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Semi-Parametric Forecasting of Realized Volatility

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Author Info

  • Ralf Becker

    (University of Manchester)

  • Adam E. Clements

    (Queensland University of Technology)

  • Stan Hurn

    (Queensland University of Technology)

Abstract

Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.

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Bibliographic Info

Article provided by Berkeley Electronic Press in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 15 (2011)
Issue (Month): 3 ()
Pages: 1
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:bpj:sndecm:v:15:y:2011:i:3:n:1

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Web page: http://www.bepress.com/snde

For corrections or technical questions regarding this item, or to correct its listing, contact: (Nickolas Zeibig-Kichas).

Related research

Keywords: volatility; forecasts; forecast evaluation; model confidence set; semi-parametric;

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