TY - JOUR AU - Andersen,Torben G. AU - Bollerslev,Tim AU - Diebold,Francis X. AU - Ebens,Heiko TI - The Distribution of Stock Return Volatility JF - National Bureau of Economic Research Working Paper Series VL - No. 7933 PY - 2000 Y2 - October 2000 UR - http://www.nber.org/papers/w7933 L1 - http://www.nber.org/papers/w7933.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 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 Heiko Ebens Dept. of Economics Johns Hopkins University Baltimore, MD 21218 E-Mail: ebens@jhu.edu AB - We exploit direct model-free measures of daily equity return volatility and correlation obtained from high-frequency intraday transaction prices on individual stocks in the Dow Jones Industrial Average over a five-year period to confirm, solidify and extend existing characterizations of stock return volatility and correlation. We find that the unconditional distributions of the variances and covariances for all thirty stocks are leptokurtic and highly skewed to the right, while the logarithmic standard deviations and correlations all appear approximately Gaussian. Moreover, the distributions of the returns scaled by the realized standard deviations are also Gaussian. Consistent with our documentation of remarkably precise scaling laws under temporal aggregation, the realized logarithmic standard deviations and correlations all show strong temporal dependence and appear to be well described by long-memory processes. Positive returns have less impact on future variances and correlations than negative returns of the same absolute magnitude, although the economic importance of this asymmetry is minor. Finally, there is strong evidence that equity volatilities and correlations move together, possibly reducing the benefits to portfolio diversification when the market is most volatile. Our findings are broadly consistent with a latent volatility fact or structure, and they set the stage for improved high-dimensional volatility modeling and out-of-sample forecasting, which in turn hold promise for the development of better decision making in practical situations of risk management, portfolio allocation, and asset pricing. ER -