Portfolio Optimization by Mean-Value at Risk Framework

Banihashemi, Shokufeh; Azarpour, Ali Moayedi; Navvabpour, Hamidreza

doi:10.18576/amis/100535

Cited by 8 publications

(8 citation statements)

References 11 publications

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“…The material for modeling the quadratic investment portfolio optimization Mean-VaR with risk-free assets refers to the research papers conducted by Gaivoronski and Pflug (2005), Sukono et al (2017.a), Hashemi et al (2016), and Sukono et al (2017.b). Furthermore, the data analyzed consists of 11 selected mining and energy sector stocks, which include the prices of shares: BSSR, BYAN, CITA, HRUM, MBAP, MDKA, MEDC, PSAB, PTBA, PTRO, and RUIS.…”

Section: Methodsmentioning

confidence: 99%

“…The result of portfolio optimization modeling is a weight vector equation that depends on the mean vector return asset vector, the identity vector, and the matrix covariance between asset returns, as well as the risk tolerance factor. Hashemi et al (2016), conducted a research with the aim of evaluating various measurement tools to improve portfolio performance and asset selection using the Mean-VaR model. This paper focuses on the portfolio optimization process where the variance is replaced by risk (VaR).…”

Section: Introductionmentioning

confidence: 99%

“…The research conducted here is seen as a difference compared to the results of some of the studies that have been done above. Researches conducted by Gaivoronski and Pflug (2005) and Hashemi et al (2016) have shown how important it is to use the VaR model for measuring financial risk, especially in investment portfolio analysis. Furthermore, in a research conducted by Sukono et al (2017.a;2017.b) the Mean-VaR portfolio optimization model has been formulated, but the investment portfolio analyzed does not involve risk-free assets.…”

Section: Introductionmentioning

confidence: 99%

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Quadratic Investment Portfolio Based on Value-at-Risk With Risk-Free Assets: For Stocks of the Mining and Energy Sector

Pandiangan

Hasbullah²

2021

IJEEP

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The mining and energy sector is still the driving force for economic development and community empowerment, especially around mining and energy activities. Therefore, increased investment in the mining and energy sectors needs to be increased and balanced with stricter safety and environmental policies. This paper aims to formulate a quadratic investment portfolio optimization model, and apply it to several stocks in the mining and energy sectors. In this paper, it is assumed that risk is measured using Value-at-Risk (VaR), so that the optimization modeling is carried out using the quadratic investment portfolio approach to the Mean-VaR model with risk-free assets. Furthermore, the model is used to determine the efficient portfolio surface based on several values of risk aversion levels. Based on the results of the analysis, it is found that an efficient portfolio surface has a minimum portfolio return value with an average of 0.766522 and a VaR risk of 0.038687. In addition, the results of the analysis can be concluded that the greater the level of risk aversion, the smaller the VaR value, which is followed by the smaller the portfolio average value.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quadratic Investment Portfolio Based on Value-at-Risk With Risk-Free Assets: For Stocks of the Mining and Energy Sector

Pandiangan

Hasbullah²

2021

IJEEP

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“…The previous developments of the Markowitz model still use the variance of the portfolio return as a minimized objective [15]. The variance measure can be substituted for the maximum possible loss.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Mean-Value-at-Risk Investment Portfolio Optimization Considering Liabilities and Risk-Free Assets

Sukono,

Ghazali,

Johansyah

et al. 2024

Computation

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This paper aims to design a quadratic optimization model of an investment portfolio based on value-at-risk (VaR) by entering risk-free assets and company liabilities. The designed model develops Markowitz’s investment portfolio optimization model with risk aversion. Model development was carried out using vector and matrix equations. The entry of risk-free assets and liabilities is essential. Risk-free assets reduce the loss risk, while liabilities accommodate a fundamental analysis of the company’s condition. The model can be applied in various sectors of capital markets worldwide. This study applied the model to Indonesia’s mining and energy sector. The application results show that risk aversion negatively correlates with the mean and VaR of the return of investment portfolios. Assuming that risk aversion is in the 5.1% to 8.2% interval, the maximum mean and VaR obtained for the next month are 0.0103316 and 0.0138270, respectively, while the minimum mean and VaR are 0.0102964 and 0.0137975, respectively. The finding of this study is that the vector equation for investment portfolio weights is obtained, which can facilitate calculating investment portfolio weight optimization. This study is expected to help investors control the quality of appropriate investment, especially in some stocks in Indonesia’s mining and energy sector.

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“…Deng et al (2009) have considered the portfolio optimization problems based on expected shortfall risk measures to get useful results and claimed that it is a natural generalization of the classical Markowitz’s portfolio optimization problem under the normal distribution. Banihashemi et al (2016) provided a detailed discussion on portfolio optimization by the mean-value-at-risk framework. Scheller and Auer (2016) found that an optimal portfolio allocation may not be very sensitive to the choice of the value-at-risk estimator.…”

Section: Introductionmentioning

confidence: 99%

Portfolio optimization based on modified expected shortfall

Jadhav

Ramanathan

2019

SEF

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Purpose An investor is expected to analyze the market risk while investing in equity stocks. This is because the investor has to choose a portfolio which maximizes the return with a minimum risk. The mean-variance approach by Markowitz (1952) is a dominant method of portfolio optimization, which uses variance as a risk measure. The purpose of this paper is to replace this risk measure with modified expected shortfall, defined by Jadhav et al. (2013). Design/methodology/approach Modified expected shortfall introduced by Jadhav et al. (2013) is found to be a coherent risk measure under univariate and multivariate elliptical distributions. This paper presents an approach of portfolio optimization based on mean-modified expected shortfall for the elliptical family of distributions. Findings It is proved that the modified expected shortfall of a portfolio can be represented in the form of expected return and standard deviation of the portfolio return and modified expected shortfall of standard elliptical distribution. The authors also establish that the optimum portfolio through mean-modified expected shortfall approach exists and is located within the efficient frontier of the mean-variance portfolio. The results have been empirically illustrated using returns from stocks listed in National Stock Exchange of India, Shanghai Stock Exchange of China, London Stock Exchange of the UK and New York Stock Exchange of the USA for the period February 2005-June 2018. The results are found to be consistent across all the four stock markets. Originality/value The mean-modified expected shortfall portfolio approach presented in this paper is new and is a natural extension of the Markowitz’s mean-variance and mean-expected shortfall portfolio optimization discussed by Deng et al. (2009).

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Portfolio Optimization by Mean-Value at Risk Framework

Cited by 8 publications

References 11 publications

Quadratic Investment Portfolio Based on Value-at-Risk With Risk-Free Assets: For Stocks of the Mining and Energy Sector

Quadratic Investment Portfolio Based on Value-at-Risk With Risk-Free Assets: For Stocks of the Mining and Energy Sector

Modeling of Mean-Value-at-Risk Investment Portfolio Optimization Considering Liabilities and Risk-Free Assets

Portfolio optimization based on modified expected shortfall

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