Hedge Funds and Asset Allocation: Investor Confidence, Diversification Benefits, and a Change in Investment Style Composition

Bessler, Wolfgang; Holler, Julian

doi:10.1007/978-3-642-01044-6_40

Cited by 3 publications

(4 citation statements)

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“…Consistent with empirical evidence on the portfolio composition of heterogeneous groups of institutional investors, we find that university endowment funds exhibit a significantly larger share of hedge funds in their optimal asset allocations. This result is in line with previous research that uses a similar Bayesian approach for a time period before the recent financial crisis (Cvitanic et al 2003;Bessler and Holler 2010).…”

Section: Introductionsupporting

confidence: 93%

“…In comparison to the traditional sequential approach (Fama and French 1993;Bessler and Opfer 2003;Bessler and Holler 2010), this democratic procedure is not sensitive to the choice of a particular starting vector and the order in which the factors are orthogonalized. The results indicate that returns on traditional and alternative asset classes explain a rising share of hedge fund returns which is driven by dynamics underlying the structural composition of the aggregate hedge fund index.…”

Section: Introductionmentioning

confidence: 98%

“…During the recent financial crisis, however, the investment performance of hedge funds has deteriorated significantly. Moreover, recent studies indicate that diversification benefits of hedge funds have continuously declined due to a slow but persistent upward trend in the co-movement of hedge fund returns with traditional and alternative asset classes (Füss and Kaiser 2007;Bessler and Holler 2010).…”

Section: Introductionmentioning

confidence: 99%

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Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

Bessler

Holler

Kurmann

2012

Financ Mark Portf Manag

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This paper analyzes the contribution of hedge funds to optimal asset allocations between 1993 and 2010. The preferences of specific institutional investors are captured by implementing a Bayesian asset allocation framework that incorporates heterogeneous expectations regarding hedge fund alpha. Mean-variance spanning tests are used to infer the ability of hedge funds to significantly enhance the mean-variance efficient frontier. Further, a novel democratic variance decomposition procedure sheds light on the dynamics in the co-movement of hedge fund returns with a set of common benchmark assets. The empirical findings indicate that portfolio benefits of hedge funds are time-varying and strongly depend on investor optimism regarding hedge funds' ability to generate alpha. In general, allocations to hedge funds improve the global minimum variance portfolio even after controlling for short-selling restrictions and minimum diversification constraints. However, due to dynamics underlying the composition of the aggregate hedge fund universe, the factor structure of hedge fund returns has become more similar to the benchmark assets over time.

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Section: Introductionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

Bessler

Holler

Kurmann

2012

Financ Mark Portf Manag

Self Cite

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“…During the recent financial crisis, however, the investment performance of hedge funds has deteriorated substantially as they suffered significant losses on their portfolio holdings. Moreover, recent studies indicate that diversification benefits of hedge funds have continuously declined due to a slow but persistent upward trend in the co-movement of hedge fund returns with conventional asset classes [Bressler and Holler, 2010].…”

Section: Applications To Hedge Fundsmentioning

confidence: 99%

Time-Varying Coefficient Models And The Kalman Filter : Applications To Hedge Funds

Punales¹

2021

Preprint

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There are various studies concerned with the estimation of stochastically varying coefficients for the hedge fund series but just [sic] few are available in the literature that study the model with time-varying coefficients and non-linear factor, or make a comparison of the series before and during the financial crisis. This work studies a model with linear and non-linear factors with stochastically varying coefficients to obtain better estimation of the exposure of the hedge fund and accuracy in the results. Better exposure estimates implies better hedging against negative changes in the market hence a reduction in the risk taken by the hedge fund manager. Besides, different techniques have been studied, implemented and applied in this thesis to estimate and analyze time varying exposures of different HFRX Index (an index that describes the hedge fund industry performance). The study shows that option-like models with time-varying coefficients perform the best for most of the HFRX indexes analyzed. It also shows that the Kalman Filter technique combined with the Maximum Likelihood Estimator is the best approach to estimate time-varying coefficients. In addition, we provide evidence that Kalman Filter is in a better position to capture changes in the exposure to the market conditions.

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Hedge Funds and Optimal Asset Allocation: Bayesian Expectations, Time-Varying Investment Opportunities and Mean-Variance Spanning

2011

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Hedge Funds and Asset Allocation: Investor Confidence, Diversification Benefits, and a Change in Investment Style Composition

Cited by 3 publications

References 5 publications

Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

Hedge funds and optimal asset allocation: Bayesian expectations and spanning tests

Time-Varying Coefficient Models And The Kalman Filter : Applications To Hedge Funds

Hedge Funds and Optimal Asset Allocation: Bayesian Expectations, Time-Varying Investment Opportunities and Mean-Variance Spanning

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