Option-like properties in the distribution of hedge fund returns

Abstract

Hedge funds have recently become popular because of their low correlation with traditional investments and their ability to generate positive returns with a relatively low volatility. However, a close look at those high-performing hedge funds raises the questions on whether their performance is truly superior and whether the high management fees are justified. Incurring no alpha costs, passive hedge fund replication strategies raise the question on whether they can similarly perform by improving efficiency at reduced costs. Therefore, this study investigates two different model approaches for the equity long/short strategy, where weighted segmented linear regression models are employed and combined with two-state Markov switching models. The main finding proves a short put option structure, i.e., short equity market volatility, with the put structure present in all market states. We obtain an evidence that the hedge fund managers decrease their short-volatility profile during turbulent markets.

Keywords

hedge funds / hedge fund index / segmented linear regression models / regime-switching models / mimicking portfolios / single factor-based hedge fund replication / equity long–short strategy

References

[1] Akaike H (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6): 716–723

[2] Amenc N, Géhin W, Martellini L, Meyfredi J C (2008). Passive hedge fund replication: A critical assessment of existing techniques. Journal of Alternative Investments, 11(2): 69–83

[3] Anson M J P, Ho H (2003). Short volatility strategies: Identification, measurement and risk. Journal of Investment Management, 1(2): 30–43

[4] Baum L E (1972). An inequality and associated maximization technique in statistical estimation of probabilistic functions of Markov processes. Inequalities, 3(1): 1–8

[5] Brunner B, Hafner R (2005). Cope with the characteristics. Finanz Business

[6] Cao C, Chen Y, Liang B, Lo A (2013). Can hedge funds time market liquidity? Journal of Financial Economics, 109(2): 493–516

[7] Chen Y (2007). Timing ability in the focus market of hedge funds. Journal of Investment Management, 5: 66–98

[8] Chen Y, Cliff M, Zhao H (2017). Hedge funds: The good, the bad, and the lucky. Journal of Financial and Quantitative Analysis, 52(3): 1081–1109

[9] Chen Y, Ferson W, Peters H (2010). Measuring the timing ability and performance of bond mutual funds. Journal of Financial Economics, 98(1): 72–89

[10] Chen Y, Liang B (2007). Do market timing hedge funds time the market? Journal of Financial and Quantitative Analysis, 42(4): 827–856

[11] Chen Y (2011). Derivatives use and risk taking: Evidence from the hedge fund industry. Journal of Financial and Quantitative Analysis, 46(4): 1073–1106

[12] Denk K (2014). Optionality Properties in the Return Distribution of Hedge Fund Returns. Dissertation for the Master’s Degree. München: Technische Universität München

[13] Ferson W, Schadt R W (1996). Measuring fund strategy and performance in changing economic conditions. The Journal of Finance, 51(2): 425–461

[14] Fung W, Hsieh D (2004). Hedge fund benchmarks: A risk based approach. Financial Analysts Journal, 60(5): 65–80

[15] Hauptmann J, Hoppenkamps A, Min A, Ramsauer F, Zagst R (2014). Forecasting market turbulences using regime-switching models. Financial Markets and Portfolio Management, 28(2): 139–164

[16] Henriksson R, Merton R (1981). On market timing and investment performance II: Statistical procedures for evaluating forecasting skills. Journal of Business, 54(4): 513–533

[17] Hudson D J (1966). Fitting segmented curves whose join points have to be estimated. Journal of the American Statistical Association, 61(316): 1097–1129

[18] Jacobs B I, Levy K N, Starer D (1999). Long–short portfolio management: An integrated approach. The Journal of Portfolio Management, 25(2): 23–32

[19] Jaeger L, Wagner C (2005). Factor modeling and benchmarking of hedge funds: Can passive investments in hedge fund strategies deliver? Journal of Alternative Investments, 8(3): 9–36

[20] Kat H M, Amin G S (2001). Hedge fund performance 1990–2000: Do the money machines really add value? In: EFMA 2001 Lugano Meetings

[21] Königsberger K (2003). Analysis 2, vol. 5. Berlin: Springer

[22] Mitchell M, Pulvino T (2001). Characteristics of risk and return in risk arbitrage. The Journal of Finance, 56(6): 2135–2175

[23] Quandt R (1958). The estimation of the parameters of a linear regression system obeying two separate regimes. Journal of the American Statistical Association, 53(284): 873–880

[24] Schneeweis T, Spurgin R (1998). Multifactor analysis of hedge fund, managed futures, and mutual fund return and risk characteristics. Journal of Alternative Investments, 1(2): 1–24

[25] Schwarz G (1978). Estimating the dimension of a model. Annals of Statistics, 6(2): 461–464

[26] Treynor J, Mazuy K (1966). Can mutual funds outguess the market? Harvard Business Review, 44: 131–136

[27] Viterbi A (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2): 260–269

[28] Zhang J, Stine R A (2001). Autocovariance structure of Markov regime switching models and model selection. Journal of Time Series Analysis, 22(1): 107–124

RIGHTS & PERMISSIONS

Higher Education Press