Do we need the constant term in the heterogenous autoregressive model for forecasting realized volatilities?

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Abstract

No-constant strategy is considered for the heterogenous autoregressive (HAR) model of Corsi, which is motivated by smaller biases of its estimated HAR coefficients than those of the constant HAR model. The no-constant model produces better forecasts than the constant model for four real datasets of the realized volatilities (RVs) of some major assets. Robustness of forecast improvement is verified for other functions of realized variance and log RV and for the extended datasets of all 20 RVs of Oxford-Man realized library. A Monte Carlo simulation also reveals improved forecasts for some historic HAR model estimated by Corsi.

Original languageEnglish
Pages (from-to)63-73
Number of pages11
JournalCommunications in Statistics: Simulation and Computation
Volume47
Issue number1
DOIs
StatePublished - 2 Jan 2018

Bibliographical note

Publisher Copyright:
© 2018 Taylor & Francis Group, LLC.

Keywords

  • Bias
  • HAR model
  • Long-memory
  • Realized volatility
  • Volatility forecasting

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