Hedge Fund Portfolio Selection with Higher‐order Moments: A Nonparametric Mean–Variance–Skewness–<i>Kurtosis</i>Efficient Frontier

Jurczenko, Emmanuel; Maillet, Bertrand; Merlin, Pierre

doi:10.1002/9781119201830.ch3

Cited by 8 publications

(7 citation statements)

References 27 publications

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“…The variance or entropy-based portfolio selection model depends only on the first-and second-order moments of return distributions. Many researchers (see [24], [41]- [44]) have argued that higher-order moments cannot be ignored unless there has enough evidence to prove that the return of each alternative is symmetrically distributed (e.g. normal) or that the investors' decision is independent to higher-order moments (e.g.…”

Section: Higher-order Fuzzy Momentsmentioning

confidence: 99%

“…normal) or that the investors' decision is independent to higher-order moments (e.g. skewness [24], [42]- [44], kurtosis [41], [44]). Especially when the first-and second-order moments of investment alternatives are the same, the higher-order moments are bound to become the decisive index, and almost all the investors will choose the portfolio with larger third-order moment or smaller fourthorder moment.…”

Section: Higher-order Fuzzy Momentsmentioning

confidence: 99%

“…The current hype over hedge funds provides a compelling evidence [41], [45] of how dangerous it can be to ignore the fourth-order central moment, i.e., kurtosis. Therefore, kurtosis risk should be integrated into the decision-making approach.…”

Section: ) Fuzzy Kurtosismentioning

confidence: 99%

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Decision-Making Model to Portfolio Selection Using Analytic Hierarchy Process (AHP) With Expert Knowledge

Zhao

Jia

2021

IEEE Access

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On the premise of ensuring profits, how to give a relatively dispersed portfolio selection result reasonably and rapidly is a challenging problem in both theory and practice. Although the use of optimization models to make decision has been shown to be an essential approach towards portfolio selection, there still has an acute need for developing a knowledge-based expert model for portfolio selection so that this model can achieve better performance in reliability and real time, especially in leading more distributed investments. In this paper, a knowledge-based expert model is proposed for portfolio selection with the aid of analytic hierarchy process (AHP) and fuzzy sets. In the proposed model, the expert knowledge which can reflect the investment attitude and experience of different investors is mainly integrated into the criterion layer and represented by a reciprocal matrix, and the scheme layer is abstracted to a strictly consistent matrix by comparing and analyzing the state characteristics of investment objects. In order to characterize the state characteristics of investment objects under fuzzy environment, their corresponding time series data are quantified as fuzzy variables in advance. Experiments involving synthetic and real-world data demonstrate that the proposed model produces better performance than other typical portfolio selection models and gives more distributed investments.INDEX TERMS Decision-making, portfolio selection, analytic hierarchy process (AHP), consistency, expert knowledge.

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Section: Higher-order Fuzzy Momentsmentioning

confidence: 99%

Section: Higher-order Fuzzy Momentsmentioning

confidence: 99%

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Decision-Making Model to Portfolio Selection Using Analytic Hierarchy Process (AHP) With Expert Knowledge

Zhao

Jia

2021

IEEE Access

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“…In contrast, the evidence on kurtosis preferences is far more complicated 3 -see for example Haas (2007). However, when portfolio optimization with higher-order moments is performed (see for example Jurczenko et al (2012)), kurtosis is usually minimized, suggesting that lower kurtosis is preferred (Maringer and Parpas (2009)).…”

Section: At >mentioning

confidence: 99%

“…;Jurczenko et al (2012);Lai et al (2006);Maringer and Parpas (2009).Lemma 5.4.5 compares the skewness 2 of the target terminal wealth distributions. Lemma 5.4.5.…”

mentioning

confidence: 98%

Numerical methods for mean-risk portfolio optimization

Staden¹,

Marais²

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We consider various aspects of the dynamic mean-risk portfolio optimization problem, with specific focus on the numerical methods to solve this problem in the case where the risk measure is either the variance of wealth outcomes or the quadratic variation of the wealth process. In the case where the risk measure is the variance of wealth outcomes, the mean-risk problem is known as the dynamic mean-variance (MV) portfolio optimization problem. Since variance is not separable in the sense of dynamic programming, the MV objective violates Bellman's principle of optimality. As a result, the possibility of the time-inconsistency of the associated MV-optimal investment strategies leads to a number of competing approaches to formulating and solving the dynamic MV problem. We primarily focus on the two main approaches, namely the time-consistent MV (TCMV) and the pre-commitment MV (PCMV) optimization problems. In addition, this investigation also leads us to consider related approaches which are not necessarily MV optimal in some sense, such as the Mean-Quadratic Variation (MQV) portfolio optimization problem.

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A Literature Review for Nonparametric Frontier Methods Applied to Portfolio Analysis

Ren,

Xiao,

Zhou

2024

Infosys Science Foundation Series

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Hedge Fund Portfolio Selection with Higher‐order Moments: A Nonparametric Mean–Variance–Skewness–KurtosisEfficient Frontier

Cited by 8 publications

References 27 publications

Decision-Making Model to Portfolio Selection Using Analytic Hierarchy Process (AHP) With Expert Knowledge

Decision-Making Model to Portfolio Selection Using Analytic Hierarchy Process (AHP) With Expert Knowledge

Numerical methods for mean-risk portfolio optimization

A Literature Review for Nonparametric Frontier Methods Applied to Portfolio Analysis

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