Mean-Variance portfolio optimization by using non constant mean and volatility based on the negative exponential utility function

Soeryana, Endang; Halim, Nurfadhlina Abdul; Rusyaman, Endang; Supian, Sudradjat

doi:10.1063/1.4979458

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…But it is different for matrices of size m × n or n × n singular which do not have an inverse. But it can be done by generalizing from a singular inverse matrix m × n or n × n [5] [7]. Generalization of singular matrix inverse can be done using the pseudo inverse method.…”

Section: Pseudo Inversmentioning

confidence: 99%

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Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix

Kalfin¹,

Carnia²

2019

IJRTE

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Abstract: Investing in the stock sector, investors often face risk problems. Usually, forming an investment portfolio is done to minimize risk. In this research, investment portfolio optimization is discussed. The data analyzed are 8 shares traded on the capital market in Indonesia through the Indonesia Stock Exchange (IDX). Optimization is performed using the Mean-Absolute Deviation model with the singular covariance matrix to determine the optimal weights. The results of portfolio optimization Mean-Absolute Deviation model with singular covariance matrix method, was obtained optimal portfolio weights that is of 17.22% for BBCA shares; 26.64% for TKIM shares; 9.96% for BBRI shares; 9.96% for BBNI shares; 8.70% for BMRI shares; 3.75% for ADRO shares; 6.52% for GGRM shares; and 17.25% for UNTR shares. Where the optimal portfolio composition is obtained the expected rate of return (expected return) of 0.18% with a portfolio risk level (standard deviation) of 0.07%.

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Section: Pseudo Inversmentioning

confidence: 99%

“…Stocks that provide higher investment return expectations will be chosen by investors [6]. Therefore, every investment has a risk, investors should not only consider high returns [7] [8]. In investing, every investor has a risk tolerance under their respective preferences [9] [10].…”

Section: Introductionmentioning

confidence: 99%

Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix

Kalfin¹,

Carnia²

2019

IJRTE

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“…In 2023, Ref. [20] used a simpler method, the Daftardar-Geijji method, to solve the Fractional Black-Scholes equation. It also discusses the problem of existence and the uniqueness of solutions of the Fractional Black-Scholes equation.…”

Section: Introductionmentioning

confidence: 99%

Using a Mix of Finite Difference Methods and Fractional Differential Transformations to Solve Modified Black–Scholes Fractional Equations

Sugandha,

Rusyaman,

Sukono

et al. 2024

Mathematics

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This paper discusses finding solutions to the modified Fractional Black–Scholes equation. As is well known, the options theory is beneficial in the stock market. Using call-and-pull options, investors can theoretically decide when to sell, hold, or buy shares for maximum profits. However, the process of forming the Black–Scholes model uses a normal distribution, where, in reality, the call option formula obtained is less realistic in the stock market. Therefore, it is necessary to modify the model to make the option values obtained more realistic. In this paper, the method used to determine the solution to the modified Fractional Black–Scholes equation is a combination of the finite difference method and the fractional differential transformation method. The results show that the combined method of finite difference and fractional differential transformation is a very good approximation for the solution of the Fractional Black–Scholes equation.

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Optimization of the mean-absolute deviation portfolio investment in some mining stocks using the singular covariance matrix method

Kalfin

Carnia

2019

J. Phys.: Conf. Ser.

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Investing in mining stocks, investors often face risk problems. Usually to minimize risk, it is done by forming an investment portfolio. This paper aims to discuss the optimization of the investment portfolio. The data analyzed are several mining stocks traded on the capital market in Indonesia. Optimization is done using the mean-absolute deviation model with the singular covariance matrix method and the non-singular covariance matrix method to determine the optimal weight of the two existing methods. Based on the results of the optimization, we can obtain a weight allocation composition that provides an optimal portfolio. In addition, we also estimate the amount of return on expectations and risks in the optimal portfolio formed. So that the composition of this optimal weight can be used as a consideration for investors in investing their capital in several analyzed mining stocks.

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Mean-Variance portfolio optimization by using non constant mean and volatility based on the negative exponential utility function

Abstract: Estimation of value at risk in currency exchange rate portfolio using asymmetric GJR-GARCH Copula AIP Conference Proceedings 1827, 020006 (2017)

Cited by 5 publications

References 4 publications

Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix

Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix

Using a Mix of Finite Difference Methods and Fractional Differential Transformations to Solve Modified Black–Scholes Fractional Equations

Optimization of the mean-absolute deviation portfolio investment in some mining stocks using the singular covariance matrix method

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