Analisis Perbandingan Model Egarch dan Model GJR dalam Memprediksi Volatilitas pada Pasar Saham di Indonesia = Comparison Analysis of Egarch Model and GJR Model in Predicting Volatility in Stock Markets in Indonesia

Widjaya, Regina Clara (2019) Analisis Perbandingan Model Egarch dan Model GJR dalam Memprediksi Volatilitas pada Pasar Saham di Indonesia = Comparison Analysis of Egarch Model and GJR Model in Predicting Volatility in Stock Markets in Indonesia. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Penelitian ini dilakukan untuk mengetahui model terbaik antara model GARCH(1,1), EGARCH(1,1), dan GJR-GARCH(1,1) dalam memprediksi volatilitas harga saham di Indonesia. Periode data yang digunakan adalah selama 11 tahun, dari 1 Januari 2008 sampai dengan 31 Desember 2018. Data yang digunakan berupa data harga adjusted close saham harian dari lima perusahaan yang memiliki kapitalisasi pasar terbesar selama tahun 2016 dan 2017 pada Bursa Efek Indonesia (BEI). Data tersebut diambil dari Yahoo Finance. Kemudian tingkat pengembalian saham akan dihitung secara individu setiap harinya. Melalui nilai pengembalian harga saham ini kemudian akan disesuaikan ke dalam model GARCH, EGARCH, dan GJR. Masing-masing model kemudian akan mengestimasi parameter yang akan digunakan dalam memprediksi nilai pengembalian dan volatilitas harga saham di masa depan. Metode yang digunakan dalam mengestimasi parameter ini adalah metode kemungkinan maksimum. Hasil prediksi within-sample menunjukkan bahwa model terbaik dalam memprediksi volatilitas harga saham di Indonesia adalah model EGARCH(1,1). Di sisi lain, hasil prediksi out-of-sample menunjukkan bahwa model terbaik adalah model GJR-GARCH(1,1). Sebagai kesimpulan, model terbaik adalah model GJR-GARCH(1,1). Hal ini dikarenakan prediksi out-of-sample dianggap lebih akurat dalam kondisi nyata. Melalui kesimpulan ini, dapat dinyatakan bahwa ekstensi model GARCH terbukti lebih baik dibandingkan dengan model GARCH tradisional. / This study was conducted to determine the best model between GARCH(1,1), EGARCH(1,1), and GJR-GARCH(1,1) models in predicting stock price volatility in Indonesia. The data period used is for 11 years, from January 1, 2008 to December 31, 2018. The data used in the form of adjusted close daily stock price data from five companies that have the largest market capitalization during 2016 and 2017 on the Indonesia Stock Exchange (IDX). The data is taken from Yahoo Finance. Then the stock returns will be calculated individually every day. Through this return on stock prices, it will then be converted into the GARCH, EGARCH, and GJR models. Each model will then estimate the parameters that will be used in predicting the value of future stock price returns and volatility. The method used in estimating this parameter is the maximum likelihood method. Using within-sample estimation, it indicates that the best model in forecasting stock price volatility in Indonesia is the EGARCH(1,1) model. On the other hand, the prediction result from out-of-sample forecasting shows that the best model is the GJR-GARCH(1,1) model. In conclusion, the best model is the GJR-GARCH(1,1) model. The reason is because out-of-sample forecasting are considered to be more accurate in real conditions. Through this conclusion, it can be stated that the extension of the GARCH model proved to be better than the traditional GARCH model.

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