Selecting a Portfolio with Skewness: Recent Evidence from US, European, and Latin American Equity Markets

Prakash, Arun J.; Chang, Chun‐Hao; Pactwa, Therese E.

doi:10.2139/ssrn.2663177

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…Portfolio optimization involves balancing reward and risk in financial assets [25][26][27]. The classical mean-variance model is used for portfolio selection, but its asymmetric distributions and excess kurtosis have led to criticism of variance as a risk measure [28][29][30][31]. Markowitz suggested a semi-variance risk measure to measure variability below the mean.…”

Section: Mean-value-at-risk (Mean-var) Portfolio Optimization Model W...mentioning

confidence: 99%

Mean-Value-at-Risk Portfolio Optimization Based on Risk Tolerance Preferences and Asymmetric Volatility

Hidayat,

Purwandari,

Sukono

et al. 2023

Mathematics

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Investors generally aim to obtain a high return from their stock portfolio. However, investors must realize that a high value-at-risk (VaR) is essential to calculate for this aim. One of the objects in the VaR calculation is the asymmetric return volatility of stocks, which causes an unbalanced decrease and increase in returns. Therefore, this study proposes a mean-value-at-risk (mean-VaR) stock portfolio optimization model based on stocks’ asymmetric return volatility and investors’ risk aversion preferences. The first stage is the determination of the mean of all stocks in the portfolio conducted using the autoregressive moving average Glosten–Jagannathan–Runkle generalized autoregressive conditional heteroscedasticity (ARMA-GJR-GARCH) models. Then, the second stage is weighting the capital of each stock based on the mean-VaR model with the investors’ risk aversion preferences. This is conducted using the Lagrange multiplier method. Then, the model is applied to stock data in Indonesia’s capital market. This application also analyzed the sensitivity between the mean, VaR, both ratios, and risk aversion. This research can be used for investors in the design and weighting of capital in a stock portfolio to ensure its asymmetrical effect is as small as possible.

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Section: Mean-value-at-risk (Mean-var) Portfolio Optimization Model W...mentioning

confidence: 99%

Mean-Value-at-Risk Portfolio Optimization Based on Risk Tolerance Preferences and Asymmetric Volatility

Hidayat,

Purwandari,

Sukono

et al. 2023

Mathematics

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“…However, these two conditions are not satisfied. Many researchers have showed that the assets returns distribution are asymmetric and exhibit skewness, see Tobin (1958), Arditti (1971, Chunhachinda et al (1997) and Prakash et al (2003). These authors have proposed a DownSide Risk (DSR) measures such as Semivariance (SV) and conditional value at risk (CVaR).…”

Section: Introductionmentioning

confidence: 99%

Conditional mean-variance and mean-semivariance models in portfolio optimization

Salah

Gannoun²,

Ribatet³

2020

Journal of Statistics and Management Systems

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In this paper, we consider the problem of portfolio optimization. The risk will be measured by conditional variance or semivariance. It is known that the historical returns used to estimate expected ones provide poor guides to future returns. Consequently, the optimal portfolio asset weights are extremely sensitive to the return assumptions used. Getting informations about the future evolution of different asset returns, could help the investors to obtain more efficient portfolio. The solution will be reached under conditional mean estimation and prediction. This strategy allows us to take advantage from returns prediction which will be obtained by nonparametric univariate methods. Prediction step uses kernel estimation of conditional mean. Application on Chinese and American markets are presented and discussed.

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“…Following Briec et al (2007), it is possible to distinguish between primal and dual methods to characterize Mean-Variance-Skewness (MVS) optimal portfolios. 1 For instance, Lai (1991) proposed a primal approach determining MVS portfolios via multi-objective programming that enjoyed quite some popularity in empirical studies (see, e.g., Chunhachinda et al (1997), Prakash et al (2003), and Sun and Yan (2003)). More recently, Yu et al (2008) develop an alternative MVS method based upon neural networks which converge relatively more quickly compared with similar multi-objective optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Geometric representation of the mean–variance–skewness portfolio frontier based upon the shortage function

Kerstens

Mounir

Woestyne³

2011

European Journal of Operational Research

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The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine meanvariance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimal portfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the geometric representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop geometric representations of the MVS portfolio frontier using the shortage function and related approaches.

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Selecting a Portfolio with Skewness: Recent Evidence from US, European, and Latin American Equity Markets

Cited by 10 publications

References 24 publications

Mean-Value-at-Risk Portfolio Optimization Based on Risk Tolerance Preferences and Asymmetric Volatility

Mean-Value-at-Risk Portfolio Optimization Based on Risk Tolerance Preferences and Asymmetric Volatility

Conditional mean-variance and mean-semivariance models in portfolio optimization

Geometric representation of the mean–variance–skewness portfolio frontier based upon the shortage function

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