Forecasting Stock Market Volatility: The Gains from Using Intraday Data

Abstract

There is evidence that volatility forecasting models that use intraday data produce superior forecast accuracy as compared with that delivered by the models that use daily data. However, this evidence is still sparse and incomplete in the stock markets. This paper extends previous studies on forecasting stock market volatility in several important directions and comprehensively assesses the gains in forecast accuracy provided by intraday data. First, we use an extensive set of intraday data on 28 single stocks and 23 stock market indices. Second, in our study we use forecast horizons ranging from 1 day to 6 months. Third, we compare forecasting abilities of several competing models. We find that the amount of gains depends on the length of the forecast horizon, on the forecasting model, and on whether the volatility is forecasted for a single stock or a stock market index. The major finding of our empirical study is that intraday data allow one to increase the forecast accuracy by 10% to 30%. Thus, the gains from using intraday data are rather significant. Surprisingly, we find that the gains in predictive accuracy from intraday data persist over longer forecast horizons and are greater for stock market indices than for single stocks.