Testing for predictability in conditionally heteroskedastic stock returns

Joakim Westerlund, Paresh Narayan

Research output: Contribution to journalArticleResearchpeer-review

135 Citations (Scopus)

Abstract

The difficulty of predicting stock returns has recently motivated researchers to start looking for more powerful tests, and the current study takes a step in this direction. Unlike existing tests, the test proposed here exploits the information contained in the heteroskedasticity of findings, which is expected to lead to higher power, a result that is confirmed by our results. In order to also maintain good size accuracy, subsample critical values are used.

Original languageEnglish
Article numbernbu001
Pages (from-to)342-375
Number of pages34
JournalJournal of Financial Econometrics
Volume13
Issue number2
DOIs
Publication statusPublished - Mar 2015
Externally publishedYes

Keywords

  • Conditional heteroskedasticity
  • FQGLS
  • Predictability
  • Stock returns
  • Subsampling

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