PENERAPAN MODEL EXPONENTIAL GARCH DAN THRESHOLD GARCH PADA PERAMALAN HARGA SAHAM

ANNISA DWI FITRIANTI, . (2022) PENERAPAN MODEL EXPONENTIAL GARCH DAN THRESHOLD GARCH PADA PERAMALAN HARGA SAHAM. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Saham merupakan salah satu alat pasar modal yang menarik bagi investor serta berperan penting dalam perekonomian di Indonesia. Harga saham merupakan data deret waktu yang biasanya memiliki kecenderungan naik dan turun secara ekstrem dari waktu ke waktu yang menyebabkan ragam tidak konstan. Dalam ARIMA memerlukan asumsi stasioner dan ragam yang konstan. Untuk kasus harga saham, ARIMA hanya digunakan sebagai persamaan rata-rata yang nantinya akan dilanjutkan dengan model yang dapat mengatasi masalah ragam tidak konstan. Model yang tepat untuk mengatasi hal tersebut yaitu GARCH (Generalized Autoregresssive Conditional Heteroscedasticity). Namun, pada beberapa kasus harga saham terdapat volatilitas yang bersifat asimetris, yaitu keadaan dimana pergerakan dalam pasar modal ketika guncangan yang datang lebih besar bersifat negatif dibandingkan positif. Untuk mengatasi hal tersebut, dengan mendukung kemungkinan adanya respon volatilitas yang asimetris model GARCH pun dikembangkan. Ada beberapa pemodelan GARCH asimetris, diantaranya Exponential GARCH (EGARCH) dan Threshold GARCH (TGARCH).Pada penelitian ini diterapkan model Exponential GARCH dan Threshold GARCH dengan menggunakan data harga saham PT. Unilever Indonesia Tbk dengan tujuan mengetahui model terbaik diantara EGARCH dan TGARCH dalam peramalan. Hasil dari penelitian ini diperoleh model terbaik diantara EGARCH dan TGARCH yaitu TGARCH. Model terbaik yang digunakan untuk peramalan pada penelitian ini ialah ARIMA(2,1,0)-TGARCH(1,1). **************************************************** Stocks are one of the most attractive capital market tools for investors and play an important role in the Indonesian economy. Stock prices are time series data that usually have an extreme tendency to rise and fall from time to time which causes the variance is not constant. In ARIMA requires stationary assumptions and constant variance. In the case of stock prices, ARIMA is only used as an average equation which will be followed by a model that can solve the problem of non-constant variance. The right model to overcome this is GARCH(Generalized Autoregressive Conditional Heteroscedasticity). However, in some cases, there is asymmetric volatility in stock prices, namely a situation where the movement in the market is greater when the shocks that come are negative rather than positive. To overcome this, by supporting the possibility of an asymmetric volatility response, the GARCH model was developed. There are several asymmetric GARCH models, including Exponential GARH (EGARCH) and Threshold GARCH (TGARCH). In this study, the Exponntial GARCH and Threshold GARCH models were applied using the stock price data of PT. Unilever Indonesia Tbk with the aim of knowing the best model between EGARCH and TGARCH in forecasting. The result of this research is that the best model between EGARCH and TGARCH is TGARCH. The best mode used for forecasting is ARIMA(2,1.,0)-TGARCH(1,1).

Item Type:	Thesis (Sarjana)
Additional Information:	1). Dra.Widyanti Rahayu, M.Si.; 2). Ibnu Hadi, M.Si
Subjects:	Sains > Matematika
Divisions:	FMIPA > S1 Matematika
Depositing User:	sawung yudo
Date Deposited:	28 Oct 2024 07:16
Last Modified:	28 Oct 2024 07:16
URI:	http://repository.unj.ac.id/id/eprint/51775

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