Robust Measurement of Beta Risk

Josef Lakonishok, Louis K. Chan

Research output: Contribution to journalArticlepeer-review


Many empirical studies find that the distribution of stock returns departs from normality. In such cases, it is desirable to employ a statistical estimation procedure that may be more efficient than ordinary least squares. This paper describes various robust methods, which have attracted increasing attention in the statistical literature, in the context of estimating beta risk. The empirical analysis documents the potential efficiency gains from using robust methods as an alternative to ordinary least squares, based on both simulated and actual returns data.

Original languageEnglish (US)
Pages (from-to)265-282
Number of pages18
JournalJournal of Financial and Quantitative Analysis
Issue number2
StatePublished - Jun 1992

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics


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