Optimal Hedge Fund Allocation with Improved Estimates for Coskewness and Cokurtosis Parameters

Hitaj, Asmerilda; Martellini, Lionel; Zambruno, Giovanni

doi:10.3905/jai.2012.14.3.006

Cited by 18 publications

(10 citation statements)

References 18 publications

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“…Introducing higher moments in portfolio selection by means of the Taylor expansion of the expected utility function is widely used in Finance: see among others (Jondeau et al, 2007; Hitaj et al, 2012, etc.). In the particular case that a negative exponential utility function is used, its Taylor expansion up to the third order, which represents the objective function of the investor optimization problem, is the following:

({,, \begin{array}{l} max_{w} - e^{- λ ((),, μ boldw)} ([],, 1 + \frac{λ^{2}}{2} w^{'} M_{2} boldw - \frac{λ^{3}}{6} w^{'} M_{3} ((),, boldw \otimes boldw)) \\ normals . normalt . boldw \in A_{0} \end{array}),

where λ is the level of risk aversion.…”

Section: Higher Moments Portfolio Allocation Using Polynomial Goal Prmentioning

confidence: 99%

“…To our knowledge, no empirical research exists addressing this question. It is well known that hedge funds are characterized by high skewness and kurtosis, and different empirical analysis have demonstrated that a three or four‐moment portfolio allocation is better than a two‐moment portfolio allocation (see Athayde & Flores, 2002; Jondeau, Poon, & Rockinger, 2007; Martellini & Ziemann, 2010; Hitaj, Martellini, & Zambruno, 2012; Hitaj & Mercuri, 2013b, etc. ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Are Smart Beta strategies suitable for hedge fund portfolios?

Hitaj

Zambruno

2016

Rev Financ Econ

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In the equity context different Smart Beta strategies (such as the equally weighted, global minimum variance, equal risk contribution and maximum diversified ratio) have been proposed as alternatives to the capweighted index. These new approaches have attracted the attention of equity managers as different empirical analyses demonstrate the superiority of these strategies with respect to cap-weighted and to strategies that consider only mean and variance. In this paper we focus our attention to hedge fund index portfolios and analyze if the results reported in the equity framework are still valid. We consider hedge fund index and equity portfolios, the approaches used for portfolio selection are the four 'Smart Beta' strategies, mean-variance and meanvariance-skewness. In the two latter approaches the Taylor approximation of a CARA expected utility function and the Polynomial Goal Programing (PGP) have been used. The obtained portfolios are analyzed in the in-sample as well as in the out-of-sample perspectives.

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({,, \begin{array}{l} max_{w} - e^{- λ ((),, μ boldw)} ([],, 1 + \frac{λ^{2}}{2} w^{'} M_{2} boldw - \frac{λ^{3}}{6} w^{'} M_{3} ((),, boldw \otimes boldw)) \\ normals . normalt . boldw \in A_{0} \end{array}),

where λ is the level of risk aversion.…”

Section: Higher Moments Portfolio Allocation Using Polynomial Goal Prmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Are Smart Beta strategies suitable for hedge fund portfolios?

Hitaj

Zambruno

2016

Rev Financ Econ

Self Cite

View full text Add to dashboard Cite

show abstract

“…Table [7] here For each level of risk aversion we have reported the percentage annual mean, percentage annual standard deviation, skewness and kurtosis of the out-ofsample portfolio returns. We remark that an investor prefers a portfolio with the highest mean and skewness and lowest standard deviation and kurtosis.…”

Section: Empirical Analysismentioning

confidence: 99%

“…Our empirical analysis is carried on a portfolio composed by 18 assets selected from the S&P500 Index and the dataset consists in daily returns observed in the period ranging from 01/04/2000 to 01/09/2011. We perform an out-of-sample comparison between the Multivariate Variance Gamma models and the sample approach (see recently [7] and references therein) in terms of Monetary Utility Gain/Loss as introduced by Ang and Bekaert [1].…”

Section: Introductionmentioning

confidence: 99%

“…These estimators are characterized by high estimation error when the number of observations is small. To overcome this problem, two methodologies have been proposed: the first is using improved estimators (see [16] and recently [7]) and the second is assuming a particular joint distribution of returns (see [9] and the references therein for a general survey).…”

Section: Introductionmentioning

confidence: 99%

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Portfolio Allocation Using Multivariate Variance Gamma

Hitaj

Mercuri

2011

SSRN Journal

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In this paper we investigate empirically the effect of using higher moments in portfolio allocation when parametric and non parametric models are used. The non parametric model considered in this paper is the sample approach while the parametric one is constructed assuming Multivariate Variance Gamma (MVG henceforth) joint distribution for asset returns. We consider the MVG models proposed by Madan and Seneta (1990), Semeraro (2006) and Wang (2009). We perform an out-of-sample analysis comparing the optimal portfolios obtained using the MVG models and the sample approach. Our portfolio is composed of 18 assets selected from the S&P500 Index and the dataset consists in daily returns observed in the period ranging from

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Portfolio Optimization Using Modified Herfindahl Constraint

Hitaj

Zambruno

2017

International Series in Operations Research &Amp; Management Science

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Optimal Hedge Fund Allocation with Improved Estimates for Coskewness and Cokurtosis Parameters

Cited by 18 publications

References 18 publications

Are Smart Beta strategies suitable for hedge fund portfolios?

Are Smart Beta strategies suitable for hedge fund portfolios?

Portfolio Allocation Using Multivariate Variance Gamma

Portfolio Optimization Using Modified Herfindahl Constraint

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