Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance

Zakamouline, Valeri; Koekebakker, Steen

doi:10.1016/j.jbankfin.2009.01.005

Cited by 172 publications

(44 citation statements)

References 41 publications

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“…Additionally Knoke and Wurm (2006) proved that assuming a normal distribution is adequate neither for European beech nor for Norway spruce or mixtures. The assumption of normally distributed revenues are criticized (Gotoh and Konno, 2000), in particular that the use of the SR P with non-normal returns can result in misleading conclusions (Zakamouline and Koekebakker, 2009). This is why many researchers replace the standard deviation by an alternative risk measure.…”

Section: Discussionmentioning

confidence: 99%

Financial consequences of losing admixed tree species: A new approach to value increased financial risks by ungulate browsing

Clasen

Griess

Knoke

2011

Forest Policy and Economics

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The influence of ungulates on the growth of young trees is amply discussed in forestry literature, particularly game browsing. Although there are a few appraisal methods that consider negative long-term influences on forest regeneration, hitherto no approach has addressed the financial consequences of lost admixed tree species. A homogenised species composition may lead to an increased financial risk of a forest. Based on financial return and risk ratios of mixed forests, this paper derives the financial compensation that would be necessary to make acceptable the increased risk of pure forest for forest owners. In this conceptual case study we consider a two-species mixed forest (Norway spruce, Picea abies

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Section: Discussionmentioning

confidence: 99%

Financial consequences of losing admixed tree species: A new approach to value increased financial risks by ungulate browsing

Clasen

Griess

Knoke

2011

Forest Policy and Economics

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“…On the other hand, it naturally fits into the robust optimization framework. Hodges (1998) and Zakamouline and Koekebakker (2009) define a "Generalized Sharpe Ratio" that takes into account the skewness (third moment) and kurtosis (fourth moment) of the observed historical return distribution. Lo (2002) derives the statistical behavior of observed Sharpe ratios under the assumption that returns are normally distributed.…”

Section: Existing Literaturementioning

confidence: 99%

Robust portfolio optimization with Value-at-Risk-adjusted Sharpe ratios

Deng¹,

Dulaney²,

McCann³

et al. 2013

J Asset Manag

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We propose a robust portfolio optimization approach based on Value-at-Risk (VaR) adjusted Sharpe ratios. Traditional Sharpe ratio estimates using a limited series of historical returns are subject to estimation errors. Portfolio optimization based on traditional Sharpe ratios ignores this uncertainty and, as a result, is not robust. In this paper, we propose a robust portfolio optimization model that selects the portfolio with the largest worse-case-scenario Sharpe ratio within a given confidence interval. We show that this framework is equivalent to maximizing the Sharpe ratio reduced by a quantity proportional to the standard deviation in the Sharpe ratio estimator. We highlight the relationship between the VaR-adjusted Sharpe ratios and other modified Sharpe ratios proposed in the literature. In addition, we present both numerical and empirical results comparing optimal portfolios generated by the approach advocated here with those generated by both the traditional and the alternative optimization approaches.

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“…In order to evaluate the performance of portfolio models, a number of alternative performance measures have been proposed in the literature [31][32][33][34][35][36][37][38]. In this study, we consider some of these performance measures.…”

Section: Portfolio Performance Measuresmentioning

confidence: 99%

“…Based on three different empirical datasets, we evaluate the out-of-sample performance of MVSEM relative to well-known portfolio models such as the equally weighted model (EWM), minimum variance model (MinVM), MVM and MVSM. The performances of the MVSEM are assessed in terms of the following portfolio performance measures [31][32][33][34][35][36][37][38]: the Sharpe ratio (SR), adjusted for skewness Sharpe ratio (ASR), mean absolute deviation ratio (MADR) Sortino-Satchell ratio (SSR), Farinelli-Tibiletti ratio (FTR), generalized Rachev ratio (GRR) and portfolio turnover (PT). We also compute Jobson and Korkie' z JK test statistic to evaluate the statistical significance for the difference in Sharpe ratios among the considered model in this study.…”

Section: Introductionmentioning

confidence: 99%

Mean-Variance-Skewness-Entropy Measures: A Multi-Objective Approach for Portfolio Selection

Usta

Kantar

2011

Entropy

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Abstract:In this study, we present a multi-objective approach based on a mean-varianceskewness-entropy portfolio selection model (MVSEM). In this approach, an entropy measure is added to the mean-variance-skewness model (MVSM) to generate a well-diversified portfolio. Through a variety of empirical data sets, we evaluate the performance of the MVSEM in terms of several portfolio performance measures. The obtained results show that the MVSEM performs well out-of sample relative to traditional portfolio selection models.

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Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance

Cited by 172 publications

References 41 publications

Financial consequences of losing admixed tree species: A new approach to value increased financial risks by ungulate browsing

Financial consequences of losing admixed tree species: A new approach to value increased financial risks by ungulate browsing

Robust portfolio optimization with Value-at-Risk-adjusted Sharpe ratios

Mean-Variance-Skewness-Entropy Measures: A Multi-Objective Approach for Portfolio Selection

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