Efficiently Backtesting Conditional Value-at-Risk and Conditional Expected Shortfall

• 成果类型: Article
• 署名作者: Su, Qihui; Qin, Zhongling; Peng, Liang; Qin, Gengsheng
• 署名单位: Jilin University; Auburn University System; Auburn University; University System of Georgia; Georgia State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1763804
• 发表日期: 2021
• 页码: 2041-2052
• 关键词: maximum-likelihood-estimation; Empirical Likelihood; estimators; GARCH

摘要

Given the importance of backtesting risk models and forecasts for financial institutions and regulators, we develop an efficient empirical likelihood backtest for either conditional value-at-risk or conditional expected shortfall when the given risk variable is modeled by an ARMA-GARCH process. Using a two-step procedure, the proposed backtests require less finite moments than existing backtests, allowing for robustness to heavier tails. Furthermore, we add a constraint on the goodness of fit of the error distribution to provide more accurate risk forecasts and improved test power. A simulation study confirms the good finite sample performance of the new backtests, and empirical analyses demonstrate the usefulness of these efficient backtests for monitoring financial crises.