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

See all articles by Roel C. A. Oomen

Roel C. A. Oomen

Deutsche Bank AG (London); London School of Economics & Political Science (LSE) - Department of Statistics

Date Written: April 2001

Abstract

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

Oomen, Roel C.A., Using High Frequency Stock Market Index Data to Calculate, Model & Forecast Realized Return Variance (April 2001). European Univ., Economics Discussion Paper No. 2001/6. Available at SSRN: https://ssrn.com/abstract=267498 or http://dx.doi.org/10.2139/ssrn.267498

Roel C.A. Oomen (Contact Author)

Deutsche Bank AG (London) ( email )

Winchester House
1 Great Winchester Street
London, EC2N 2DB
United Kingdom

London School of Economics & Political Science (LSE) - Department of Statistics ( email )

Houghton Street
London, England WC2A 2AE
United Kingdom

Register to save articles to
your library

Register

Paper statistics

Downloads
1,181
Abstract Views
4,402
rank
16,873
PlumX Metrics