Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures

• 成果类型: Article
• 署名作者: Banulescu-Radu, Denisa; Hurlin, Christophe; Leymarie, Jeremy; Scaillet, Olivier
• 署名单位: Universite de Orleans; Universite Catholique de Lille; EDHEC Business School; University of Geneva; University of Geneva; Swiss Finance Institute (SFI)
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3751
• 发表日期: 2021
• 页码: 5730-5754
• 关键词: backtesting; banking regulation; Hypothesis Testing; risk management; Systemic risk

摘要

This paper proposes an original approach for backtesting systemic risk measures. This backtesting approach makes it possible to assess the systemic risk measure forecasts used to identify the financial institutions that contribute the most to the overall risk in the financial system. Our procedure is based on simple tests similar to those generally used to backtest the standard market risk measures such as value-at-risk or expected shortfall. We introduce a concept of violation associated with the marginal expected shortfall (MES), and we define unconditional coverage and independence tests for these violations. We can generalize these tests to any MES-based systemic risk measures such as the systemic expected shortfall (SES), the systemic risk measure (SRISK), or the delta conditional value-at-risk (Delta CoVaR). We study their asymptotic properties in the presence of estimation risk and investigate their finite sample performance via Monte Carlo simulations. An empirical application to a panel of U.S. financial institutions is conducted to assess the validity of MES, SRISK, and Delta CoVaR forecasts issued from a bivariate GARCH model with a dynamic conditional correlation structure. Our results show that this model provides valid forecasts for MES and SRISK when considering a medium-term horizon. Finally, we propose an early warning system indicator for future systemic crises deduced from these backtests. Our indicator quantifies how much is the measurement error issued by a systemic risk forecast at a given point in time which can serve for the early detection of global market reversals.