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Time-varying volatility and the power law distribution of stock returns

Missaka Warusawitharana ()

Journal of Empirical Finance, 2018, vol. 49, issue C, 123-141

Abstract: While many studies find that the tail distribution of high frequency stock returns follows a power law, there are only a few explanations for this finding. This study presents evidence that time-varying volatility can account for the power law property of high frequency stock returns. In particular, one finds that a conditional normal model with nonparametric volatility provides a strong fit. Specifically, a cross-sectional regression of the power law coefficients obtained from stock returns on the coefficients implied by the nonparametric volatility model yields a slope close to one. Further, for most of the stocks in the sample taken individually, the model-implied coefficient falls within the 95 percent confidence interval for the coefficient estimated from returns data.

Keywords: Tail distributions; High frequency returns; Power laws; Time-varying volatility (search for similar items in EconPapers)
JEL-codes: C58 D30 G12 (search for similar items in EconPapers)
Date: 2018
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Working Paper: Time-varying Volatility and the Power Law Distribution of Stock Returns (2016) Downloads
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Handle: RePEc:eee:empfin:v:49:y:2018:i:c:p:123-141