Evidence of an asymmetry in the relationship between volatility and autocorrelation

Michael D. McKenzie, Suk Joong Kim*

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

This paper focuses on the general determinants of autocorrelation and the relationship between autocorrelation and volatility in particular. Using UK stock market index and individual stock price data, a multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) model is used to generate estimates of conditional autocorrelation. The covariance equation of this model is modified to include the potential determinants of autocorrelation including volatility, which is proxied using the time series of filtered probabilities of a Markov regime switching model. Consistent with the previous literature, this paper documents a negative relationship between volatility and autocorrelation. The results suggest that an asymmetry exists in this relationship which is attributed to the constraints placed on short selling.

Original languageEnglish
Pages (from-to)22-40
Number of pages19
JournalInternational Review of Financial Analysis
Volume16
Issue number1
DOIs
Publication statusPublished - 2007

Keywords

  • Conditional autocorrelations
  • Feedback trading
  • Markov models

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