Forecasting Expected Shortfall and Value-at-Risk with the FZ Loss and Realized Variance Measures

55 Pages Posted: 23 Sep 2019 Last revised: 10 Jan 2022

See all articles by Ray Y. Chou

Ray Y. Chou

Academia Sinica

Tso-Jung Yen

Academia Sinica - Institute of Statistical Science

Yu-Min Yen

Department of International Business, National Chengchi University

Date Written: August 28, 2019

Abstract

Expected shortfall (ES) and value at risk (VaR) are two of the most widely used risk measures in economics and finance. In this paper, we incorporate realized variance measures into structural models for the two risk measures. Our estimation procedure is semiparametric and relies on using a class of consistent loss functions proposed by Fissler and Ziegel (2016). We develop an efficient and stable two-stage method to implement the estimations. We then compare performances of out-of-sample forecasts from the estimated structural models with some existing methods, including several recently proposed novel models. We demonstrate that the proposed structure models with realized variance measures overall deliver superior forecasts of ES and VaR for major stock indices than the considered existing methods. An analysis of model averaging further shows that aggregating information from different methods can improve performances of the forecasts, and information from models with realized variance measures is indispensable for generating a superior model averaging forecast.

Keywords: Expected shortfall, Forecast, Realized variance measure, Semiparametric estimation, Value-at-risk

JEL Classification: C22, C53, C58, G17

Suggested Citation

Chou, Ray Y. and Yen, Tso-Jung and Yen, Yu-Min, Forecasting Expected Shortfall and Value-at-Risk with the FZ Loss and Realized Variance Measures (August 28, 2019). Available at SSRN: https://ssrn.com/abstract=3448882 or http://dx.doi.org/10.2139/ssrn.3448882

Ray Y. Chou

Academia Sinica ( email )

128 Academia Road, Section 2
Nankang
Taipei, 11529
Taiwan

Tso-Jung Yen

Academia Sinica - Institute of Statistical Science ( email )

Nankang
Taipei, 11529
Taiwan

Yu-Min Yen (Contact Author)

Department of International Business, National Chengchi University ( email )

64, Section 2, Zhi-nan Road
Wenshan
Taipei, 116
Taiwan

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