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Volatility Forecasting

  • Torben G. Andersen


    (Kellogg School of Management, Northwestern University)

  • Tim Bollerslev


    (Department of Economics, Duke University)

  • Peter F. Christoffersen


    (Faculty of Management, McGill University)

  • Francis X. Diebold


    (Department of Economics, University of Pennsylvania)

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3,4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.

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Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 05-011.

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Length: 111 pages
Date of creation: 22 Feb 2005
Date of revision:
Handle: RePEc:pen:papers:05-011
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