Penerapan Metode Kalman Filter Dalam Estimasi Harga Saham Menggunakan Model Arch-Garch

Rahmawati, Lusi Nur (2022) Penerapan Metode Kalman Filter Dalam Estimasi Harga Saham Menggunakan Model Arch-Garch. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Saham merupakan produk pasar modal yang menjadi salah satu instrumen investasi. Banyak investor yang memilih saham sebagai instrumen investasi dikarenakan saham memberikan keuntungan yang menarik. Metode estimasi merupakan metode yang tepat bagi para investor untuk memprediksi harga saham sehingga dapat membantu mengoptimalkan keuntungannya. Penelitian ini bertujuan untuk menentukan model terbaik dari data harga saham menggunakan model ARCH-GARCH dan mendapatkan hasil estimasi harga saham menggunakan metode Kalman Filter dengan model ARCH-GARCH untuk periode selanjutnya. Adapun data harga saham yang digunakan yaitu data harga saham PT. Telkom Indonesia Tbk yang diambil dari website resmi Yahoo Finance. Data yang diambil adalah data harga saham saat penutupan (close) periode 29 Februari 2020 sampai 31 Agustus 2021. Pada data harga saham digunakan model ARIMA (Autoregressive Integrated Moving Average) dan terdeteksi terdapat unsur heteroskedastisitas, sehingga digunakan model time series ARCH-GARCH (Autoregressive Conditional Heteroskedasticity-Generalized Autoregressive Conditional Heteroskedasticity). Didapatkan model terbaik yaitu GARCH(1,1) dengan model ARIMA (2,1,3). Pada penerapan metode Kalman Filter didapatkan hasil estimasi harga saham lebih akurat yaitu mendekati data aktual yang ditandai dengan nilai MAPE (Mean Absolute Percentage Error) pada GARCH-Kalman Filter lebih kecil dibandingkan nilai MAPE pada model GARCH(1,1).
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Stock is a capital market product which is an investment instrument. Many investors choose stocks as investment instruments because stocks provide attractive benefits. The estimation method is a method that right for investors to predict stock prices so that it can help optimize profits. The purpose of the research are to determine the best model of stock price data using the ARCH-GARCH model and get stock price estimation results using the Kalman Filter method with the ARCH-GARCH for the next period. The stock price data used are stock price data of PT. Telkom Indonesia Tbk taken from the official Yahoo Finance website. The data taken is stock price data at the close of the period 29 February 2020 to 31 August 2021. On stock price data ARIMA (Autoregressive Integrated Moving Average) model was used and detected there is an element of heteroscedasticity, so used the time series model ARCH-GARCH (Autoregressive Conditional Heteroscedasticity-Generalized Autoregressive Conditional Heteroscedasticity). The best model is obtained, namely GARCH(1,1) with model ARIMA (2,1,3). In the application of the Kalman Filter method, the stock price estimation results are more accurate which is closer to the actual data indicated by the value of MAPE (Mean Absolute Percentage Error) on the GARCH-Kalman Filter is smaller than the value of MAPE on the GARCH(1,1) model.

Item Type:	Thesis (Other)
Additional Information:	RSMa 515.642 Ama p-1 2022
Uncontrolled Keywords:	ARIMA. ARCH-GARCH. Kalman Filter. Harga Saham. ARIMA. ARCH-GARCH. Kalman Filter. Stock Price.
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Mathematics and Science > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Mr. Marsudiyana -
Date Deposited:	05 Jun 2026 04:06
Last Modified:	05 Jun 2026 04:06
URI:	http://repository.its.ac.id/id/eprint/133600

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