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Open AccessArticle

Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data

Department of Statistics and Operations Research, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
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Econometrics 2020, 8(4), 40; https://doi.org/10.3390/econometrics8040040
Received: 8 March 2020 / Revised: 24 September 2020 / Accepted: 27 September 2020 / Published: 10 October 2020
For typical sample sizes occurring in economic and financial applications, the squared bias of estimators for the memory parameter is small relative to the variance. Smoothing is therefore a suitable way to improve the performance in terms of the mean squared error. However, in an analysis of financial high-frequency data, where the estimates are obtained separately for each day and then combined by averaging, the variance decreases with the sample size but the bias remains fixed. This paper proposes a method of smoothing that does not entail an increase in the bias. This method is based on the simultaneous examination of different partitions of the data. An extensive simulation study is carried out to compare it with conventional estimation methods. In this study, the new method outperforms its unsmoothed competitors with respect to the variance and its smoothed competitors with respect to the bias. Using the results of the simulation study for the proper interpretation of the empirical results obtained from a financial high-frequency dataset, we conclude that significant long-range dependencies are present only in the intraday volatility but not in the intraday returns. Finally, the robustness of these findings against daily and weekly periodic patterns is established.
Keywords: long-range dependence; log periodogram regression; smoothed periodogram; subsampling; intraday returns long-range dependence; log periodogram regression; smoothed periodogram; subsampling; intraday returns
MDPI and ACS Style

Reschenhofer, E.; Mangat, M.K. Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data. Econometrics 2020, 8, 40.

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