Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities

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We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.








Tim Bollerslev

Juanita and Clifton Kreps Distinguished Professor of Economics, in Trinity College of Arts and Sciences

Professor Bollerslev conducts research in the areas of time-series econometrics, financial econometrics, and empirical asset pricing finance. He is particularly well known for his developments of econometric models and procedures for analyzing and forecasting financial market volatility. Much of Bollerslev’s recent research has focused on the analysis of newly available high-frequency intraday, or tick-by-tick, financial data and so-called realized volatility measures, macroeconomic news announcement effects, and the pricing of volatility risk. Recent reviews of his work are available in the two Handbook chapters "Volatility and Correlation Forecasting” (with Torben G. Andersen, Peter Christoffersen and Francis X. Diebold), Handbook of Economic Forecasting, (eds. Graham Elliott, Clive W.J. Granger and Allan Timmermann), 2006, and "Parametric and Nonparametric Volatility Measurement” (with Torben G. Andersen and Francis X. Diebold), in Handbook of Financial Econometrics, (eds. Yacine Aït-Sahalia and Lars P. Hansen), 2009.

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