The benefits of bagging for forecast models of realized volatility

Eric Hillebrand, Marcelo C. Medeiros

Research output: Contribution to journalArticlepeer-review


This article shows that bagging can improve the forecast accuracy of time series models for realized volatility. We consider 23 stocks from the Dow Jones Industrial Average over the sample period 1995 to 2005 and employ two different forecast models, a log-linear specification in the spirit of the heterogeneous autoregressive model and a nonlinear specification with logistic transitions. Both forecast model types benefit from bagging, in particular in the 1990s part of our sample. The log-linear specification shows larger improvements than the nonlinear model. Bagging the log-linear model yields the highest forecast accuracy on our sample.

Original languageEnglish (US)
Pages (from-to)571-593
Number of pages23
JournalEconometric Reviews
Issue number5
StatePublished - 2010
Externally publishedYes


  • Bagging
  • Boostrap
  • HAR
  • Realized volatility

ASJC Scopus subject areas

  • Economics and Econometrics


Dive into the research topics of 'The benefits of bagging for forecast models of realized volatility'. Together they form a unique fingerprint.

Cite this