Sequential monitoring for changes in dynamic semiparametric risk models

Horváth, L., Lazar, E. ORCID: https://orcid.org/0000-0002-8761-0754, Liu, Z., Wang, S. ORCID: https://orcid.org/0000-0003-2113-5521 and Xue, X. (2025) Sequential monitoring for changes in dynamic semiparametric risk models. Journal of Business & Economic Statistics. ISSN 0735-0015 doi: 10.1080/07350015.2025.2540071 (In Press)

Abstract/Summary

We propose a sequential monitoring scheme to detect changes in dynamic semiparametric risk models that capture Value-at-Risk (VaR) and Expected Shortfall (ES) jointly. The monitoring scheme is based on a gradient--based detector and a boundary function, and a change is detected when the detector crosses the boundary function. We derive the asymptotic limit of the stopping time of detection under the null hypothesis of no change. Monte Carlo simulations show that the proposed test has reasonable size control under the null hypothesis and high power under alternative hypotheses of various change point scenarios in finite samples. Empirical applications based on the S&P 500 index and the GBP/EUR exchange rate illustrate that our proposed test is able to detect change points in real-time.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/123681
Identification Number/DOI	10.1080/07350015.2025.2540071
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting Arts, Humanities and Social Science > School of Politics, Economics and International Relations > Economics
Publisher	Taylor & Francis
Download/View statistics	View download statistics for this item