Global optimization of higher order moments in portfolio selection

Maringer, Dietmar; Parpas, Panos

doi:10.1007/s10898-007-9224-3

Cited by 93 publications

(40 citation statements)

References 23 publications

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“…The process of finding the optimal parameters of a search heuristic is usually referred to as tuning. Compared to other heuristics, such as GA, DE does not have many parameters and requires very little tuning, as it has been already shown in other studies (Maringer and Parpas 2009;Paterlini and Krink 2006;Krink and Paterlini 2009). …”

Section: Appendix A: Parameter Tuningmentioning

confidence: 98%

Differential evolution and combinatorial search for constrained index-tracking

Krink¹,

Mittnik

Paterlini

2009

Ann Oper Res

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Index-tracking is a low-cost alternative to active portfolio management. The implementation of a quantitative approach, however, is a major challenge from an optimization perspective. The optimal selection of a group of assets that can replicate the index of a much larger portfolio requires both to find the optimal subset of assets and to fine-tune their weights. The former is a combinatorial, the latter a continuous numerical problem. Both problems need to be addressed simultaneously, because whether or not a selection of assets is promising depends on the allocation weights and vice versa. Moreover, the problem is usually of high dimension. Typically, an optimal subset of 30-150 positions out of 100-600 need to be selected and their weights determined. Search heuristics can be a valuable alternative to traditional methods, which often cannot deal with the problem. In this paper, we propose a new optimization method, which is partly based on Differential Evolution (DE) and on combinatorial search. The main advantage of our method is that it can tackle the index-tracking problem as complex as it is, generating accurate and robust results.

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Section: Appendix A: Parameter Tuningmentioning

confidence: 98%

Differential evolution and combinatorial search for constrained index-tracking

Krink¹,

Mittnik

Paterlini

2009

Ann Oper Res

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“…Recently, utility maximization for portfolio choice has again become the focus of research via the development of the method of Full-Scale Optimization (FSO) (see, e.g., Adler and Kritzman, 2006;Gourieroux and Monfort, 2005;Maringer and Parpas, 2009;Sharpe, 2007). But,this development also exemplifies the above mentioned problems.…”

Section: Disposal Representation Setmentioning

confidence: 99%

Portfolio selection in multidimensional general and partial moment space

Briec

Kerstens

2010

Journal of Economic Dynamics and Control

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“…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. Krokhmal, Palmquist, and Uryasev (2002) devise a global search approach to optimizing the expected returns of a portfolio given constraints on the portfolio's conditional value-at-risk.…”

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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Global optimization of higher order moments in portfolio selection

Cited by 93 publications

References 23 publications

Differential evolution and combinatorial search for constrained index-tracking

Differential evolution and combinatorial search for constrained index-tracking

Portfolio selection in multidimensional general and partial moment space

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

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