LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

Jonathan Wright

Econometric Reviews, 2002, vol. 21, issue 4, 397-417

Abstract: Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.

Keywords: Semiparametric methods; Fractional integration; Stochastic volatility; Stock returns; Heavy tails; JEL Classification: C22; G10 (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (15)

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Working Paper: Log-periodogram estimation of long memory volatility dependencies with conditionally heavy tailed returns (2000)
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DOI: 10.1081/ETC-120015382

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