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.
Highlights
Due to increasing fragility in financial markets and the extensive use of derivative products, effective management of financial risks has become ever more important
In this paper we focus on the latter, the quantile regression with the high frequency information incorporated
Motivated by this subsampling approach, which is shown to outperform those using directly the highest frequency series, and to avoid arbitrariness in choosing sampling frequencies, we propose a subsample averaging quantile forecast similar in construction to the bagging approach and to the simple average combination of forecasts
Summary
Due to increasing fragility in financial markets and the extensive use of derivative products, effective management of financial risks has become ever more important. Reference [15] shows that subsampling is highly advantageous for RV estimators based on discontinuous kernels Motivated by this subsampling approach, which is shown to outperform those using directly the highest frequency series, and to avoid arbitrariness in choosing sampling frequencies, we propose a subsample averaging quantile forecast similar in construction to the bagging approach and to the simple average combination of forecasts. We deem that proper use of high-frequency intraday data should be helpful for achieving a more accurate estimation of volatility, but should be beneficial for forecasting daily VaR/quantiles. We proceed by considering these two alternate approaches of using intraday information and compare the CF approaches (combination of individual forecasts obtained from using one 5-minute intraday information block at a time) with the CI approaches (combination of all 5-minute intraday information blocks into one model) for their forecasting ability for daily market return VaR/quantiles prediction. Rt is the daily return based on this particular subsample, which is the conventional daily close return
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
