Portfolio Allocation of Hedge Funds

Bruder, Benjamin; Darolles, Serge; Koudiraty, Abdul K.; Roncalli, Thierry

doi:10.2139/ssrn.1804511

Cited by 6 publications

(6 citation statements)

References 63 publications

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“…Elevated levels of idiosyncratic risk were considered to generate the outsize returns expected of hedge fund managers. [16] found that the desire for risk-taking led hedge fund managers to acquire skills, including actively gathering a large amount of information, developing analytical skills, and demonstrating sound judgement. The ability to follow company practice in the selection of diversified portfolios that conform to market models, represents superior performance in mutual funds.…”

Section: Risk Aversion Among Hedge Fund Traders and Mutual Fund Tradersmentioning

confidence: 99%

Hedge Fund Investing or Mutual Fund Investing: An Application of Multi-Attribute Utility Theory

Abraham¹

2019

TEL

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This paper contrasts high-risk, hedge fund trading, with low-risk, mutual fund trading, in terms of their differing utility functions. We envision hedge funds, led by informed traders who use information to seek out investment opportunities, timing market conditions, with the expectation that prices will move in their favor. Directional hedge funds act to influence prices, while non-directional hedge funds do not act to influence prices. We present utility functions based on steeply-sloping Laplace distributions and hyperbolic cosine distributions, to describe the actions of directional hedge fund traders. Less steeply-sloping lognormal distributions, Coulomb wave functions, quadratic utility functions, and Bessel utility functions are used to describe the investing style of non-directional hedge fund traders. Flatter Legendre utility functions and inverse sine utility functions describe the modest profit-making aspirations of mutual fund traders. The paper's chief contribution is to develop optimal prices quantitatively, by intersecting utility functions with price distributions. Price distributions for directional hedge fund returns are portrayed as sharp increases and decreases, in the form of jumps, in a discrete arrival Poisson-distributed process. Separate equations are developed for directional hedge fund strategies, including event-driven arbitrage, and global macro strategies. Non-directional strategies include commodity trading, riskneutral arbitrage, and convertible arbitrage, with primarily lognormal pricing distributions, and some Poisson jumps. Mutual funds are perceived to be Markowitz portfolios, lying on the Capital Market Line, or the International Capital Market Line, tangent to the Efficient Frontier of minimum variancemaximum return portfolios.

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Section: Risk Aversion Among Hedge Fund Traders and Mutual Fund Tradersmentioning

confidence: 99%

Hedge Fund Investing or Mutual Fund Investing: An Application of Multi-Attribute Utility Theory

Abraham¹

2019

TEL

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“…Currently, people are more interested in investing in hedge funds, since hedge funds' returns are very different from those of standard asset classes, which makes them attractive to investors looking for a diversified and balanced portfolio [1][2]. Therefore, the research on hedge fund investment naturally focuses on finding the optimal proportion of hedge fund investment, measuring the performance of hedge fund, identifying the risk factors of hedge fund, and finally constructing the optimal hedge fund investment portfolio [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Identification of Optimal Risky Portfolios for Hedge Fund

Rong¹,

Yang²,

He³

et al. 2021

Proceedings of the 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021)

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The hedge fund market, stock market and bond market all have high risks. The purpose of our project is to build the optimal risky portfolio consisting of ten different strategies (which includes Arbitrage, CTA/Managed Futures etc.) to diversify risk and gain a high return, and we also conducted a 5-fold cross-validation test to validate if our method works accurately for all the periods. Using more strategies can help us carry out comprehensive analysis on different situations, periods and types of investment portfolio, so as to disperse risks and obtain high returns, also, ensure the diversity of portfolio and a lower risk.

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“…Elliott and Miao (2009) and Guidolin and Timmermann (2004) combined the RSM with the value at risk (VaR) and conditional VaR (CVaR) and obtained significantly better performance. Bruder et al (2011), Saunders et al (2010) and Guidolin and Ria (2010) combined the Black-Litterman (BL) estimates with the RSM. Most studies focus on subcategories of only one asset class, like hedge funds, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Most studies focus on subcategories of only one asset class, like hedge funds, e.g. Saunders et al (2010) and Bruder et al (2011), or stocks, e.g. Zhao (2010) and Guidolin and Timmermann (n.d.).…”

Section: Introductionmentioning

confidence: 99%

Portfolio Optimization: A Combined Regime-Switching and Black–Litterman Model

Fischer

Seidl

2013

SSRN Journal

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Traditionally portfolios are optimized with the single-regime Markowitz model using the volatility as the risk measure and the historical return as the expected return. This study shows the effects that a regime-switching framework and alternative risk measures (modified value at risk and conditional value at risk) and return measures (CAPM estimates and Black-Litterman estimates) have on the asset allocation and on the absolute and relative performance of portfolios. It demonstrates that the combination of alternative risk and return measures within the regime-switching framework produces significantly better results in terms of performance and the modified Sharpe ratio. The usage of alternative risk and return measures is also shown to meet the need that asset returns very often are not distributed normally and serially correlated. To eliminate these empirical shortcomings of asset returns an unsmoothing algorithm is used in combination with the Cornish-Fisher expansion.

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Portfolio Allocation of Hedge Funds

Cited by 6 publications

References 63 publications

Hedge Fund Investing or Mutual Fund Investing: An Application of Multi-Attribute Utility Theory

Hedge Fund Investing or Mutual Fund Investing: An Application of Multi-Attribute Utility Theory

Identification of Optimal Risky Portfolios for Hedge Fund

Portfolio Optimization: A Combined Regime-Switching and Black–Litterman Model

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