Journal of Financial Stability最新文献

In the midst of the unprecedented COVID-19 pandemic crisis, the scope of the current study is to outline the channels of shock propagation across sovereigns under these unprecedent conditions. We use a sample of European countries for a period of twelve years that encompasses the COVID-19 as well as the turbulent period of the European debt crisis. We apply Bayesian Vector Autoregressive techniques to show a dramatic increase in sovereign contagion during the outbreak of the COVID-19 pandemic, even higher than the increase recorded during the European Debt crisis. The result works through government response and containment measures. Extensive and severe detachment from any financial fundamentals is evident. The announcements of fiscal and monetary easing measures have eliminated the tension in the markets. When focusing on the period of the pandemic the impact of the national culture emerges through the channel of collectivism.

In this paper, we use knowledge graph (KG) to study systemic risk in the banking industry. KG provides a graphic representation of the connections of entities of interest (known as vertices or nodes) with the strengths of connections being reflected by the lines connecting them (known as edges) or distances between them. As a result, KG is a natural tool for visualizing the relationships among financial institutions. Furthermore, various data and graph choices can present how differently entities of interest can be connected. In this paper, we draw KGs on two datasets: liquidity index and volatility and three different embedding methods: locally linear embedding, spectral embedding and principal component analysis. Our empirical results show, not surprisingly, that volatility and liquidity index are not similar in explaining how banks are connected. Embedding methods also matter.

We examine the direct and indirect impacts of natural disasters on deposit rates of U.S. bank branches from 2008 to 2017. We capture the indirect impact by the spatial spillover effects of disasters, from branches directly exposed to such disasters to neighboring branches. We theoretically motivate our spatial framework by local competition for deposits among branches and provide empirical evidence consistent with this model. We find that indirect effects contribute to at least two-thirds of the total impact for deposit rate-setting branches. Rate-setting branches in affected counties, on average, raise their deposit rates on 12-month CDs by 1.5 basis points directly due to the disaster shock. However, there is an additional indirect increase of 2.7 – 4.3 basis points for all rate-setting branches, including those in adjacent but unaffected counties, due to the local geographical competition for deposits. We also confirm that the spillover effect occurs among branches across counties via an overlooked social connectedness. Moreover, and importantly, online and one-county banks are more likely to rely on the information channel embedded in the social connectedness effect in response to natural disasters. Branches in less concentrated local markets also respond more to the nature disaster and rate adjustments of neighboring branches.

Climate-related risks have become a major concern for financial regulators and can pose a significant threat to financial stability. In this paper, we first propose a theoretical framework for the transmission of climate risks to financial institutions and the financial system. We then estimate the influence of physical and transition risks on the European financial system through bank-level and system-wide measures of financial stability. We find that Scope 3 greenhouse gas emissions, chronic and acute climate risks negatively affect financial stability at both the financial institution and system levels. Temperature anomalies, heat waves, wildfires and droughts are among the most significant risks. As Europe warms twice as fast as the rest of the world, our theoretical and empirical results urge regulators to mandatorily require the assessment and disclosure of corporate climate risks to allow banks to adjust their prudential capital requirements.

Assume data on Nj stock (asset) returns are available for p stocks, allowing us to construct approximate density functions $f\text{(}{x}_j$) for (j=1, 2, …, p) from p empirical cumulative distribution functions (ECDFs). Our portfolio choice is designed to rank ECDF-induced, ill-behaved $f\text{(}{x}_j$) densities subject to multiple modes, asymmetric fat tails, dips, turns, and numerous overlaps. Older portfolio theory assumes that parameters like the mean, variance, and percentiles fully describe $f\text{(}{x}_j$). All six of our algorithms avoid (expected) utility theory. The only available algorithm by Anderson for order-k Stochastic Dominance (SDk) needs a trapezoidal approximation. Our new exact algorithm for SDk is based on ECDFs and overcomes pairwise comparisons. We include algorithms for statistical inference using the bootstrap and one for “pandemic proof” out-of-sample portfolio performance comparisons from our R package ‘generalCorr’. We suggest a test for “zero cost profitable arbitrage” and illustrate our algorithms in action by using two sets of recent 169-month stock returns. We do not claim to suggest new optimal portfolios.

We find strong evidence that measures of social responsibility contribute to increasing the resilience of banks. This finding holds when social responsibility is measured by aggregated ESG scores provided by Thomson Reuters, both according to their older Asset 4 categorization and to the reformed ESG Refinitiv classification, and resilience is proxied by various measures of systemic and systematic risk. The results hold on the level of subcategories of the ESG pillars, where we find that, particularly, variables related to the long-term perspective enhance resilience. Moreover, in our international study, we find significant transatlantic differences.