Beyond Black-Litterman: Views on Non-Normal Markets

Meucci, Attilio

doi:10.2139/ssrn.848407

Cited by 39 publications

(28 citation statements)

References 9 publications

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“…Harvey et al make the important point that skewness in asset returns is not always evident from a univariate histogram and present a number of bivariate plots to illustrate the point. A recent paper which also uses the distribution is by Meucci (2006).…”

Section: The Multivariate Extended Skew-normal Distributionmentioning

confidence: 99%

Asset pricing and portfolio selection based on the multivariate extended skew-Student-t distribution

Adcock

2009

Ann Oper Res

122

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The returns on most financial assets exhibit kurtosis and many also have probability distributions that possess skewness as well. In this paper a general multivariate model for the probability distribution of assets returns, which incorporates both kurtosis and skewness, is described. It is based on the multivariate extended skew-Student-t distribution. Salient features of the distribution are described and these are applied to the task of asset pricing. The paper shows that the market model is non-linear in general and that the sensitivity of asset returns to return on the market portfolio is not the same as the conventional beta, although this measure does arise in special cases. It is shown that the variance of asset returns is time varying and depends on the squared deviation of market portfolio return from its location parameter. The first order conditions for portfolio selection are described. Expected utility maximisers will select portfolios from an efficient surface, which is an analogue of the familiar meanvariance frontier, and which may be implemented using quadratic programming.

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Section: The Multivariate Extended Skew-normal Distributionmentioning

confidence: 99%

Asset pricing and portfolio selection based on the multivariate extended skew-Student-t distribution

Adcock

2009

Ann Oper Res

122

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“…We first present computational results using a relatively large number of instruments together with randomly generated scenarios, and next evaluate the solution procedures using two real-world examples obtained from Meucci [20,21]. All algorithms were coded in C++ and CPLEX 9.0 C++ Concert Technology was used for solving the linear programs.…”

Section: Numerical Studymentioning

confidence: 99%

“…We initially examined two instances compiled by Meucci [20,21]. The first instance, denoted STOCK, has four international stock indices, the American S&P500, the British FTSE100, the French CAC40, and the German DAX, and the second, denoted BOND, has four US Treasury bonds with 2, 5, 10, and 30 years to maturity, respectively.…”

Section: Experiments Using Real-world Examplesmentioning

confidence: 99%

“…The first instance, denoted STOCK, has four international stock indices, the American S&P500, the British FTSE100, the French CAC40, and the German DAX, and the second, denoted BOND, has four US Treasury bonds with 2, 5, 10, and 30 years to maturity, respectively. STOCK consists of 10,000 scenarios generated from prior and posterior distributions (denoted by STOCK-PR and STOCK-PO) using Monte Carlo simulation, while BOND has 50,000 scenarios generated from each of two respective distributions, denoted by BOND-PR and BOND- [20,21] for details on the related data.) Noting that there are only four instruments, we reduced the stopping tolerance to 10 −6 for PBA (otherwise, this method performed excessive iterations without sufficient improvements).…”

Section: Experiments Using Real-world Examplesmentioning

confidence: 99%

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Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization

2008

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“…Satchell and Scowcroft [2000] and Qian and Gorman [2001] extend the approach of Black-Litterman to the second moments of distribution -variances and covariances of asset returns. Meucci [2006], Beach and Orlov [2007], and Martellini and Ziemann [2007] include non-normally distributed returns and consider fat tails, which is essential for hedge funds and derivatives. The literature emphasizes that establishing an investor's view for the Black-Litterman model is rather difficult.…”

mentioning

confidence: 99%

Quantitative Forecast Model for the Application of the Black-Litterman Approach

Becker

Gürtler

2010

SSRN Journal

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Beyond Black-Litterman: Views on Non-Normal Markets

Cited by 39 publications

References 9 publications

Asset pricing and portfolio selection based on the multivariate extended skew-Student-t distribution

Asset pricing and portfolio selection based on the multivariate extended skew-Student-t distribution

Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization

Quantitative Forecast Model for the Application of the Black-Litterman Approach

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