Journal of Financial Stability最新文献

This study explores the relationship between sovereign credit risk, banking fragility, and global financial factors in a large panel database of emerging market economies. To measure banking fragility, we construct a novel model-based semi-parametric metric (JLoss) that computes the expected joint loss of the banking sector in each country conditional on a country-level systemic event. Our metric of banking fragility is positively associated with sovereign credit spreads, after controlling for the standard determinants of sovereign credit risk, a comprehensive set of measures of systemic risk, and country and time fixed effects. The results additionally indicate that countries with more fragile banking sectors are more exposed to global (exogenous) financial factors than those with more resilient banking sectors. These findings underscore that regulators must ensure the stability of the banking sector to improve governments’ borrowing costs in international debt markets.

How financial investors may react to policy events related to sustainability and climate change mitigation in particular, is a key question with implications for sustainable finance and financial stability. We address this question by carrying out a multi-period difference-in-difference approach on a confidential database of securities holdings of the European Central Bank, and we provide evidence of several effects related to the Paris Agreement. In aggregate, investors reduced their participation in the equities of high-carbon firms in response to the agreement, and the trend reverted after the US’s announcement of withdrawal from the agreement. However, the reaction varies across categories and geographies of the securities holders, their ownership size, and the emissions of owned firms. In particular, transition risk has been taken up by less regulated financial institutions and the BRIC countries. Our results highlight that the redirection of global financial flows towards climate action requires clear and unanimous signals from the global community of policy makers.

This paper shows that higher information technology (IT) adoption by banks was associated to a larger increase in corporate lending in the months following the COVID-19 outbreak in Italy. Examining banks with heterogeneous degrees of IT adoption, we investigate the dynamics of credit and its allocation across firms using a new database with detailed information on banks’ IT expenditures and use of innovative technologies matched with bank-firm level data on credit growth before and during the pandemic. Using a diff-in-diff approach, we find that banks with a higher share of IT spending increased their credit more than others during the pandemic. The increase was concentrated in term loans extended to smaller and financially sounder companies; the effect was stronger in the initial phase of tighter restrictions to firm activity and individual mobility, and more significant for undertakings active in the sectors most affected by the shock. We provide evidence that these results are driven by bank’s ability to offer credit entirely online and bank’s use of artificial intelligence for credit risk assessment. Physical proximity between borrowers and lenders was important for credit provision during the pandemic, but only when combined with high level of IT adoption.

Basel III features requirements on bank capital and liquidity along with disclosure requirements. I study these prudential tools by developing a general equilibrium model with bank runs in a global game framework, where leverage, liquidity, interest rates, and the probability of a banking crisis are all determined endogenously. With timely disclosure about bank assets, the unregulated economy has efficient liquidity but excessive leverage due to a pecuniary externality, warranting a leverage restriction. Delayed disclosure gives rise to bank risk shifting, making leverage even more excessive and liquidity insufficient, which warrants joint requirements on leverage and liquidity. Empirical predictions and policy implications are derived and discussed.

Financial regulation has introduced bail-ins (i.e. enforced debt-to-equity swaps) as a tool for orderly bank resolution, and hence it is the authorities’ task to decide when to apply this tool in a resolution. We present a quantitative framework to support this decision by computing the systemic impact of a bail-in. Our model takes into account systemic feedback effects using state-of-the-art multilayer contagion models, which we extend to include liquidation losses. Using real-world data for the Austrian banking system, we perform an empirical assessment of the systemic risk impact of idiosyncratic and systemic shocks. Our results show that bail-ins have the potential to reduce systemic risk compared to insolvencies for the Austrian banking system. They also incur lower social cost than bail-outs, but only for moderate, idiosyncratic crises. Our findings quantitatively corroborate earlier discussions that bail-ins may be an inadequate tool to deal with systemic crises. This suggests that the bail-in mechanism alone may not be sufficient to rule out future bail-outs.

We analyse the double materiality of climate physical and transition risks in the euro area economy and banking sector. First, by tailoring the EIRIN Stock-Flow Consistent behavioural model, we provide a dynamic balance sheet assessment of the Network for Greening the Financial System (NGFS) scenarios. We find that an orderly transition achieves early co-benefits by reducing CO₂ emissions (12% less in 2040 than in 2020) while supporting growth in economic output. In contrast, a disorderly transition worsens the economic performance and financial stability of the euro area. Further, in a disorderly transition with higher physical risks, real GDP decreases by 12.5% in 2050 relative to an orderly transition. Second, we analyse how firms’ expectations about climate policy credibility (climate sentiments) affect investment decisions in high or low-carbon goods. Firms that trust an orderly policy introduction do anticipate the carbon tax and switch earlier to low-carbon investments. This, in turn, accelerates economic decarbonization and decreases the risk of carbon-stranded assets for investors. Our results highlight the crucial role of early and credible climate policies to signal investment decisions in the low-carbon transition.

This paper shows how the combined endogenous reaction of banks and investment funds to an exogenous shock can amplify or dampen losses to the financial system compared to results from single-sector stress testing models. We build a new model of contagion propagation using a very large and granular data set for the euro area. Based on the economic shock caused by the Covid-19 outbreak, we model three sources of exogenous shocks: a default shock, a market shock and a redemption shock. Our contagion mechanism operates through a dual channel of liquidity and solvency risk. Our analysis reveals that adding the fund sector to our model for banks leads to additional losses through fire sales and a further depletion of banks’ capital ratios by around one percentage point. The main driver of additional bank losses are endogenous market losses generated by investment funds’ asset liquidation.

In this paper we provide a framework to study the network topology of generalized forecast error variance decomposition (GFEVD) derived from multi-country, multi-variable time series models. Our dynamic variance decomposition network is based on a Bayesian Global Vector Autoregressive (GVAR) model, a suitable macroeconometric method to consider simultaneous multi-level interdependencies across variables. We demonstrate the usefulness of our methodology to analyze the network structure of shock propagation in longitudinal time series and, in particular: (a) the shortest paths of contagion; (b) the clusters of shock transmission; (c) the role of nodes in the risk transmission channels. We illustrate our method through an empirical application to a set of 12 European countries’ Industrial Production, Retail Trade and Economic Sentiment indices over the period 01/2000–11/2021.

This paper explores the relationship between environmental, social and governance (ESG) information and systemic risk, an increasingly important issue for both regulators and investors. While ESG ratings are widely used to assess a company’s non-financial performance, the impact of these factors on financial stability and systemic risk is still under debate. By extending the Forecast Error Variance Decomposition (FEVD) method with a double regularization on both the underlying vector autoregressive (VAR) parameters and the covariance matrix of the VAR residuals, we are able to address the curse of dimensionality within each estimation. This allows us to examine how vulnerable a company is and how much systemic impact a company has given its specific ESG. Looking at a larger sample of European stocks over the period 2007–2022, we empirically show that both the best and worst ESG performers have the largest impact on the financial system in normal times. However, during a crisis, companies with the best ESG ratings generate significant spillovers throughout the system. These findings highlight the importance of incorporating ESG factors into systemic risk assessments and monitoring companies’ ESG performance to ensure financial stability. Policymakers can benefit from this research by supporting investment in high ESG companies to mitigate relevant spillovers during stressed market conditions, when such companies are more interconnected.