Forecasting Value-at-Risk Using High Frequency Information

Tae Hwy Lee and Huiyu Huang ()
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Huiyu Huang: GMO Emerging Markets

No 201409, Working Papers from University of California at Riverside, Department of Economics

Abstract: In prediction of quantiles of daily S&P 500 returns we consider how we use high-frequency 5-minute data. We examine methods that incorporate the high frequency information either indirectly through combining forecasts (using forecasts generated from returns sampled at different intra-day interval) or directly through combining high frequency information into one model. We consider subsample averaging, bootstrap averaging, forecast averaging methods for the indirect case, and factor models with principal component approach for both cases. We show, in forecasting daily S&P 500 index return quantile (VaR is simply the negative of it), using high-frequency information is beneficial, often substantially and particularly so in forecasting downside risk. Our empirical results show that the averaging methods (subsample averaging, bootstrap averaging, forecast averaging), which serve as different ways of forming the ensemble average from using high frequency intraday information, provide excellent forecasting performance compared to using just low frequency daily information.

Keywords: VaR; Quantiles; Subsample averaging; Bootstrap averaging; Forecast combination; High-frequency data. (search for similar items in EconPapers)
JEL-codes: C22 C53 G32 (search for similar items in EconPapers)
Pages: 16 Pages
Date: 2014-09
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mst and nep-rmg
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Published in Econometrics 1(1): 127-140. June 2013.

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https://economics.ucr.edu/repec/ucr/wpaper/201409.pdf First version, 2014 (application/pdf)

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Journal Article: Forecasting Value-at-Risk Using High-Frequency Information (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:201409

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