Forecasting Risk Measures Using Intraday Data in a Generalized Autoregressive Score (GAS) Framework

33 Pages Posted: 13 Jun 2019

See all articles by Emese Lazar

Emese Lazar

University of Reading - ICMA Centre

Xiaohan Xue

University of Reading - ICMA Centre

Date Written: May 29, 2019

Abstract

A new framework for the joint estimation and forecasting of dynamic Value-at-Risk (VaR) and Expected Shortfall (ES) is proposed by incorporating intraday information into a generalized autoregressive score (GAS) model, introduced by Patton, Ziegel, and Chen (2019) to estimate risk measures in a quantile regression setup. We consider four intraday measures: the realized variance at 5-min and 10-min sampling frequencies, and the overnight return incorporated into these two realized variances. In a forecasting study, the set of newly proposed semiparametric models is applied to 4 international stock market indices: the S&P 500, the Dow Jones Industrial Average, the NIKKEI 225 and the FTSE 100, and is compared with a range of parametric, nonparametric and semiparametric models including historical simulations, GARCH and the original GAS models. VaR and ES forecasts are backtested individually, and the joint loss function is used for comparisons. Our results show that GAS models, enhanced with the realized variance measures (especially at 5 minutes frequency), outperform the benchmark models consistently across all indices and various probability levels.

Keywords: Value-at-Risk, Expected Shortfall, Generalized Autoregressive Score (GAS) Dynamics, Realized Variance, Intraday Data, Risk Forecasting

JEL Classification: C14, C32, C58, G17, G32

Suggested Citation

Lazar, Emese and Xue, Xiaohan, Forecasting Risk Measures Using Intraday Data in a Generalized Autoregressive Score (GAS) Framework (May 29, 2019). Available at SSRN: https://ssrn.com/abstract=3395888 or http://dx.doi.org/10.2139/ssrn.3395888

Emese Lazar (Contact Author)

University of Reading - ICMA Centre ( email )

Whiteknights Park
P.O. Box 242
Reading RG6 6BA
United Kingdom
+44 (0)1183 786675 (Phone)
+44 (0)1189 314741 (Fax)

Xiaohan Xue

University of Reading - ICMA Centre ( email )

Whiteknights
Reading, Berkshire RG6 6AH
United Kingdom
7440389543 (Phone)
RG2 7EU (Fax)

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