Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika

  • Maulida Nurhidayati IAIN Ponorogo
Keywords: Nonlinear; SETAR; Grid Search; Genetic Algorithm; Stock Return

Abstract

Nonlinear time series model is a time series model applied to data that has the nonlinear pattern. One of the nonlinear time series models is Self-Exciting Threshold Autoregressive (SETAR). The SETAR model is a time series model that data modeling is done by dividing data into multiple regimes, whereas each regime following an autoregressive (AR) model. The division of the regime based on the score of the delay and threshold of the data itself. The number of SETAR model parameters not only resulted from the best model search process but also resulted in a SETAR model that is not yet optimum. Based on these findings, this study used Genetic Algorithm (GA) to produce the best and optimum SETAR model. In this research, using SETAR simulation data modeling and return data of Bank Rakyat Indonesia (BRI) were performed. The method used to model the data is Grid Search (GS) and Genetic Algorithm (GA). The result of analysis of SETAR simulation data shows that GA method gives better modeling result than GS method. The GA motive AIC value for the amount of 200 data is -3.976178 which is smaller than the AIC GS method of 1.361723. For the amount of data of 500 AIC values, GA method is also smaller than AIC GS method. In BRI stock return data, GA method also gives better modeling result compared to GS. It is marked by the GA AIC method value of -11147.66 less than -11146.26 which is the AIC method of GS. Thus, the result of analysis of SETAR model simulation data and BRI stock return shows that GA method gives better modeling result compared to GS method based on generated AIC value.

Downloads

Download data is not yet available.

References

D. N. Gujarati dan D. C. Porter, Dasar-dasar Ekonometrika, 5 ed. Jakarta: Salemba Empat, 2013.
[2] P. H. Franses dan D. van Dijk, Nonlinear Time Series Models in Empirical Finance. New York: Cambridge University Press, 2003.
[3] B. Wu dan C.-L. Chang, “Using Genetic Algorithms to Parameters (d;r) Estimation for Threshold Autoregressive Models,” Comput. Stat. Data Anal., vol. 38, hlm. 315–330, 2002.
[4] M. Sawaka, Genetic Algoritms and Fuzzy Multiobjective Optimization. Boston: Kluwer Academic Publishers, 2002.
[5] W. W. S. Wei, Time Series Analysis Univariate and Multivariate Methods. New York: Pearson, 2006.
[6] H. Tong dan K. S. Lim, “Threshold Autoregression, Limit Cycles and Cyclical Data,” J. R. Stat. Soc. Ser. B Methodol., vol. 42, no. 3, hlm. 245–292, 1980.
[7] R. S. Tsay, “Testing and Modeling Threshold Autoregressive Process,” J. Am. Stat. Assoc., vol. 84, no. 405, hlm. 231–240.
[8] D. Sri Kusuma dan H. Purnomo, Penyelesaian Masalah Optimasi dengan Teknik-teknik Heuristik. Yogyakarta: Graha Ilmu, 2005.
[9] B. Santosa dan P. Willy, Metode Metaheuristik, Konsep dan Implementasi. Surabaya: Guna Widya, 2011.
[10] R. Barogana, F. Battaglia, dan D. Cucina, “Estimating Threshold Subset Autoregressive Moving-Average Models by Genetic Algorithms,” METRON - Int. J. Stat., vol. LXII, hlm. 39–61, 2004.
Published
2018-05-15
How to Cite
Nurhidayati, M. (2018). Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika. Jurnal Matematika "MANTIK", 4(1), 16-21. https://doi.org/10.15642/mantik.2018.4.1.16-21
Section
Articles