A Global Optimization Heuristic for Portfolio Choice with VaR and Expected Shortfall

Gilli, Manfred; Këllezi, Evis

doi:10.1007/978-1-4757-3613-7_9

Cited by 44 publications

(22 citation statements)

References 14 publications

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“…Since these algorithms involve iterative computation, they are quite time-consuming and would have been infeasible without the aid of mighty computation ability. For instance, Gilli and Kellezi (2001) employ a global search approach to optimizing a portfolio's weights on individual assets. Maringer and Parpas (2009) apply a global search algorithm to optimize the higher order moments in portfolio selection.…”

Section: Portfolio Optimizationmentioning

confidence: 99%

Can hedge fund elites consistently beat the benchmark? A study of portfolio optimization

Yen

Hsu

Hsiao

2015

Asia Pacific Management Review

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a b s t r a c tThis study aims to explore whether a regularly updated portfolio of outperforming hedge funds can consistently beat the corresponding hedge fund dataset index. If yes, moreover, the second question concerns whether portfolio optimization approaches can lead to an even better performance than the naïve equal-weighting method. The dataset spans the January-1994 to August-2008 period and is classified into four main categories -Macro, Equity Hedge, Relative Value and Event Driven. Based on a seven-factor model, this study applies the Step-SPA test to each category of funds and examines the statistical significance of the studentized fund alpha over the selection period of 3e7 years in length. A 'winner' portfolio of funds, namely, consisting of funds with statistically significant, positive studentized alpha, can be formed at the end of the selection period and held for 1 up to 3 years. We find that the winner portfolio tends to beat the dataset indexes during the holding period, irrespective of the time span for the selection and the holding periods investigated. Moreover, two of the three optimization approaches employed, the Probabilistic Global Search Lausanne and the Genetic Algorithm, prove to further enhance the performance of the equal-weighted winning portfolio.

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Section: Portfolio Optimizationmentioning

confidence: 99%

Can hedge fund elites consistently beat the benchmark? A study of portfolio optimization

Yen

Hsu

Hsiao

2015

Asia Pacific Management Review

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“…This method seems could be salient and an efficient method applied in portfolio problem but we would that it has had some limitations for the various markets with hard boundaries and still has a problem in scalable uncertain markets. Besides, authors in [5] exploit threshold policy to control portfolio optimization and applied VaR, ES, mean absolute semi deviation and semi variance for risk measurements. In addition, [24] used quadratic programming for portfolio selection.…”

Section: Related Workmentioning

confidence: 99%

“…This constraint leads to introducing integer variables. Therefore, the results of a mixed integer optimization problem are multiple local exterma and discontinuities [5][6][7]. Indeed, authors in [7] present GAMCC as a genetic-aware credit crunch constraint applied in an intelligent model in bank lending decisions.…”

Section: Introductionmentioning

confidence: 99%

2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization

Eftekharian¹,

Shojafar

Shamshirband

2017

Algorithms

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Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non-convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA). In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm.

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“…Some solution methods can be found e.g. in Pflug (2000); Andersson et al (2001); Gilli and Kellezi (2002); Larsen et al (2002); Pang and Leyffer (2004) and references therein. But they do not guarantee the globality of computed solutions.…”

Section: Introductionmentioning

confidence: 98%

DC programming and DCA for globally solving the value-at-risk

Dinh

Nam

Thi³

2009

Comput Manag Sci

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A Global Optimization Heuristic for Portfolio Choice with VaR and Expected Shortfall

Cited by 44 publications

References 14 publications

Can hedge fund elites consistently beat the benchmark? A study of portfolio optimization

Can hedge fund elites consistently beat the benchmark? A study of portfolio optimization

2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization

DC programming and DCA for globally solving the value-at-risk

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