with Simon Fritzsch and Maike Timphus.
Copulas. We study the model risk of multivariate risk models using a comprehensive empirical study on Copula-GARCH models used for forecasting Value-at-Risk and Expected Shortfall. To determine whether model risk inherent in the forecasting of portfolio risk is caused by the candidate marginal or copula models, we analyze different groups of models in which we fix either the marginals, the copula, or neither. Model risk is economically significant, is especially high during periods of crisis, and is almost completely due to the choice of the copula. We then propose the use of the model confidence set procedure to narrow down the set of available models and reduce model risk for Copula-GARCH risk models. Our proposed approach leads to a significant improvement in the mean absolute deviation of one day ahead forecasts by our various candidate risk models.
The Impact of Corporate Social Responsibility on Firms' Exposure to Tail Risk: The Case of Insurers, with David Sonnenberger
We propose a novel corporate social responsibility (CSR) index that captures various aspects of an insurer's internal and external CSR activities. We first show that insurers worldwide have significantly increased their CSR activities with the average index value almost doubling between 2006 and 2015. CSR activities are particularly pronounced at large firms, composite insurers, and insurance companies in Europe. We find that insurers' exposure to market risk in previous times significantly drives future CSR engagement. Finally, we provide empirical evidence for a causal and decreasing effect of an insurer's CSR on its tail risk as well as its short- and medium-term exposure to systemic risk.
Journal of Financial and Quantitative Analysis, accepted.
We use the EBA capital exercise of 2011 as a quasi-natural experiment to investigate how capital requirements affect various measures of bank solvency risk. We show that, while regulatory measures of solvency improve, non-regulatory measures indicate a deterioration in bank solvency in response to higher capital requirements. The decline in bank solvency is driven by a permanent reduction in banks' market value of equity. This finding is consistent with a reduction in bank profitability, rather than a repricing of bank equity due to a reduction of implicit and explicit too-big-too-fail guarantees. We then discuss alternative policies to improve bank solvency.
We study the effects of innovations by banks on local deposit inflows and credit supply. To identify the causal effect of bank innovations on deposits and lending, we exploit two distinct instrument variables to explain banks’ patent approvals: the geographic heterogeneity in human capital available to bank headquarters, as well as the leniency of patent examiners. Banks that innovate experience deposit inflows, increase their local market power, and expand aggregate local lending without impairing the quality of their loan portfolio. Finally, we show that the innovation-induced credit supply shock spurs local economic growth and employment.
Portfolio sorts and cross-sectional regressions are standard tools to test the pricing of asset characteristics. We propose the alternative use of non-parametric machine learning methods to estimate quantile curves of the characteristic of interest conditional on a set of controls. Building portfolios based on conditional quantile curves yields characteristic portfolios that should only reflect the priced risk associated with the characteristic. We apply our procedure to the pricing of volatility risk in the cross-section of option returns. The Sharpe ratio of the resultant characteristic portfolios are up to 30% higher than those of comparable strategies.