Risk and Asset Allocation

Meucci, Attilio

doi:10.1007/978-3-540-27904-4

Cited by 318 publications

(211 citation statements)

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“…Grinold and Kahn (2000), Litterman (2003), and Meucci (2005) are important monographs detailing the practical applications of the mean-variance framework. Because of its enormous theoretical and practical importance, a substantial body of literature has developed under the mean-variance framework, and it is not surprising to find several insightful reviews that have been written on the related portfolio selection problem.…”

Section: Introductionmentioning

confidence: 99%

Robust portfolios: contributions from operations research and finance

2009

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In this paper we provide a survey of recent contributions to robust portfolio strategies from operations research and finance to the theory of portfolio selection. Our survey covers results derived not only in terms of the standard mean-variance objective, but also in terms of two of the most popular risk measures, mean-VaR and mean-CVaR developed recently. In addition, we review optimal estimation methods and Bayesian robust approaches.

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Section: Introductionmentioning

confidence: 99%

Robust portfolios: contributions from operations research and finance

2009

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“…It is natural in financial modeling to consider cumulative distribution function F X to map a generic random variable X into a random variable U. Following Meucci (2005) the random variable U is called grade of X and reads:…”

Section: Definition Of Copulasmentioning

confidence: 99%

“…Following Meucci (2005), the quantitative framework of solving a generic asset allocation problem can be summarized to several steps: (i) detecting quantities that fully describe behavior of asset prices: so-called market invariants. For equities the invariants are the returns; for bonds the invariants are the changes in the yield to maturity, for vanilla derivatives it is the change in implied volatility; (ii) estimating the distribution of market invariants; (iii) mapping the distribution of market invariants into the distribution of asset prices at a generic time in the future; (iv) defining optimality depending on investor's profile; (v) computing the optimal allocation, solving portfolio selection problem.…”

Section: Introductionmentioning

confidence: 99%

Comparison of different copula assumptions and their application in portfolio construction

Štulajter¹

2009

ER-CEREI

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The paper deals with modeling of mutual dependencies among financial assets. Its aim is to investigate the impact of different copula assumptions on optimal portfolios, when CVaR optimization is used. Strategic asset allocation perspective is supposed. It is demonstrated that copula functions enable us to separate the modeling of dependency features of financial assets from the modeling of marginal distribution characteristics, in the context of practical portfolio construction tasks. The difference between portfolios constructed using normal copula and student t copula is shown when mutual or pension fund exposed to long-only constrain is assumed. The fund is considered to invest solely into equity and fixed-income instruments. As expected, the exclusive use of linear correlation coefficients leads to underestimation of total portfolio risk. The superiority of student t copula portfolios intensifies as the confidence level of CVaR rises and/or as the CVaR target increases.

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“…The predictive distribution combines both estimation risk and market risk. Many Bayesian approaches to portfolio optimization are based on a purely analytical fundament (Garlappi et al, 2007, Jorion, 1986, Klein and Bawa, 1976, Polson and Tew, 2000, Meucci, 2005. However, this is not suitable if we want to take stylized facts into account and then generally it is not possible to find the predictive distribution analytically.…”

Section: Motivationmentioning

confidence: 99%

A general approach to Bayesian portfolio optimization

2008

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe develop a general approach to portfolio optimization taking account of estimation risk and stylized facts of empirical finance. This is done within a Bayesian framework. The approximation of the posterior distribution of the unknown model parameters is based on a parallel tempering algorithm. The portfolio optimization is done using the first two moments of the predictive discrete asset return distribution. For illustration purposes we apply our method to empirical stock market data where daily asset logreturns are assumed to follow an orthogonal MGARCH process with tdistributed perturbations. Our results are compared with other portfolios suggested by popular optimization strategies.Keywords: Bayesian portfolio optimization, Gordin's condition, Markov chain Monte Carlo, Stylized facts. AMS AbstractWe develop a general approach to portfolio optimization taking account of estimation risk and stylized facts of empirical finance. This is done within a Bayesian framework. The approximation of the posterior distribution of the unknown model parameters is based on a parallel tempering algorithm. The portfolio optimization is done using the first two moments of the predictive discrete asset return distribution. For illustration purposes we apply our method to empirical stock market data where daily asset log-returns are assumed to follow an orthogonal MGARCH process with t-distributed perturbations. Our results are compared with other portfolios suggested by popular optimization strategies.

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Risk and Asset Allocation

Cited by 318 publications

References 0 publications

Robust portfolios: contributions from operations research and finance

Robust portfolios: contributions from operations research and finance

Comparison of different copula assumptions and their application in portfolio construction

A general approach to Bayesian portfolio optimization

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