Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
-, Khadijah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
VOLATILITY ANALYSIS AND INFLATION PREDICTION IN PANGKALPINANG USING ARCH GARCH MODEL Dalimunthe, Desy Yuliana; Kustiawan, Elyas; -, Khadijah; Halim, Niken; Suhendra, Helen
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp237-244

Abstract

One of the concerns of both developed and developing countries, as well as in a region, is the amount of inflation that occurs. Inflation is a serious problem. Inflation is a macroeconomic variable that affects people's welfare and is defined as a complex phenomenon resulting from general and continuous price increases. This research aims to analyze the volatility and projected value of the inflation rate, especially in Pangkalpinang City, using the Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. This research uses time series data on inflation rate of Pangkalpinang, Bangka Belitung Island Province from January 2014 to May 2024. This data was obtained through publications from the Central Statistics Agency of Bangka Beliltung Islands Province. The ARCH model is used to handle heteroscedasticity in data, while the GARCH model is a development of the ARCH model and serves as a generalization of the volatility model. This research shows that the predicted inflation rate in Pangkalpinang City from June 2024 to November 2024 tends to decrease with a MAPE prediction accuracy level of 200.04%. The high MAPE value is caused by actual data moving toward 0.
Co-Authors Desy Yuliana Dalimunthe, Desy Yuliana Halim, Niken Kustiawan, Elyas Suhendra, Helen