Evidence of an asymmetry in the relationship between volatility and autocorrelation

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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.
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McKenzie, Michael
Kim, Suk-Joong
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Publication Year
2007
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Journal Article
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UNSW Faculty
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download SSRN-id664743.pdf 262.35 KB Adobe Portable Document Format
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