Loss function-based change point detection in risk measures

Lazar, E. ORCID: https://orcid.org/0000-0002-8761-0754, Wang, S. ORCID: https://orcid.org/0000-0003-2113-5521 and Xue, X. (2023) Loss function-based change point detection in risk measures. European Journal of Operational Research, 310 (1). pp. 415-431. ISSN 0377-2217 doi: 10.1016/j.ejor.2023.03.033

Abstract/Summary

We propose a new test to detect change points in financial risk measures, based on the cumulative sum (CUSUM) procedure applied to the Wilcoxon statistic within a popular class of loss functions for risk measures. The proposed test efficiently captures change points jointly in two risk measure series: Value-at-Risk (VaR) and Expected Shortfall (ES), estimated by (semi)parametric models. We derive the asymptotic distribution of the proposed statistic and adopt a stationary bootstrapping technique to obtain the p-values of the test statistic. Monte Carlo simulation results show that our proposed test has better size control and higher power than the alternative tests under various change point scenarios. An empirical study of risk measures based on the S&P 500 index illustrates that our proposed test is able to detect change points that are consistent with well-known market events.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/111396
Identification Number/DOI	10.1016/j.ejor.2023.03.033
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting Arts, Humanities and Social Science > School of Politics, Economics and International Relations > Economics
Publisher	Elsevier
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