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Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility

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  • Torben G. Andersen

    ()
    (Kellogg School of Management, Northwestern University and NBER)

  • Tim Bollerslev

    ()
    (Department of Economics, Duke University and NBER)

  • Francis X. Diebold

    ()
    (Department of Economics, University of Pennsylvania and NBER)

Abstract

A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from high frequency returns coupled with relatively simple reduced-form time series modeling procedures. Building on recent theoretical results from Barndorff-Nielsen and Shephard (2003c,d) for related bi-power variation measures involving the sum of high-frequency absolute returns, the present paper provides a practical framework for non-parametrically measuring the jump component in realized volatility measurements. Exploiting these ideas for a decade of high-frequency five-minute returns for the DM/$ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find the jump component of the price process to be distinctly less persistent than the continuous sample path component. Explicitly including the jump measure as an additional explanatory variable in an easy-to implement reduced form model for realized volatility results in highly significant jump coefficient estimates at the daily, weekly and quarterly forecast horizons.

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Bibliographic Info

Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 03-025.

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Length: 42 pages
Date of creation: 01 Feb 2003
Date of revision: 01 Sep 2003
Handle: RePEc:pen:papers:03-025

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Keywords: Continuous-time methods; jumps; quadratic variation; realized volatility; bi-power variation; high-frequency data; volatility forecasting; HAR-RV model;

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