TY - JOUR AU - Andersen,Torben G. AU - Bollerslev,Tim AU - Christoffersen,Peter F. AU - Diebold,Francis X. TI - Volatility Forecasting JF - National Bureau of Economic Research Working Paper Series VL - No. 11188 PY - 2005 Y2 - March 2005 UR - http://www.nber.org/papers/w11188 L1 - http://www.nber.org/papers/w11188.pdf N1 - Author contact info: Torben G. Andersen Kellogg School of Management Northwestern University 2001 Sheridan Road Evanston, IL 60208 Tel: 847/467-1285 Fax: 847/491-5719 E-Mail: t-andersen@kellogg.northwestern.edu Tim Bollerslev Department of Economics Duke University Box 90097 Durham, NC 27708-0097 Tel: 919/660-1846 Fax: 919/684-8974 E-Mail: boller@econ.duke.edu Peter Christoffersen Peter Christoffersen Professor of Finance Rotman School of Management University of Toronto 105 St. George Street 447 Toronto, ON, M5S 3E6, Canada Tel: 416-946-5511 E-Mail: peter.christoffersen@rotman.utoronto.ca Francis X. Diebold Department of Economics University of Pennsylvania 3718 Locust Walk Philadelphia, PA 19104-6297 Tel: 215/898-1507 Fax: 212/573-4217 E-Mail: fdiebold@sas.upenn.edu AB - 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. ER -