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

Emese Lazar, Xiaohan Xue

Research output: Contribution to journalArticlepeer-review

6 Citations (SciVal)

Abstract

A new framework for the joint estimation and forecasting of dynamic value at risk (VaR) and expected shortfall (ES) is proposed by our incorporating intraday information into a generalized autoregressive score (GAS) model introduced by Patton et al., 2019 to estimate risk measures in a quantile regression set-up. 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 are applied to four international stock market indices (S&P 500, Dow Jones Industrial Average, Nikkei 225 and FTSE 100) and are compared with a range of parametric, nonparametric and semiparametric models, including historical simulations, generalized autoregressive conditional heteroscedasticity (GARCH) models 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.
Original languageEnglish
Pages (from-to)1057-1072
Number of pages16
JournalInternational Journal of Forecasting
Volume36
Issue number3
Early online date5 Jun 2020
DOIs
Publication statusPublished - Jul 2020

Keywords

  • Value at risk
  • Expected shortfall
  • Generalized autoregressive score dynamics
  • Realized measures
  • Intraday data
  • Risk forecasting

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