Hedge Fund Portfolio Allocation with Higher Moments and MVG Models

Hitaj, Asmerilda; Mercuri, Lorenzo

doi:10.1057/9781137025098.0022

Cited by 3 publications

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“…Athayde and Flores (2002) constructed the efficient frontier based on the first four moments of the portfolio return distribution. Jondeau et al (2007) worked on the Taylor expansion of the expected utility function truncated at the fourth order (see among others Martellini & Ziemann, 2010; Hitaj & Mercuri, 2013a, etc.). In the empirical part of this work we consider the Taylor expansion stopped at the second order (referring to the mean–variance (EU‐MV) portfolio) or at the third order (referring to the mean–variance–skewness (EU‐MVS) portfolio) for different levels of risk aversion.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

Are Smart Beta strategies suitable for hedge fund portfolios?

Hitaj

Zambruno

2016

Rev Financ Econ

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“…For instance, choosing all α i =2 for i =1,…, N , we obtain the multivariate Variance Gamma introduced in Semeraro () as a special case. As observed in Hitaj and Mercuri (), Semeraro's model is not able to capture some situations often observed in financial time series. We recall that Semeraro's model has the same structure as in but instead of each X i , we have W i where W 1 ,…, W N are independent Standard Normals.…”

Section: Multivariate Mixed Tempered Stablementioning

confidence: 90%

“…See Appendix B for details on moment derivation. From and is evident that the multivariate M i x e d T S −Γ overcomes the limits of the multivariate Variance Gamma distribution in capturing the dependence structure between components (see Hitaj & Mercuri, ). Indeed, the relation that exists between the sign of the skewness of two marginals and the sign of their covariance in the multivariate Variance Gamma is broken up by the tempering parameters in the multivariate M i x e d T S −Γ.…”

Section: Multivariate Mixed Tempered Stablementioning

confidence: 99%

“…A similar approach has been used in Semeraro () for the construction of a multivariate Variance Gamma distribution starting from the idea that the components in the mixing random vector are Gamma distributed. However, as observed in Mercuri (), Semeraro's model seems to be too restrictive for describing the joint distribution of asset returns. In particular, the sign of the skewness of the marginal distributions determines the sign of the covariance.…”

Section: Introductionmentioning

confidence: 99%

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On Properties of the MixedTS Distribution and Its Multivariate Extension

Hitaj

Hubalek

Mercuri

et al. 2018

Int Statistical Rev

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A review of the univariate MixedTS is given and some new results on the asymptotic tail behaviour are derived. The multivariate version of the Mixed Tempered Stable, which is a generalisation of the Normal Variance Mean Mixtures, is discussed. Characteristics of this distribution, its capacity in fitting tails and in capturing dependence structure between components are investigated. We discuss a random number generating procedure and introduce an estimation methodology based on the minimisation of a distance between empirical and theoretical characteristic functions. Asymptotic tail behaviour of the univariate Mixed Tempered Stable is exploited in the estimation procedure in order to obtain a better fitting on tails. Advantages of the multivariate Mixed Tempered Stable distribution are discussed and illustrated via numerical analysis.

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“…See Appendix A for details on moment derivation. From ( 37) and ( 38) is evident that the multivariate MixedT S−Γ overcomes the limits of the multivariate Variance Gamma distribution in capturing the dependence structure between components (see Hitaj and Mercuri (2013a)). Indeed, the relation that exists between the sign of the skewness of two marginals and the sign of their covariance in the multivariate Variance Gamma, is broken up by the tempering parameters in the multivariate MixedT S − Γ.…”

Section: Proposition 9 the Characteristic Function Of The Multivariat...mentioning

confidence: 99%

Multivariate Mixed Tempered Stable Distribution

Hitaj,

Hubalek,

Mercuri

et al. 2016

Preprint

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The multivariate version of the Mixed Tempered Stable is proposed. It is a generalization of the Normal Variance Mean Mixtures. Characteristics of this new distribution and its capacity in fitting tails and capturing dependence structure between components are investigated. We discuss a random number generating procedure and introduce an estimation methodology based on the minimization of a distance between empirical and theoretical characteristic functions. Asymptotic tail behavior of the univariate Mixed Tempered Stable is exploited in the estimation procedure in order to obtain a better model fitting. Advantages of the multivariate Mixed Tempered Stable distribution are discussed and illustrated via simulation study.

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Hedge Fund Portfolio Allocation with Higher Moments and MVG Models

Cited by 3 publications

References 0 publications

Are Smart Beta strategies suitable for hedge fund portfolios?

Are Smart Beta strategies suitable for hedge fund portfolios?

On Properties of the MixedTS Distribution and Its Multivariate Extension

Multivariate Mixed Tempered Stable Distribution

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