Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data
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 financ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Econometrics |
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| Online Access: | https://www.mdpi.com/2225-1146/8/4/40 |
| _version_ | 1797551426032893952 |
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| author | Erhard Reschenhofer Manveer K. Mangat |
| author_facet | Erhard Reschenhofer Manveer K. Mangat |
| author_sort | Erhard Reschenhofer |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:44:32Z |
| format | Article |
| id | doaj.art-9a2f0b1270b24a5ebeaf40aa161e6dc6 |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-03-10T15:44:32Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-9a2f0b1270b24a5ebeaf40aa161e6dc62023-11-20T16:34:37ZengMDPI AGEconometrics2225-11462020-10-01844010.3390/econometrics8040040Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency DataErhard Reschenhofer0Manveer K. Mangat1Department of Statistics and Operations Research, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, AustriaDepartment of Statistics and Operations Research, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, AustriaFor 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.https://www.mdpi.com/2225-1146/8/4/40long-range dependencelog periodogram regressionsmoothed periodogramsubsamplingintraday returns |
| spellingShingle | Erhard Reschenhofer Manveer K. Mangat Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data Econometrics long-range dependence log periodogram regression smoothed periodogram subsampling intraday returns |
| title | Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data |
| title_full | Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data |
| title_fullStr | Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data |
| title_full_unstemmed | Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data |
| title_short | Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data |
| title_sort | reducing the bias of the smoothed log periodogram regression for financial high frequency data |
| topic | long-range dependence log periodogram regression smoothed periodogram subsampling intraday returns |
| url | https://www.mdpi.com/2225-1146/8/4/40 |
| work_keys_str_mv | AT erhardreschenhofer reducingthebiasofthesmoothedlogperiodogramregressionforfinancialhighfrequencydata AT manveerkmangat reducingthebiasofthesmoothedlogperiodogramregressionforfinancialhighfrequencydata |