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PEMODELAN DAN PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN ARIMAX-TARCH

*Endah Fauziyah  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Dwi Ispriyanti  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Tarno Tarno  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Open Access Copyright 2021 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

The Composite Stock Price Index (IHSG) is a value that describes the combined performance of all shares listed on the Indonesia Stock Exchange. JCI serves as a benchmark for investors in investing. The method used to predict future conditions based on past data is forecasting . Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) is amodel time series that can be used for forecasting. Financial data has high volatility which causes the variance of the residual model which is not constant (heteroscedasticity). ARCH / GARCH model is used to solve the heteroscedasticity problem in the model. If the data is heteroscedastic and asymmetric, then the model can be used Threshold Autoregressive Conditional Heteroskedasticity (TARCH). The data used are the Composite Stock Price Index (IHSG) for the January 2000 - April 2020 period and the dollar exchange rate data for the January 2000 - April 2020 period asvariables independent from the ARIMAX model. The best model used to predict the JCI from the results of this study is the ARIMAX (1,1,0) -TARCH (1,2) model with an AIC value of -0.819074.

 

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Keywords: IHSG, forecasting, ARIMAX, TARCH

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  1. Black, F. (1976). Studies of Stock Price Volatility Changes. Proceedings of the Bussiness and Econometrics Section of the American Statistical Association, 177-181
  2. Bollerslev. (1986). Generalize Autoregressive Conditional Heteroskedasticity. Journal of Econometric, 307-327
  3. Brooks, C. (2008). Introductory Econometrics for Finance. New York: Cambridge University Press
  4. Cools, M., Moons, E., & Wets, G. (2009). Investigating The Variability In Daily Traffic Counts Using ARIMAX and SARIMA(X) Models. Transportation Research Institute Hasselt University
  5. Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimation of the Variane of United Kingdom Inflation. Econometrica, Vol. 50, 987-1008
  6. Hamilton, J. D., & Susmel, R. (1994). Autoregressive Conditional Heteroskedasticity and Changes in Regimes. Journal of Econometrics, 307-333
  7. Makridakis, S. d. (1999). Metode dan Aplikasi Peramalan Edisi ke-2. Jakarta: Erlangga
  8. Permadi, H. d. (2013). Peramalan Saham S&P 500 Index Menggunakan Model TARCH. Journal Statistik . Universitas Negeri Malang
  9. Rabemananjara, R., & Zakoian, J. M. (1993). Threshold ARCH Models and Asymmetries in Volatility. Journal of Applied Econometric, Volume 8
  10. Rosadi, D. (2012). Ekonometrika dan Analisis Runtun Waktu Terapan dengan EViews. Yogyakarta: C.V Andi Offset
  11. Thadewald, T., & Buning, H. (2007). Jarque-Bera test and its competitors for testing normality - A power comparison. Journal of Applied Statistics, volume 34(Issue 1), 87-105
  12. Tsay, R. S. (2005). Analysis Of Financial Time Series. Chicago: A John Wiley & Sons, Inc
  13. Wei, W. W. (1994). Time Series Analysis. America: Department of Statistics Temple University

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