Exploiting the errors: A simple approach for improved volatility forecasting

Published

Journal Article

© 2015 Elsevier B.V. All rights reserved. We propose a new family of easy-to-implement realized volatility based forecasting models. The models exploit the asymptotic theory for high-frequency realized volatility estimation to improve the accuracy of the forecasts. By allowing the parameters of the models to vary explicitly with the (estimated) degree of measurement error, the models exhibit stronger persistence, and in turn generate more responsive forecasts, when the measurement error is relatively low. Implementing the new class of models for the S&P 500 equity index and the individual constituents of the Dow Jones Industrial Average, we document significant improvements in the accuracy of the resulting forecasts compared to the forecasts from some of the most popular existing models that implicitly ignore the temporal variation in the magnitude of the realized volatility measurement errors.

Full Text

Duke Authors

Cited Authors

  • Bollerslev, T; Patton, AJ; Quaedvlieg, R

Published Date

  • May 1, 2016

Published In

Volume / Issue

  • 192 / 1

Start / End Page

  • 1 - 18

Electronic International Standard Serial Number (EISSN)

  • 1872-6895

International Standard Serial Number (ISSN)

  • 0304-4076

Digital Object Identifier (DOI)

  • 10.1016/j.jeconom.2015.10.007

Citation Source

  • Scopus