Estimating the Value-at-Risk for some stocks at the capital market in Indonesia based on ARMA-FIGARCH models

Lesmana, Eman; Susanti, Dwi; Napitupulu, Herlina; Hidayat, Yuyun

doi:10.1088/1742-6596/909/1/012040

Cited by 9 publications

(12 citation statements)

References 3 publications

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“…If Equation (18) substitutes into equation (13), the efficient weighted portfolio vector is obtained as: (20) Based on the variation value of p  in the interval (20), the vector weight efficient portfolio x can be obtained in which refer to the equation (19).…”

Section: E Investment Portfolio Modelsmentioning

confidence: 99%

Value-at-Risk and Minimum Variance in the Investment Portfolio with Non Constant Volatility

Lesmana

Johansyah

Napitupulu

et al. 2019

ijrte

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One of the criteria for efficient portofilio is that it produces the same level of profit, but with minimum risk.This paper discusses the estimates Value-at-Risk and minimum variance on an investment portfolio.In this case it is assumed that the asset return follows the time series model. Therefore, non-constant meanis estimated using autoregressive moving average (ARMA) models.While non constant volatility is estimated using generalized autoregressive conditionaly heteroscedasticity (GARCH) models. To determine the minimum variance is done using Markowitz's model optimization. Furthermore, Value-at-Risk is calculated based on the values of the mean and minimum variance. The result of return analysis of assets of BBRI, INCI, LPBN, and MPPA, obtained the minimum variance value of 0.011734775, and at the 95% confidence level obtained Value-at-Risk of 0.017873889.The minimum variance and Value-at-Risk are obtained on the vector of the investment weighted composition as x' = (0.092827551, 0.212180907, 0.14631804, 0.548673502). So to get a minimum risk on the investment portfolio consisting of the four assets, the capital allocation must follow the vector of weighted composition produced

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Section: E Investment Portfolio Modelsmentioning

confidence: 99%

Value-at-Risk and Minimum Variance in the Investment Portfolio with Non Constant Volatility

Lesmana

Johansyah

Napitupulu

et al. 2019

ijrte

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“…Estimation of fractional differentiation parameters d m (mstates market index), performed using the maximum likelihood method [3], [17]. Trust hose (1 − α)100% for [11], [17].…”

Section: 1mentioning

confidence: 99%

“…with ψ m0 constants and ψ mg (g = 1, ..., p) and θ mh (h = 1, ..., q)parameter coefficient of the mean model of return market index r mt . Assumed {a mt } residual white noise sequence that has an average of 0 and a certain variance σ 2 am [10], [11]. The stages of the mean modeling process include: (i) model identification, (ii) do parameter estimation, (iii) carry out diagnostic tests, and (iv) do forecasting [7], [17].…”

Section: 1mentioning

confidence: 99%

Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach

Lesmana¹,

Susanti²,

Napitupulu³

et al. 2019

JMI

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Investors having an understanding of investment statistics are important. Especially quantitative tools related to investment risk measurement. Value-at-Risk Adjusted is one of the investment risk measurement tools, which assumes that returns are not normally distributed.This paper intends to measure investment risk based onValue-at-Risk Adjustedor called Modified Value-at-Risk under the Capital Asset Pricing Model. It is assumed that the return of the market index has a non-constant average and there is a long memory effect. The average of the return of the market index is estimated using ARFIMA models.It is also assumed that the stock risk premium correlates with market risk premiums, and stock risk premiums some time before. The correlation will be analyzed using the ARMAX-GARCH model approach. The Modified Value-at-Risk was then formulated based on the Capital asset Pricing Model with the ARMAX-GARCH model approach.To measure the performance of Modified Value-at-Risk that has been formulated is done with back testing. Back testing is carried out based on the Lopez II method. As a case study, analyzed some data on 10 stocks traded on the capital market in Indonesia.The results of the analysis show that the market index return risk premium significantly follows the ARFIMA model, and the 10 share risk premium significantly follows the ARMAX-GARCH model. Based on the results of back testing calculations indicate that the Value-at-Risk Adjustedor Modified Value-at-Risk is very suitable to be used to measure investment risk in the 10 stocks analyzed.

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“…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]. The formation of a portfolio generally will provide optimum results, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In the portfolio optimization process, a covariance matrix is formed which is obtained from the estimated covariance of each share [10]. Next, the inverse of the covariance matrix is determined.…”

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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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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Estimating the Value-at-Risk for some stocks at the capital market in Indonesia based on ARMA-FIGARCH models

Cited by 9 publications

References 3 publications

Value-at-Risk and Minimum Variance in the Investment Portfolio with Non Constant Volatility

Value-at-Risk and Minimum Variance in the Investment Portfolio with Non Constant Volatility

Estimation of Value-at-Risk Adjusted under the Capital Asset Pricing Model Based on ARMAX-GARCH Approach

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

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