Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11023363 | Journal of Empirical Finance | 2018 | 19 Pages |
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.
Related Topics
Social Sciences and Humanities
Economics, Econometrics and Finance
Economics and Econometrics
Authors
Missaka Warusawitharana,