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Long memory in volatilities of German stock returns

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  • Philipp Sibbertsen

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Abstract

We show that there is strong evidence of long-range dependence in the volatilities of several German stock returns. This will be done by applying a method using the difference of the classical log-periodogram regression estimator for the memory parameter and of the tapered periodogram based estimator. Both estimators give similar values for the memory parameter for each series and this indicates long memory. To support our findings we apply also a methodology using the sample variance and a wavelet based estimator to the data. Also these two methods show clear evidence of long-range dependence in the volatilities of German stock returns. Copyright Springer-Verlag 2004

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Bibliographic Info

Article provided by Springer in its journal Empirical Economics.

Volume (Year): 29 (2004)
Issue (Month): 3 (09)
Pages: 477-488

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Handle: RePEc:spr:empeco:v:29:y:2004:i:3:p:477-488

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Keywords: Long memory; volatilities; log-periodogram estimation; C14; C22;

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References

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  1. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
  2. Walter Kramer & Philipp Sibbertsen, 2002. "Testing for Structural Changes in the Presence of Long Memory," International Journal of Business and Economics, College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 1(3), pages 235-242, December.
  3. Sibbertsen, Philipp, 2001. "Log-periodogram estimation of the memory parameter of a long-memory process under trend," Technical Reports 2001,39, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  4. Philipp Sibbertsen, 2004. "Long memory versus structural breaks: An overview," Statistical Papers, Springer, vol. 45(4), pages 465-515, October.
  5. Krämer, Walter & Sibbertsen, Philipp & Kleiber, Christian, 2001. "Long memory vs. structural change in financial time series," Technical Reports 2001,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  6. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
  7. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
  8. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
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Citations

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Cited by:
  1. Sibbertsen, Philipp & Venetis, Ioannis, 2003. "Distinguishing between long-range dependence and deterministic trends," Technical Reports 2003,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  2. Luis A. Gil-Alana & Yun Cao, 2010. "Stock market prices in China. Efficiency, mean reversion, long memory volatility and other implicit dynamics," Faculty Working Papers 12/11, School of Economics and Business Administration, University of Navarra.
  3. Kuswanto, Heri, 2009. "A New Simple Test Against Spurious Long Memory Using Temporal Aggregation," Hannover Economic Papers (HEP) dp-425, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  4. Wolfgang Härdle & Julius Mungo, 2007. "Long Memory Persistence in the Factor of Implied Volatility Dynamics," SFB 649 Discussion Papers SFB649DP2007-027, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  5. Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2010. "Long memory volatility in Chinese stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1425-1433.
  6. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2011. "Long Memory and Fractional Integration in High-Frequency British Pound / Dollar Spot Exchange Rates," Faculty Working Papers 02/11, School of Economics and Business Administration, University of Navarra.
  7. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2013. "Long Memory and Fractional Integration in High Frequency Data on the US Dollar / British Pound Spot Exchange Rate," CESifo Working Paper Series 4224, CESifo Group Munich.
  8. Gil-Alana, Luis A. & Shittu, Olanrewaju I. & Yaya, OlaOluwa S., 2014. "On the persistence and volatility in European, American and Asian stocks bull and bear markets," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 149-162.
  9. J. Cuñado & L. Gil-Alana & F. Gracia, 2009. "US stock market volatility persistence: evidence before and after the burst of the IT bubble," Review of Quantitative Finance and Accounting, Springer, vol. 33(3), pages 233-252, October.
  10. Leïla Nouira & Mohamed Boutahar & Vêlayoudom Marimoutou, 2009. "The effect of tapering on the semiparametric estimators for nonstationary long memory processes," Statistical Papers, Springer, vol. 50(2), pages 225-248, March.
  11. Herzberg, Markus & Sibbertsen, Philipp, 2004. "Pricing of options under different volatility models," Technical Reports 2004,62, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  12. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2011. "Long Memory and Volatility Dynamics in the US Dollar Exchange Rate," Faculty Working Papers 04/11, School of Economics and Business Administration, University of Navarra.

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