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Modeling and Forecasting Realized Volatility

Torben Andersen (), Tim Bollerslev (), Francis Diebold () and Paul Labys

No 8160, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: This paper provides a general framework for integration of high-frequency intraday data into the measurement forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time series procedures for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we formally develop the links between the conditional covariancematrix and the concept of realized volatility. Next, using continuously recorded observations for the Deutschemark Dollar and Yen / Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably compared to popular daily ARCH and related models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate quantile estimates. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.

JEL-codes: C1 G1 (search for similar items in EconPapers)
Date: 2001-03
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fin, nep-fmk and nep-ifn
Note: AP IFM
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Published as Andersen, Torben G., Tim Bollerslev, Francis X. Diebold and Paul Labys. "Modeling And Forecasting Realized Volatility," Econometrica, 2003, v71(2,Mar), 579-625.

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Journal Article: Modeling and Forecasting Realized Volatility (2003)
Working Paper: Modeling and Forecasting Realized Volatility (2002) Downloads
Working Paper: Modeling and Forecasting Realized Volatility (2001) Downloads
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