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@article{J.Gauss38929, author = {Auliana Hafizhah and Di Asih I Maruddani and Rukun Santoso}, title = {PERBANDINGAN METODE EXPONENTIAL GARCH (EGARCH) DAN GLOSTEN-JAGANNATHAN-RUNKLE GARCH (GJR-GARCH) PADA MODEL VOLATILITAS SAHAM TUNGGAL}, journal = {Jurnal Gaussian}, volume = {13}, number = {1}, year = {2024}, keywords = {Automatic ARIMA, GARCH Asimetris, EGARCH, GJR-GARCH}, abstract = { Financial data, such as stock prices, usually have a tendency to fluctuate rapidly and create a heteroscedastic effect on the variance of residuals. The Covid-19 pandemic that occurred from 2020 to 2022 is one factor that can affect economic movements, especially in Indonesia, and has an impact on the volatility of financial data. The problem of heteroscedasticity can be addressed using the ARCH/GARCH model. However, this model has a weakness in capturing the asymmetry of volatility resulting from good news and bad news. Several models that can overcome the problem of volatility asymmetry are EGARCH and GJR-GARCH. The purpose of this thesis research is to determine the best volatility model using the daily stock price data of PT Bank Rakyat Indonesia (Persero) Tbk. covering the period from February 2020 to February 2023. The result of the asymmetric GARCH model suggests that the ARIMA(2,0,2) EGARCH(1,1) model has an AIC value of -4.8850, and the ARIMA(2,0,2) GJR-GARCH(1,1) model has an AIC value of -4.8907. Therefore, the model with the minimum AIC value, which is the ARIMA(2,0,2) GJR-GARCH(1,1) model, is considered the best model. Furthermore, this model exhibits very good forecast accuracy, as evaluated by the sMAPE value of 5,17%. }, issn = {2339-2541}, pages = {199--209} doi = {10.14710/j.gauss.13.1.199-209}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/38929} }
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Financial data, such as stock prices, usually have a tendency to fluctuate rapidly and create a heteroscedastic effect on the variance of residuals. The Covid-19 pandemic that occurred from 2020 to 2022 is one factor that can affect economic movements, especially in Indonesia, and has an impact on the volatility of financial data. The problem of heteroscedasticity can be addressed using the ARCH/GARCH model. However, this model has a weakness in capturing the asymmetry of volatility resulting from good news and bad news. Several models that can overcome the problem of volatility asymmetry are EGARCH and GJR-GARCH. The purpose of this thesis research is to determine the best volatility model using the daily stock price data of PT Bank Rakyat Indonesia (Persero) Tbk. covering the period from February 2020 to February 2023. The result of the asymmetric GARCH model suggests that the ARIMA(2,0,2) EGARCH(1,1) model has an AIC value of -4.8850, and the ARIMA(2,0,2) GJR-GARCH(1,1) model has an AIC value of -4.8907. Therefore, the model with the minimum AIC value, which is the ARIMA(2,0,2) GJR-GARCH(1,1) model, is considered the best model. Furthermore, this model exhibits very good forecast accuracy, as evaluated by the sMAPE value of 5,17%.
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