the CAMELS rating system as a general framework for supervision, we study how charter value relates to risk and supervision by means of classification and regression tree analysis. The sample covers the period 2005–2016 and consists of listed banks in countries that were members of the Eurozone when it came into existence, along with Greece. To evaluate the crisis consequences, we also separately analyze four subperiods and countries that required financial aid from third parties and those that did not so, along with large and small banks. Our results reflect the complexity of the relations between charter value, supervision, and risk. Indeed, supervision and charter value seem aligned regarding only some types of risk.