High Frequency Data, Frequency Domain Inference and Volatility Forecasting

IFDS Working Paper No. 649

27 Pages Posted: 10 Jul 2000

See all articles by Tim Bollerslev

Tim Bollerslev

Duke University - Finance; Duke University - Department of Economics; National Bureau of Economic Research (NBER)

Jonathan H. Wright

Johns Hopkins University - Department of Economics

Date Written: October 1999

Abstract

While it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high frequency data. The method avoids using a tight parametric model, by instead simply fitting a long autoregression to log-squared, squared or absolute high frequency returns. This can either be estimated either by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise. Wiener-Kolmogorov Filter, high frequency data, exchange rates

Keywords: Autoregression, spectrum, volatility forecasting,

JEL Classification: C22, F31

Suggested Citation

Bollerslev, Tim and Wright, Jonathan H., High Frequency Data, Frequency Domain Inference and Volatility Forecasting (October 1999). IFDS Working Paper No. 649. Available at SSRN: https://ssrn.com/abstract=231830 or http://dx.doi.org/10.2139/ssrn.231830

Tim Bollerslev (Contact Author)

Duke University - Finance ( email )

Durham, NC 27708-0120
United States
919-660-1846 (Phone)
919-684-8974 (Fax)

Duke University - Department of Economics

213 Social Sciences Building
Box 90097
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National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
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Jonathan H. Wright

Johns Hopkins University - Department of Economics ( email )

3400 Charles Street
Baltimore, MD 21218-2685
United States

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