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Forecasting Value-at-Risk Using High-Frequency Information

Huiyu Huang and Tae Hwy Lee
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Huiyu Huang: Grantham, Mayo, Van Otterloo and Company LLC, 2150 Shattuck Ave, Suite 900, Berkeley, CA 94704, USA

Econometrics, 2013, vol. 1, issue 1, 1-14

Abstract: in the prediction of quantiles of daily Standard&Poor’s 500 (S&P 500) returns we consider how to 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 intraday 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 direct and indirect cases. We show that in forecasting the daily S&P 500 index return quantile (Value-at-Risk or 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 an 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: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2013
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