Aplikasi Model ARCH-GARCH dalam Menganalisis Volatilitas Harga Bawang Merah

Puspitasari, Puspitasari; Kurniasih, Dian; Kiloes, Adhitya Marendra

doi:10.21082/ip.v28n1.2019.p21-30

Cited by 4 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results of research conducted by [13] showed that the price volatility of broiler commodities showed the best model ARCH (1.0) this is in line with the results of this study while beef cattle showed the best model ARCH (1.1). This best model is like what happened to the price of shallots which also has the same best model [10]. Based on the data processing of red cayenne pepper, the equation of the ARCH (1) model is obtained as follows: ht = 562170630 + 0.171429 Based on data processing curly red chili, the equation of the ARCH (1) model is obtained as follows:…”

Section: Volatility Valuementioning

confidence: 73%

See 1 more Smart Citation

Price Volatility Analysis of Red Cayenne Pepper and Curly Red Chili in Kebumen District

Kusnaman¹,

Setiawati²,

Nugroho³

et al. 2023

Advances in Biological Sciences Research

View full text Add to dashboard Cite

Kebumen Regency is an area with a surplus of chilies. The purpose of this study was to analyze the price volatility of red cayenne pepper and curly red chili at the consumer level. The research used was the ARCH-GARCH approach (Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autogressive Conditional Heteroscedasticity (GARCH)) with the assistance of the Eviews 12.0 software. The data was weekly time series data for the period January 2019 to December 2021. The type of red chili used for research were curly red chilies and red cayenne peppers. The results showed that the best model for the volatility of curly red chili is ARCH (1), which means that the price of curly red chili at the consumer level is influenced by the volatility of the previous week. It has low volatility with an ARCH coefficient value of 0.171429 and a probability of 0.0365. The volatility of red cayenne pepper showed similar results, namely with an ARCH coefficient value of 0.173510 and a probability of 0.0319. Furthermore, the volatility model on chili prices at the producer level is still needed.

show abstract

Section: Volatility Valuementioning

confidence: 73%

“…The best model will be used to estimate the price volatility of cayenne pepper and curly red chili [10]. The measure of volatility can be indicated by the standard deviation value which is the square root of the estimated variance of the ARCH GARCH model.…”

Section: Model Parameter Estimationmentioning

confidence: 99%

Price Volatility Analysis of Red Cayenne Pepper and Curly Red Chili in Kebumen District

Kusnaman¹,

Setiawati²,

Nugroho³

et al. 2023

Advances in Biological Sciences Research

View full text Add to dashboard Cite

show abstract

“…Maka, data sudah dapat dianalisis dengan menerapkan model Box-Jenkins. Dalam menentukan model ARIMA yang paling cocok, dapat dilihat dari ordo p pada AR(p) dan ordo q pada MA(q) [11] .…”

Section: Identifikasi Model Arimaunclassified

“…Pada analisis ini, pemilihan model yang cocok untuk forecasting didasarkan pada hasil AIC terkecil. Penentuan model terbaik juga dilakukan dengan cara mencoba setiap ordo yang mungkin [11]. Dari beberapa model ARIMA yang telah dicobakan, model estimasi sementara data inflasi bulanan Indonesia yang paling cocok adalah ARMA (5,0,4), dengan hasil estimasi pada Tabel 3.…”

Section: Identifikasi Model Arimaunclassified

Aplikasi Model ARCH/GARCH dalam Peramalan Laju Inflasi Bulanan Indonesia

Salsabila

Fatharani

Taqiyyuddin

et al. 2022

JSMS

View full text Add to dashboard Cite

Inflasi telah menjadi bagian penting masalah perekonomian pada setiap negara, termasuk Indonesia. Kestabilan inflasi merupakan suatu hal yang penting karena inflasi yang rendah dan stabil merupakan prasyarat bagi pertumbuhan ekonomi sehingga memberikan manfaat bagi peningkatan kesejahteraan masyarakat. Penelitian ini menggunakan metode ARCH/GARCH dalam memodelkan laju inflasi periode bulanan di Indonesia selama periode Januari 1979 sampai April 2021. Residual white noise heteroscedasticity menunjukkan bahwa data memiliki sifat heteroskedastisitas. Untuk mengatasi sifat heteroskedastisitas pada data laju inflasi bulanan digunakan pemodelan dengan metode ARCH/GARCH. Model yang paling sesuai untuk melakukan prediksi laju inflasi bulanan adalah model GARCH (0, 1) yang dapat dilihat dari nilai Akaike Information Criteria terkecil baik pada AIC konstan maupun tidak konstan yaitu sebesar 1366,07 dan 1364,04. Berdasarkan nilai AIC terkecil maka mengindikasikan bahwa model yang diperoleh sudah cocok untuk prediksi.

show abstract

“…Model ARCH/GARCH sering digunakan dalam menganalisis volatilitas dan peramalan harga saham, (Desvina dan Rahmah, 2016); (Eliyawati et al, 2014); (Nastiti dan Suharsono, 2012); (Puspitasari et al, 2019); seluruh data pada penelitian tersebut merupakan data time series yang seringkali berfluktuasi dengan varian kesalahan pengganggu yang tidak konstan.…”

Section: Pendahuluanunclassified

Analisis Volatilitas Variabel Makroekonomi dan Harga Saham Menggunakan Generalized Autoregressive Conditonal Heteroscedasticity (Garch Model)

Paulina

2022

JMSAB

View full text Add to dashboard Cite

Macroeconomic variables often have an impact on stock price volatility and this is needed especially to determine stock price policies in the future. The approach used to see the extent of the volatility of macroeconomic variables on stock prices is the ARCH/GARCH model. This study aims to analyze the nature of stock price volatility in the food and beverage industry in Indonesia using ARCH/GARCH. The data used in this study is secondary data, namely the stock prices of food and beverage industry companies during the period January 2015 to May 2021. The analysis of stock price volatility was carried out with the help of the Eviews 9 software. The results showed the characteristics of the stock price volatility of food and beverage companies based on the ARCH model. /GARCH is quite risky in the short term but not in the long term (indicated by macroeconomic variables of exchange rates, short-term interest rates), so that stock price movements can be anticipated as an early warning system of an increase or decrease in stock prices in the sector. In addition, it can be estimated that the volatility of stock prices in the future is moderate with changes in macroeconomic variables. For policy makers to anticipate changes in macroeconomic variables in certain situations, such as during the current Covid 19 pandemic.

show abstract

Aplikasi Model ARCH-GARCH dalam Menganalisis Volatilitas Harga Bawang Merah

Cited by 4 publications

References 7 publications

Price Volatility Analysis of Red Cayenne Pepper and Curly Red Chili in Kebumen District

Price Volatility Analysis of Red Cayenne Pepper and Curly Red Chili in Kebumen District

Aplikasi Model ARCH/GARCH dalam Peramalan Laju Inflasi Bulanan Indonesia

Analisis Volatilitas Variabel Makroekonomi dan Harga Saham Menggunakan Generalized Autoregressive Conditonal Heteroscedasticity (Garch Model)

Contact Info

Product

Resources

About