Forecasting daily volatility with intraday data

The aim of this paper is to assess to what extent intraday data can explain and predict end-of-the-day volatility. Using a realized volatility measure as proposed by Andersen, T., T. Bollerslev, F. Diebold, and P. Labys. 2001. The distribution of realized exchange rate volatility. Journal of the American Statistical Association 96: 42-55, we hypothesize that volatility generated at the start of the day is an important predictor of daily volatility either on its own accord or in conjunction with information about the seasonal pattern characterizing intraday volatility. We address the question of how much information needs to arrive to the market before a good predictor can be formed. Using data from a specialist market (NYSE), a dealer market (Nasdaq) and a continuous auction market (Paris Bourse), we investigate how different trading structures may affect intraday volatility formation. As a preview to our results, we find that the explanatory power of first-hour volatility for daily volatility is as high as 68%, whereas the average volatility generated during this first hour is <30%. Comparison to a standard GARCH model shows that the forecasts based on the intraday data are generally highly informative both on their own accord and in combination with the GARCH forecasts.