Penerapan Model ARIMA-GARCH Untuk Memprediksi Harga Saham Bank BRI

Yolanda, Natasya Bella; Nainggolan, Nelson; Komalig, Hanny A.H.

doi:10.35799/jm.6.2.2017.17817

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…Untuk penelitian model ARIMA-GARCH pada Bank BRI, juga sudah pernah diteliti oleh [16]. Penerapan model ARIMA-GARCH tersebut digunakan untuk memprediksi harga saham pada Bank BRI.…”

Section: Pendahuluanunclassified

Perbandingan Kinerja Model Arima Dan Garch Dalam Peramalan Harga Saham Bank Bri

Dhifa Maulia,

Triwulandari,

Fauzan

et al. 2024

MILANG

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Model ARIMA dan GARCH adalah model yang dapat mengatasi volatilitas yang sering terjadi pada harga saham. Tujuan dari penelitian ini adalah untuk membandingkan kinerja model dan mencari efektivitas ARIMA serta GARCH dalam peramalan harga penutupan saham Bank BRI. Penelitian ini menggunakan data sekunder time series yang bersumber dari website Yahoo Finance mengenai harga penutupan saham harian Bank BRI mulai dari 1 September 2014 sampai 30 September 2023 dengan jumlah pengamatan sebanyak 2260 hari. Berdasarkan hasil ramalan, MAPE dari model ARIMA(2,1,0) didapatkan sebesar 2,256% sedangkan model ARIMA(2,1,0)-GARCH(3,3) sebesar 21,735%. Oleh karena itu, model terbaik untuk meramalkan harga saham BRI adalah model ARIMA(2,1,0) dengan peramalan data yang dihasilkan cenderung stabil dan selang kepercayaan yang cenderung melebar di setiap periode.

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“…Untuk penelitian model ARIMA-GARCH pada Bank BRI, juga sudah pernah diteliti oleh [16]. Penerapan model ARIMA-GARCH tersebut digunakan untuk memprediksi harga saham pada Bank BRI.…”

Section: Pendahuluanunclassified

Perbandingan Kinerja Model Arima Dan Garch Dalam Peramalan Harga Saham Bank Bri

Dhifa Maulia,

Triwulandari,

Fauzan

et al. 2024

MILANG

View full text Add to dashboard Cite

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“…It also has as residual with lagged conditional volatility, and as ARCH component, and and as GARCH component. After the ARCH/GARCH model estimation, the ARCH Lagrange Multiplier (LM) test can be applied to the model to determine whether the data still have heteroscedasticity (Yolanda et al, 2017).…”

Section: Return= Logmentioning

confidence: 99%

Volatility and Liquidity Comparison of Indonesian and Singapore Stock Market in COVID-19 Mobility Restrictions Era

Nathania,

Sumani

2023

Binus Business Review

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The COVID-19 case found at the end of December 2019 became a pandemic in March 2020. The research aimed to see and understand the differences in the performance of the Indonesian and Singapore stock indices represented by the Indeks Harga Saham Indonesia (IHSG) and Straits Times Index (STI) before and after the implementation of community mobility restrictions (Pembatasan Sosial Berskala Besar (PSBB) in Indonesia and Circuit Breaker in Singapore). The stock index data were stock index prices at closing and stock trading volume. The stock index performance was measured by its volatility and liquidity. Meanwhile, data volatility with heteroscedasticity symptoms was measured using the GARCH (1,1) model. Meanwhile, the standard deviation was used to measure homoscedastic data. The results show differences in return volatility and stock index liquidity before and after restrictions on community mobility. The return volatility of the IHSG and STI is higher before the community mobility restrictions compared to the period after. IHSG experiences liquidity after PSBB I and before PSBB II. The conclusion that can be drawn from these results is that liquidity in Indonesia does not improve when PSBB I is implemented, but liquidity improves in PSBB II. Meanwhile, STI’s liquidity is higher in the period after the implementation of Circuit Breaker. These results indicate that implementing the Circuit Breaker helps to improve the stock index’s performance in Singapore because volatility decreases when the policy is implemented. The policy also reduced the liquidity of the Singapore stock index.

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“…Heteroscedasticity occurs when the residual variance in time series data is not constant, necessitating a combination of ARIMA and GARCH models. In the study by (Yolanda et al, 2017), the ARIMA (2,1,1)-GARCH(2,2) method was found to be effective in predicting the stock price of BRI, with an R-squared value of 0.99916 or 99.91%. In another study by (Ningsih, 2021), the GARCH (1,1) method was proven to be reliable, as the forecasted data closely approximated the actual data with a Mean Absolute Percentage Error (MAPE) value of 1.273% for the Daily Stock Price of PT BTPN Syariah.…”

Section: Introductionmentioning

confidence: 99%

ARIMA-GARCH Model Price Forecasting in PT. Unilever Indonesia Tbk

Pramesti,

Sulistijanti

2023

EKSYAR

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The purpose of this research is to predict the closing stock price of PT. Unilever Indonesia Tbk using the ARIMA-GARCH method. The data used in this study covers the period from February 20, 2020, to February 17, 2023, consisting of 734 daily observations. The data processing is performed using E-Views software. The closing stock price data of PT. Unilever Indonesia Tbk is non-stationary, thus requiring natural logarithm transformation and differencing. This is followed by model identification, parameter estimation, and diagnostic checking. The best-selected ARIMA model is ARIMA ([3,9],1,0), which accounts for the presence of heteroscedasticity. Subsequently, the GARCH method is applied, including model identification, parameter estimation, and diagnostic checking. The best GARCH model is GARCH (1,1), with the mean equation = 0,000047 + 0,230821 + 0,681968σ , which is free from heteroscedasticity effect. The forecast using the ARIMA ([3,9],1,0) GARCH (1,1) model yields a Mean Absolute Percentage Error (MAPE) of 2.423%, indicating a close approximation to the actual data. From the results of this research, the best model for forecasting PT. Unilever Indonesia Tbk for the next period was obtained. Therefore, the findings can assist PT. Unilever Indonesia Tbk and prospective investors in making decisions regarding the sale and purchase of shares in PT. Unilever Indonesia Tbk.

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Penerapan Model ARIMA-GARCH Untuk Memprediksi Harga Saham Bank BRI

Cited by 5 publications

References 2 publications

Perbandingan Kinerja Model Arima Dan Garch Dalam Peramalan Harga Saham Bank Bri

Perbandingan Kinerja Model Arima Dan Garch Dalam Peramalan Harga Saham Bank Bri

Volatility and Liquidity Comparison of Indonesian and Singapore Stock Market in COVID-19 Mobility Restrictions Era

ARIMA-GARCH Model Price Forecasting in PT. Unilever Indonesia Tbk

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