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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 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.
Full paper (3726 KB PDF)
Keywords
Autoregression, Spectrum, Volatility Forecasting, Wiener-Kolmogorov Filter, High Frequency Data, Exchange Rates
PDF files: Adobe Acrobat Reader ZIP files: PKWARE
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Last update: July 19, 2001
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