Analisis Risiko Investasi Saham Syariah Dengan Model Value AT Risk-Asymmetric Power Autoregressive Conditional Heterocedasticity (VaR-APARCH)

Hidayatullah, Syarif; Qudratullah, Mohammad Farhan

doi:10.14421/fourier.2017.61.37-43

Cited by 1 publication

(4 citation statements)

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“…investing in bitcoin in the future. This is supported by research (Hidayatullah and Qudratullah, 2017) which found APARCH (1,1) model which is used to analyze the risk of investing in Islamic stocks in the next 10 days. And also supported by research (Stavroyiannis, 2016) which shows that APARCH (1,1) model with the standardized Pearson distribution is quite accurate in modeling financial risk, able to analyze finances using the skewed distribution in investment risk management tools.…”

Section: Conclusion and Suggestionsmentioning

confidence: 78%

“…Return is the result of the rate of return obtained as a result of the investment made. There are two types of returns that are used to calculate risk, namely simple net return ( ) and log return ( ) (Hidayatullah & Qudratullah, 2017).…”

Section: B Methodsmentioning

confidence: 99%

“…Other research also shows that the best model results to calculate the risk of Var-APARCH (1,1) (Hidayatullah & Qudratullah, 2017). And found that the APARCH (1,1) model with the student-t distribution are able to model volatility at five foreign exchange selling rates against the rupiah (Nugroho & Susanto, 2017), including also capable to model the stock market volatility (Conrad, Karanasos, & Zeng, 2011), and able to calculate risk in the commodity market using risk measurement tools (VaR), namely the ARCH and APARCH models (Giot & Laurent, 2013).…”

Section: A Introductionmentioning

confidence: 95%

“…Therefore, we need a way to find out how much risk will be borne in investing. Thus, the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model is one of the models used to analyze investment risk (Hidayatullah & Qudratullah, 2017) (Gunay & Khaki, 2018).…”

Section: A Introductionmentioning

confidence: 99%

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Investment Risk Analysis On Bitcoin With Applied of VaR-APARCH Model

Kasse

Mariani

Utari

et al. 2021

JTAM

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Investment can be defined as an activity to postpone consumption at the present time with the aim to obtain maximum profits in the future. However, the greater the benefits, the greater the risk. For that we need a way to predict how much the risk will be borne. Modelling data that experiences heteroscedasticity and asymmetricity can use the Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) model. This research discusses the time series data risk analysis using the Value at Risk-Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCH) model using the daily closing price data of Bitcoin USD period January 1 2019 to 31 December 2019. The best APARCH model was chosen based on the value of Akaike's Information Criterion (AIC). From the analysis results obtained the best model, namely ARIMA (6,1,1) and APARCH (1,1) with the risk of loss in the initial investment of IDR 100,000,000 in the next day IDR 26,617,000. The results of this study can be used as additional information and apply knowledge about the risk of investing in Bitcoin with the VaR-APARCH model.

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Section: Conclusion and Suggestionsmentioning

confidence: 78%

Section: B Methodsmentioning

confidence: 99%