Using High Frequency Stock Market Index Data to Calculate, Model & Forecast Realized Return Variance
European Univ., Economics Discussion Paper No. 2001/6
30 Pages Posted: 1 May 2001
Date Written: April 2001
The objective of this paper is to calculate, model and forecast realized volatility, using high frequency stock market index data. The approach taken differs from the existing literature in several aspects. First, it is shown that the decay of the serial dependence of high frequency returns with the sampling frequency, is consistent with an ARMA process under temporal aggregation. This finding has important implications for the modelling of high frequency returns and the optimal choice of sampling frequency when calculating realized volatility. Second, motivated by the outcome of several test statistics for long memory in realized volatility, it is found that the realized volatility series can be modelled as an ARFIMA process. Significant exogenous regressors include lagged returns and contemporaneous trading volume. Finally, the ARFIMA's forecasting performance is assessed in a simulation study. Although it outperforms representative GARCH models, the simplicity and flexibility of the GARCH may outweigh the modest gain in forecasting performance of the more complex and data intensive ARFIMA model.
Keywords: High Frequency Data, Realized Volatility, Market Microstructure, Temporal Aggregation, Fractional Integration, GARCH
JEL Classification: C51, C52, C53, G12, G13
Suggested Citation: Suggested Citation