High frequency data, frequency domain inference and volatility forecasting
AbstractWhile 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.
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Bibliographic InfoPaper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 649.
Date of creation: 1999
Date of revision:
Other versions of this item:
- Tim Bollerslev & Jonathan H. Wright, 2001. "High-Frequency Data, Frequency Domain Inference, And Volatility Forecasting," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 596-602, November.
- NEP-ALL-2001-02-08 (All new papers)
- NEP-ECM-2001-02-08 (Econometrics)
- NEP-ETS-2001-02-08 (Econometric Time Series)
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