Prediksi Volatilitas Indeks Harga Saham Gabungan Menggunakan GARCH


Authors

  • Beno Jange STMIK Dharmapala Riau, Pekanbaru, Indonesia

DOI:

https://doi.org/10.47065/arbitrase.v4i1.1122

Keywords:

Prediction; Volatility; JKSE; GARCH; Time Series

Abstract

This study aims to determine the Volatility Prediction of the Indonesia Composite Index using GARCH. GARCH is a model for generating forecasts based on historical data using a function of its own past lag plus past innovations. This study uses quantitative methods. The data in this study are JKSE data for 11 (eleven) years, from 01-01-2012 to 31-12-2022. The volatility prediction is carried out for a year, namely in 2022. The results of this study indicate that the prediction accuracy is quite good with a MAPE of 17.26 percent. It was also found that volatility was quite pronounced, which rose significantly in May 2022 due to the effect of the absence of restrictions on the Eid holiday and decreased significantly in July 2022 due to the government extending the implementation of restrictions on community activities (PPKM) throughout Indonesia and dropping even more sharply due to the government increasing prices of Pertalite in September 2022.

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References

Abdalla, Suliman Zakaria Suliman, & Winker, Peter (2012). Modelling stock market volatility using univariate GARCH models: Evidence from Sudan and Egypt. International Journal of Economics and Finance, 4(8), 161-176.

Ahmed, M. Tuhin, & Naher, Nurun (2021). Modelling & Forecasting Volatility of Daily Stock Returns Using GARCH Models: Evidence from Dhaka Stock Exchange. Economics and Business Quarterly Reviews, 4(3).

Ali, Farman, Suri, Pradeep, Kaur, Tarunpreet, & Bisht, Deepa (2022). Modelling time-varying volatility using GARCH models: evidence from the Indian stock market. F1000Research, 11.

Awalludin, S. A., Ulfah, S., & Soro, S (2018). Modeling the stock price returns volatility using GARCH (1, 1) in some Indonesia stock prices. In Journal of Physics: Conference Series (Vol. 948, No. 1, p. 012068). IOP Publishing.

Awartani, Basel M., & Corradi, Valentina (2005). Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries. International Journal of forecasting, 21(1), 167-183.

Black, Fischer (1976). Studies of stock price volatility changes, proceedings of the 1976 meetings of the business and economic statistics section. 177-191. In American Statistical association (pp. 307-327). sn.

Bollerslev, Tim (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.

Cheteni, Priviledge (2016). Stock market volatility using GARCH models: Evidence from South Africa and China stock markets.

Dickey, David A., & Fuller, Wayne A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74: 427–431

Desvina, Ari Pani, & Rahmah, Nadyatul (2016). Penerapan Metode ARCH/GARCH Dalam Peramalan Indeks Harga Saham Sektoral. Jurnal Sains Matematika dan Statistika, Vol. 2, No. I.

Engle, Robert F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50, 987–1007.

Grek, Åssa (2014). Forecasting accuracy for ARCH models and GARCH (1, 1) family: Which model does best capture the volatility of the Swedish stock market?.

Koima, J. K., Mwita, P. N., & Nassiuma, D. K. (2015). Volatility estimation of stock prices using Garch method.

Kazungu, Khatibu, & Mboya, John R. (2021). Volatility of Stock Prices in Tanzania: Application of Garch Models to Dar Es Salaam Stock Exchange. Asian Journal of Economic Modelling, 9(1), 15-28.

Lim, Ching Mun, & Sek, Siok Kun (2013). Comparing the performances of GARCH-type models in capturing the stock market volatility in Malaysia. Procedia Economics and Finance, 5, 478-487.

Marvillia, Bunga Lety (2013). Pemodelan dan Peramalan Penutupan Harga Saham PT. Telkom dengan Metode ARCH-GARCH. MATHunesa: Jurnal Ilmiah Matematika, 1(1).

Miah, M., & Rahman, A. (2016). Modelling volatility of daily stock returns: Is GARCH (1, 1) enough. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 18(1), 29-39.

Namugaya, Jalira, Weke, Patrick GO, & Charles, Wilson M. (2014). modelling stock returns volatility on Uganda securities exchange.

Ugurlu, Erginbay, Thalassinos, Eleftherios, & Muratoglu, Yusuf (2014). Modeling volatility in the stock markets using GARCH models: European emerging economies and Turkey.

Raneo, Agung Putra & Muthia, Fida (2018). Penerapan Model GARCH Dalam Peramalan Volatilitas di Bursa Efek Indonesia. Jurnal Manajemen dan Bisnis Sriwijaya, 16(3), 194-202.

Sharma, Prateek (2015). Forecasting stock index volatility with GARCH models: international evidence. Studies in Economics and Finance, 32(4), 445-463.

Wong, Yoke Chen, & Kok, Kim Lian (2005). A comparison forecasting models for ASEAN equity markets. Sunway Academic Journal, 2, 1-12.


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Published: 2023-07-31
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