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A Longer Look at the Asymmetric Dependence between Hedge Funds and the Equity Market

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  • Kang, Byoung Uk
  • In, Francis
  • Kim, Gunky
  • Kim, Tong Suk

Abstract

This paper reexamines, at a range of investment horizons, the asymmetric dependence between hedge fund returns and market returns. Given the current availability of hedge fund data, the joint distribution of longer-horizon returns is extracted from the dynamics of monthly returns using the filtered historical simulation; we then apply the method based on copula theory to uncover the dependence structure therein. While the direction of asymmetry remains unchanged, the magnitude of asymmetry is attenuated considerably as the investment horizon increases. Similar horizon effects also occur on the tail dependence. Our findings suggest that nonlinearity in hedge fund exposure to market risk is more short term in nature, and that hedge funds provide higher benefits of diversification, the longer the horizon.

Suggested Citation

  • Kang, Byoung Uk & In, Francis & Kim, Gunky & Kim, Tong Suk, 2010. "A Longer Look at the Asymmetric Dependence between Hedge Funds and the Equity Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(3), pages 763-789, June.
    • Handle: RePEc:cup:jfinqa:v:45:y:2010:i:03:p:763-789_00
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    Cited by:

    1. Weiß, Gregor N.F. & Bostandzic, Denefa & Neumann, Sascha, 2014. "What factors drive systemic risk during international financial crises?," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 78-96.
    2. Szabolcs Blazsek & Anna Downarowicz, 2013. "Forecasting hedge fund volatility: a Markov regime-switching approach," The European Journal of Finance, Taylor & Francis Journals, vol. 19(4), pages 243-275, April.
    3. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    4. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 899-960, Elsevier.
    5. Lourme, Alexandre & Maurer, Frantz, 2017. "Testing the Gaussian and Student's t copulas in a risk management framework," Economic Modelling, Elsevier, vol. 67(C), pages 203-214.
    6. Supper, Hendrik & Irresberger, Felix & Weiß, Gregor, 2020. "A comparison of tail dependence estimators," European Journal of Operational Research, Elsevier, vol. 284(2), pages 728-742.
    7. Silvo Dajcman, 2013. "Asymmetric Correlation of Sovereign Bond Yield Dynamics in the Eurozone," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 60(6), pages 775-789, December.

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