MODELING AND FORECASTING OF DYNAMIC VAR SERIES BASED ON HIGH FREQUENCY DATA
Dong Ca · Journal of Beijing Normal University · 2014
A method is proposed to predict dynamically the VaR time series.Daily VaR value was calculated differently based on daily data in min of Shanghai Composite Index and Shenzhen Composite Index.Statistic features of VaR time series were given,including stability and long-term memory characteristics.ARMA model and ARFIMA model were then built from VaR time series and the two models were compared to find the best predicted result.Analysis indicated that ARMA model of VaR time series based on delta-normal method had better predictive effect than that based on historical simulation method and Monte Carlo simulation method;Although VaR time series was of long memory,ARMA model of VaR time series demonstrated better predictive result than ARFIMA model.