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Forecasting risk measures using intraday data in a generalized autoregressive score (GAS) framework

Lazar, E. and Xue, X. (2019) Forecasting risk measures using intraday data in a generalized autoregressive score (GAS) framework. International Journal of Forecasting. ISSN 0169-2070 (In Press)

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To link to this item DOI: 10.1016/j.ijforecast.2019.10.007

Abstract/Summary

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 volatility at 5-min and 10-min sampling frequencies, and the overnight return incorporated into these two realized volatilities. 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 volatility measures, outperform the benchmark models consistently across all indices and various probability levels.

Item Type:Article
Refereed:Yes
Divisions:Henley Business School > ICMA Centre
ID Code:86861
Publisher:Elsevier

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