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dc.contributor.author Bollerslev, Tim en_US
dc.contributor.author Andersen, T.G. en_US
dc.contributor.author Diebold, F. X. en_US
dc.contributor.author Labys, P. en_US
dc.date.accessioned 2010-03-09T15:26:47Z
dc.date.available 2010-03-09T15:26:47Z
dc.date.issued 2003 en_US
dc.identifier.uri http://hdl.handle.net/10161/1859
dc.description.abstract We provide a general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on potentially restrictive and complicated parametric multivariate ARCH or stochastic volatility models. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time-series methods for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we develop formal links between realized volatility and the conditional covariance matrix. 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 a variety of popular daily ARCH and more complicated high-frequency 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, produces well-calibrated density forecasts of future returns, and correspondingly accurate quantile predictions. 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. en_US
dc.format.extent 979146 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Econometrica en_US
dc.subject Continuous time methods en_US
dc.subject Density forecasting en_US
dc.subject Long memory en_US
dc.subject Quadratic variation en_US
dc.subject Risk management en_US
dc.subject Volatiltiy forecasting en_US
dc.title "Modeling and Forecasting Realized Volatility" en_US
dc.type Journal Article en_US
dc.department Economics

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