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Long Memory on the German Stock Exchange

  • Henryk GURGUL


    (Faculty of Management, University of Science and Technology, Krakow – corresponding author)

  • Tomasz WÓJTOWICZ

    (Faculty of Management, University of Science and Technology, Krakow)

In this study, the contributors present the results of their investigations into the long-memory properties of trading volume and the volatility of stock returns (given by absolute returns and alternatively by square returns). Their database is daily stock data of German companies in the DAX segment of the German Stock Exchange. The purpose of these investigations is the calculation of memory parameters and to determine whether there exists the same degree of long memory for trading-volume and return-volatility data. Calculations are performed on daily results from January 1994 to November 2005 and in three sub-periods: January 1994 to December 1997, January 1998 to December 2001, and January 2002 to November 2005.

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Article provided by Charles University Prague, Faculty of Social Sciences in its journal Finance a uver - Czech Journal of Economics and Finance.

Volume (Year): 56 (2006)
Issue (Month): 09-10 (September)
Pages: 447-468

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Handle: RePEc:fau:fauart:v:56:y:2006:i:9-10:p:447-468
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  1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
  2. Lamoureux, Christopher G & Lastrapes, William D, 1994. "Endogenous Trading Volume and Momentum in Stock-Return Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 253-60, April.
  3. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January.
  4. Giampiero Gallo & Barbara Pacini, 2000. "The effects of trading activity on market volatility," The European Journal of Finance, Taylor & Francis Journals, vol. 6(2), pages 163-175.
  5. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 261-68, July.
  6. Lobato, Ignacio N & Velasco, Carlos, 2000. "Long Memory in Stock-Market Trading Volume," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 410-27, October.
  7. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
  8. Katsumi Shimotsu & Peter C.B. Phillips, 2000. "Local Whittle Estimation in Nonstationary and Unit Root Cases," Cowles Foundation Discussion Papers 1266, Cowles Foundation for Research in Economics, Yale University, revised Sep 2003.
  9. Velasco, Carlos, 1999. "Non-stationary log-periodogram regression," Journal of Econometrics, Elsevier, vol. 91(2), pages 325-371, August.
  10. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(01), pages 109-126, March.
  11. Michael D. McKenzie & Robert W. Faff, 2003. "The Determinants of Conditional Autocorrelation in Stock Returns," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 26(2), pages 259-274.
  12. Lobato, Ignacio N., 1999. "A semiparametric two-step estimator in a multivariate long memory model," Journal of Econometrics, Elsevier, vol. 90(1), pages 129-153, May.
  13. Katsumi Shimotsu & Peter C.B. Phillips, 2000. "Pooled Log Periodogram Regression," Cowles Foundation Discussion Papers 1267, Cowles Foundation for Research in Economics, Yale University.
  14. Billy P. Helms & Fred R. Kaen & Robert E. Rosenman, 1984. "Memory in commodity futures contracts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 4(4), pages 559-567, December.
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