Modeling and forecasting realized volatility

Published

Journal Article

We provide a framework for Integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency return volatilities and return distributions. 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, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution 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.

Full Text

Duke Authors

Cited Authors

  • Andersen, TG; Bollerslev, T; Diebold, FX; Labys, P

Published Date

  • January 1, 2003

Published In

Volume / Issue

  • 71 / 2

Start / End Page

  • 579 - 625

International Standard Serial Number (ISSN)

  • 0012-9682

Digital Object Identifier (DOI)

  • 10.1111/1468-0262.00418

Citation Source

  • Scopus