Extreme conditional tail risk inference in ARMA–GARCH models

Yaolan Ma and Bo Wei

Journal of Economic Dynamics and Control, 2025, vol. 177, issue C

Abstract: In this study, we investigate the estimation of extreme conditional Value-at-Risk (CVaR) and conditional Expected Shortfall (CES) within the framework of ARMA-GARCH models, where innovations are assumed to follow a Pareto-type tail distribution and have no finite fourth moments. Building on the two-stage self-weighted estimation procedure proposed by He et al. (2022), we develop a robust methodology for forecasting extreme CVaR and CES. Using extreme value theory, we derive a unified asymptotic theory for the extreme CVaR and CES estimators. Through comprehensive simulation studies, we evaluate the performance of our approach and compare it with several recently proposed estimators in the literature. Additionally, we apply our methodology to forecast extreme CVaR and CES for daily negative log-returns (i.e., losses) of four financial assets, demonstrating its practical applicability in financial risk management.

Keywords: ARMA-GARCH models; Conditional value-at-risk; Conditional expected shortfall; Extreme value theory (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165188925000946
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:177:y:2025:i:c:s0165188925000946

DOI: 10.1016/j.jedc.2025.105128

Access Statistics for this article

Journal of Economic Dynamics and Control is currently edited by J. Bullard, C. Chiarella, H. Dawid, C. H. Hommes, P. Klein and C. Otrok

More articles in Journal of Economic Dynamics and Control from Elsevier
Bibliographic data for series maintained by Catherine Liu ().