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Quantile regression analysis of hedge fund strategies

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  • Meligkotsidou, Loukia
  • Vrontos, Ioannis D.
  • Vrontos, Spyridon D.

Abstract

Extending previous work on hedge fund pricing, this paper introduces the idea of modelling the conditional quantiles of hedge fund returns using a set of risk factors. Quantile regression analysis provides a way of understanding how the relationship between hedge fund returns and risk factors changes across the distribution of conditional returns. We propose a Bayesian approach to model comparison which provides posterior probabilities for different risk factor models that can be used for model averaging. The most relevant risk factors are identified for different quantiles and compared with those obtained for the conditional expectation model. We find differences in factor effects across quantiles of returns, which suggest that the standard conditional mean regression method may not be adequate for uncovering the risk-return characteristics of hedge funds. We explore potential economic impacts of our approach by analysing hedge fund single strategy return series and by constructing style portfolios.

Suggested Citation

  • Meligkotsidou, Loukia & Vrontos, Ioannis D. & Vrontos, Spyridon D., 2009. "Quantile regression analysis of hedge fund strategies," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 264-279, March.
  • Handle: RePEc:eee:empfin:v:16:y:2009:i:2:p:264-279
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    as
    1. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    2. Mark Mitchell, 2001. "Characteristics of Risk and Return in Risk Arbitrage," Journal of Finance, American Finance Association, vol. 56(6), pages 2135-2175, December.
    3. Michael C. Jensen, 1968. "The Performance Of Mutual Funds In The Period 1945–1964," Journal of Finance, American Finance Association, vol. 23(2), pages 389-416, May.
    4. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    5. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Morton, David P. & Popova, Elmira & Popova, Ivilina, 2006. "Efficient fund of hedge funds construction under downside risk measures," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 503-518, February.
    8. Lorenzo Garlappi & Raman Uppal & Tan Wang, 2007. "Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach," Review of Financial Studies, Society for Financial Studies, vol. 20(1), pages 41-81, January.
    9. Fung, William & Hsieh, David A, 2001. "The Risk in Hedge Fund Strategies: Theory and Evidence from Trend Followers," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 313-341.
    10. Giamouridis, Daniel & Vrontos, Ioannis D., 2007. "Hedge fund portfolio construction: A comparison of static and dynamic approaches," Journal of Banking & Finance, Elsevier, vol. 31(1), pages 199-217, January.
    11. Vikas Agarwal, 2004. "Risks and Portfolio Decisions Involving Hedge Funds," Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 63-98.
    12. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    13. Len Umantsev & Victor Chernozhukov, 2001. "Conditional value-at-risk: Aspects of modeling and estimation," Empirical Economics, Springer, vol. 26(1), pages 271-292.
    14. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    15. Amin, Gaurav S. & Kat, Harry M., 2003. "Hedge Fund Performance 1990–2000: Do the “Money Machines” Really Add Value?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 38(02), pages 251-274, June.
    16. 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.
    17. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    18. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, May.
    19. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    20. Jesse Levin, 2001. "For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement," Empirical Economics, Springer, vol. 26(1), pages 221-246.
    21. Carhart, Mark M, 1997. " On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    22. Eric R. Eide & Mark H. Showalter, 1999. "Factors Affecting the Transmission of Earnings across Generations: A Quantile Regression Approach," Journal of Human Resources, University of Wisconsin Press, vol. 34(2), pages 253-267.
    23. Michelle L. Barnes & Anthony W. Hughes, 2002. "A quantile regression analysis of the cross section of stock market returns," Working Papers 02-2, Federal Reserve Bank of Boston.
    24. Buchinsky, Moshe, 1995. "Quantile regression, Box-Cox transformation model, and the U.S. wage structure, 1963-1987," Journal of Econometrics, Elsevier, vol. 65(1), pages 109-154, January.
    25. Yufeng Han, 2006. "Asset Allocation with a High Dimensional Latent Factor Stochastic Volatility Model," Review of Financial Studies, Society for Financial Studies, vol. 19(1), pages 237-271.
    26. Trede, Mark, 1998. "Making mobility visible: a graphical device," Economics Letters, Elsevier, vol. 59(1), pages 77-82, April.
    27. Avramov, Doron, 2002. "Stock return predictability and model uncertainty," Journal of Financial Economics, Elsevier, vol. 64(3), pages 423-458, June.
    28. Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-428.
    29. Capocci, Daniel & Hubner, Georges, 2004. "Analysis of hedge fund performance," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 55-89, January.
    30. Fung, William & Hsieh, David A., 1999. "A primer on hedge funds," Journal of Empirical Finance, Elsevier, vol. 6(3), pages 309-331, September.
    31. Vrontos, Spyridon D. & Vrontos, Ioannis D. & Giamouridis, Daniel, 2008. "Hedge fund pricing and model uncertainty," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 741-753, May.
    32. Meligkotsidou, Loukia & Vrontos, Ioannis D., 2008. "Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2471-2481, November.
    33. Gilbert W. Bassett Jr. & Hsiu-Lang Chen, 2001. "Portfolio style: Return-based attribution using quantile regression," Empirical Economics, Springer, vol. 26(1), pages 293-305.
    34. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
    35. P. Dellaportas & I. D. Vrontos, 2007. "Modelling volatility asymmetries: a Bayesian analysis of a class of tree structured multivariate GARCH models," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 503-520, November.
    36. Fung, William & Hsieh, David A., 2000. "Performance Characteristics of Hedge Funds and Commodity Funds: Natural vs. Spurious Biases," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(03), pages 291-307, September.
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    Cited by:

    1. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    2. Giannikis, Dimitrios & Vrontos, Ioannis D., 2011. "A Bayesian approach to detecting nonlinear risk exposures in hedge fund strategies," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1399-1414, June.
    3. Jenq-Tzong Shiau & Ting-Ju Chen, 2015. "Quantile Regression-Based Probabilistic Estimation Scheme for Daily and Annual Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2805-2818, June.
    4. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2017. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Working Papers 2017-10, University of Tasmania, Tasmanian School of Business and Economics.
    5. Yang, Ann Shawing, 2016. "Calendar trading of Taiwan stock market: A study of holidays on trading detachment and interruptions," Emerging Markets Review, Elsevier, vol. 28(C), pages 140-154.
    6. Amparo Soler Domínguez & Juan Carlos Matallín Sáez & Emili Tortosa-Ausina, 2013. "Does active management add value? New evidence from a quantile regression approach," Working Papers. Serie EC 2013-02, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    7. 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.
    8. Abdelaati Daouia & Laurent Gardes & Stéphane Girard & Alexandre Lekina, 2011. "Kernel estimators of extreme level curves," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 311-333, August.
    9. Ming-Chi Chen & Chi-Lu Peng & So-De Shyu & Jhih-Hong Zeng, 2012. "Market States and the Effect on Equity REIT Returns due to Changes in Monetary Policy Stance," The Journal of Real Estate Finance and Economics, Springer, vol. 45(2), pages 364-382, August.
    10. Korobilis, Dimitris, 2015. "Quantile forecasts of inflation under model uncertainty," MPRA Paper 64341, University Library of Munich, Germany.
    11. Korobilis, Dimitris, 2017. "Quantile regression forecasts of inflation under model uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 11-20.
    12. Olmo, José & Sanso-Navarro, Marcos, 2012. "Forecasting the performance of hedge fund styles," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2351-2365.
    13. Badshah, Ihsan & Frijns, Bart & Knif, Johan & Tourani-Rad, Alireza, 2016. "Asymmetries of the intraday return-volatility relation," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 182-192.
    14. Mattos, Fabio & Garcia, Philip, 2009. "The Effect of Prior Gains and Losses on Current Risk-Taking Using Quantile Regression," 2009 Conference, April 20-21, 2009, St. Louis, Missouri 53035, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    15. Lee, Bong Soo & Li, Ming-Yuan Leon, 2012. "Diversification and risk-adjusted performance: A quantile regression approach," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2157-2173.
    16. Ramos, Sofía B. & Veiga, Helena, 2010. "Asymmetric effects of oil price fluctuations in international stock markets," DES - Working Papers. Statistics and Econometrics. WS ws100904, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. J. Carlos Matallín-Sáez & Amparo Soler-Domínguez & Emili Tortosa-Ausina, 2013. "Does active management add value? New evidence from a quantile regression," Working Papers 2013/01, Economics Department, Universitat Jaume I, Castellón (Spain).
    18. repec:bla:manchs:v:85:y:2017:i:2:p:212-242 is not listed on IDEAS

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