Knowledge that Transforms

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.

We investigate how investors perceive the adoption of the expected-loss model (ELM) for impairment incorporated in IFRS 9. Using a sample of European listed banks covering the period of the standard-setting process of IFRS 9, we examine whether the market perceives the new regulation to increase shareholder wealth. First, we document a positive market reaction to the ELM adoption events. Second, we find that investors perceive that the potential benefits of ELM are more pronounced for larger banks, banks with lower profitability and higher systemic risk, and for those that received a public bailout and with more positively skewed returns. Overall, these results support a “monitoring” channel suggesting that ELM may lead to greater bank transparency and more effective market discipline, fundamental for improving financial stability.

This study explores the effect of an investment system reform (deregulation) on the innovation performance of private firms in China. Using a difference-in-differences approach on the data on patent applications of private firms from 1998 to 2009, this study confirms the positive relationship between investment deregulation and innovation performance. We also find that the positive relationship is moderated by types of innovation, firm size and international orientation, and regional patent promotion policy. The study further finds evidence that investment deregulation stimulates innovation performance through two mechanisms, the escaping competition effect, and the preemptive patenting effect. Moreover, investment deregulation may improve the innovation performance of private firms by encouraging investment in fixed capital.

This paper uses a stylized simulation model to assess the potential impact of climate transition risk on banks' balance sheets in a climate-stress-testing (i.e. short-run) framework. We show that a moderate to high transition risk increases overall bank losses only relatively modestly if the baseline is a stressed macroeconomic scenario. However, even in a benign macroeconomic scenario, if high-carbon assets are at least 13% riskier than comparable assets a fire sale mechanism could amplify an initially contained shock into a systemic crisis, resulting in significant losses for the EU banking sector. We show that transition risks are concentrated, and find that an additional capital buffer of 0.9% risk-weighted assets on average would be sufficient to protect the system.

We examine how climate risk influences analyst forecast accuracy proxied by forecast error and dispersion. Using country-level climate risk estimated with time trends in droughts, we find that analyst forecasts are less accurate for firms in drought-prone countries. This effect of climate risk is stronger when climate risks are denoted in earnings forecasts, and when firms’ home countries have greater reliance on hydroelectric sources in electricity generation, more important agricultural and food industries, and active stances concerning climate change. Overall, our findings suggest noteworthy implications of climate risk on the financial markets via analyst forecast accuracy.

In this study, we examine the relationship between firm-level political risk and stock price crash risk. Using a broad dataset of 4230 U.S. firms, 38,097 firm-year observations from 2002 to 2019, we reveal a positive association between political risk and stock price crash risk. These findings are robust to several model specifications and endogeneity checks. By using the Brexit referendum as a quasi-natural experiment, we provide evidence of a causal relationship between political risk and crash risk. Through channel analysis, we identify that this relationship is mediated via higher idiosyncratic volatility, lower price informativeness, and higher distress risk. We also find that our results are more pronounced in intangible-intensive firms. Interestingly, we show that managers of these firms respond to political risk by engaging in bad news hoarding. Finally, strong (external or internal) corporate governance mechanisms can moderate the positive relationship between political risk and stock price crash risk.

Charge-offs provide critical insights into the risk level of loan portfolios within the banking system, signaling potential systemic risks that could lead to deep recessions. Utilizing consolidated financial statements, we have compiled the net charge-off rate (COR) data from the 10 largest U.S. bank holding companies (BHCs) for disaggregated loans, including business loans, real estate loans, and consumer loans, as well as the average COR for each loan category among these top 10 banks. We propose factor-augmented forecasting models for CORs that incorporate latent common factor estimates, including targeted factors, via an array of data dimensionality reduction methods for a large panel of macroeconomic predictors. Our models have demonstrated superior performance compared with benchmark forecasting models, particularly well for business loan and real estate loan CORs, while predicting consumer loan CORs remains challenging especially at short horizons. Notably, real activity factors improve the out-of-sample predictability over the benchmarks for business loan CORs even when financial sector factors are excluded.

We employ the green revenue factors of firms, used in the computation of the FTSE Russell 1000 Green Revenues index to create corresponding green-adjusted share prices. We compute the firm betas, under both the standard and the green-adjusted share pricing. Our findings suggest that tilting of firm stock returns towards green finance could change temporarily asset pricing views. The Fama-French risk factors display very high correlations between the two settings. Nevertheless, there are some significant differences between standard and green-adjusted betas during periods associated with green activism and positive political decisions of financially supporting the global climate action. • Employ green revenues as measured by FTSE Russell. • Construct green revenues adjusted share prices. • Compare betas for green and standard investors. • Differences in asset pricing for green investors only temporary. • Fama-French factors for green versus standard highly correlated.

This paper quantifies the Too-Systemic-To-Fail (TSTF) paradigm in the Eurozone since the introduction of the Euro through three primary dimensions: Too-Big-To-Fail (TBTF), Too-Interconnected-To-Fail (TITF), and Too-Many-To-Fail (TMTF). We apply prominent systemic risk measures based on public data, including the Granger-causality network (GCN), Delta Conditional Value-at-Risk (ΔCoVaR), Marginal Expected Shortfall (MES), and Systemic Risk Index (SRISK). Financial interconnectedness and systemic risk exposure within the 17-member states of the Eurozone are measured on two levels: (i) identifying which financial sectors (banking, diversified financials, insurance, and real estate) are most exposed to systemic risk in the Eurozone at the union level; and (ii) identifying which member state is most exposed to systemic risk within each financial sector at the country level. We extend the original ΔCoVaR, MES and SRISK models by incorporating the bootstrap Kolmogorov-Smirnov stochastic dominance test to rank institutions based on their exposure to systemic risk formally.

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.