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Hedge Fund Return Dynamics: Long Memory and Regime Switching

Author

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  • M. A. Limam
  • V. Terraza
  • M. Terraza
Abstract
This paper investigates the dynamics of hedge fund returns and their behavior of persistence in a unified framework through the Markov Switching ARFIMA model of H?rdle and Tsay (2009). Major results based on the CSFB/Tremont hedge fund indexes monthly data during the period 1994-2012, highlight the importance of the long memory parameter magnitude i.e shocks in shaping hedge fund return dynamics and show that the hedge fund dynamics are characterized by two levels of persistence: in the first one, associated to low-volatility regime, hedge fund returns are a stationary long memory process whereas in the second one, associated to high-volatility regime, returns exhibit higher parameter of fractional integration. More precisely, in high volatility regime i.e periods of turmoil, the process tends to be non-stationary but still exhibits a mean-reverting behavior. The findings are interesting and enable us to establish a relationship between hedge fund return states and memory phenomenon.

Suggested Citation

  • M. A. Limam & V. Terraza & M. Terraza, 2017. "Hedge Fund Return Dynamics: Long Memory and Regime Switching," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 8(4), pages 148-166, October.
  • Handle: RePEc:jfr:ijfr11:v:8:y:2017:i:4:p:148-166
    DOI: 10.5430/ijfr.v8n4p148
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    References listed on IDEAS

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    1. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    2. Granger, Clive W. J. & Hyung, Namwon, 2004. "Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 399-421, June.
    3. Katsumi Shimotsu, 2006. "Simple (but Effective) Tests Of Long Memory Versus Structural Breaks," Working Paper 1101, Economics Department, Queen's University.
    4. Zivot, Eric & Andrews, Donald W K, 2002. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 25-44, January.
    5. Brown, Stephen J & Goetzmann, William N, 1995. "Performance Persistence," Journal of Finance, American Finance Association, vol. 50(2), pages 679-698, June.
    6. Perron, Pierre & Qu, Zhongjun, 2010. "Long-Memory and Level Shifts in the Volatility of Stock Market Return Indices," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 275-290.
    7. Qu, Zhongjun, 2011. "A Test Against Spurious Long Memory," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(3), pages 423-438.
    8. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    9. Lavancier, Frédéric & Leipus, Remigijus & Philippe, Anne & Surgailis, Donatas, 2013. "Detection Of Nonconstant Long Memory Parameter," Econometric Theory, Cambridge University Press, vol. 29(5), pages 1009-1056, October.
    10. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    11. Tsay, Wen-Jen & Härdle, Wolfgang Karl, 2007. "A generalized ARFIMA process with Markov-switching fractional differencing parameter," SFB 649 Discussion Papers 2007-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Shimotsu, Katsumi & Phillips, Peter C.B., 2006. "Local Whittle estimation of fractional integration and some of its variants," Journal of Econometrics, Elsevier, vol. 130(2), pages 209-233, February.
    13. Shimotsu, Katsumi, 2010. "Exact Local Whittle Estimation Of Fractional Integration With Unknown Mean And Time Trend," Econometric Theory, Cambridge University Press, vol. 26(2), pages 501-540, April.
    14. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    15. Carl Ackermann & Richard McEnally & David Ravenscraft, 1999. "The Performance of Hedge Funds: Risk, Return, and Incentives," Journal of Finance, American Finance Association, vol. 54(3), pages 833-874, June.
    16. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
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    Cited by:

    1. Costas Siriopoulos & Dionisis Philippas, 2024. "Putting corona into hedge fund managers' head," Economics Bulletin, AccessEcon, vol. 44(1), pages 341-357.

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