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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika)
Afifa, Fitriana Nur
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Prediction of Dow Jones Index, US Inflation, and Interest Rate with Kernel Estimator and Vector Error Correction Model Mardianto, M. Fariz Fadillah; Syahzaqi, Idruz; Permana, Made Riyo Ary; Makhbubah, Karina Rubita; Vanisa, Davina Shafa; Afifa, Fitriana Nur
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28460

Abstract

The Dow Jones Industrial Average (DJIA) is the oldest running U.S. stock market index, established by Dow Jones & Company under Charles Dow. Comprising thirty major publicly traded companies, the DJIA is a key indicator of macroeconomic health, reflecting investor confidence and economic stability. This study applies a quantitative research approach to forecast DJIA stock prices, inflation, and U.S. interest rates using time series analysis. Two forecasting methods are compared: Vector Error Correction Model (VECM) and Kernel regression. VECM, a parametric approach, estimates both short- and long-term relationships among economic variables, while Kernel regression, a nonparametric technique, effectively captures complex, nonlinear relationships without strict model assumptions. The results indicate that the Gaussian Kernel method provides the most accurate predictions, achieving a Mean Absolute Percentage Error (MAPE) of 5.72%. The analysis also shows that despite annual fluctuations, the DJIA has exhibited a steady growth trend from 2009 to 2024, with both its starting and ending prices increasing over time. This research is significant for investors, policymakers, and financial analysts, offering insights into market trends and economic indicators. By providing a reliable forecasting model, it aids in better decision-making regarding stock market investments and economic policies.
FORECASTING THE INFLATION RATE IN INDONESIA USING ARIMA-GARCH MODEL Saifudin, Toha; Suliyanto, Suliyanto; Afifa, Fitriana Nur; Arrofah, Aini Divayanti; Fauzi, Doni Muhammad; Pratama, Fachriza Yosa; Adyatma, Isryad Yoga
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp0955-0970

Abstract

Inflation is a key economic indicator that affects purchasing power, economic growth, and financial stability. Accurate forecasting is essential for policymakers to implement effective monetary and fiscal policies. However, traditional models like ARIMA (Autoregressive Integrated Moving Average) mainly capture general trends and often fail to address inflation volatility. This study enhances inflation forecasting accuracy by applying the ARIMA-GARCH hybrid model, which combines trend estimation with volatility modelling. Focusing on Indonesia’s inflation patterns using recent data, it addresses a gap in existing research. This type of research uses quantitative methods, and the data were obtained from the official website of Bank Indonesia. The dataset consists of 240 monthly Indonesian inflation data points spanning from September 2004 to August 2024. The ARIMA (0,1,1)-GARCH (2,0) model is used to analyze inflation trends and volatility dynamics. The model evaluation shows strong predictive performance, with a Mean Absolute Percentage Error (MAPE) of 2.73% and Root Mean Squared Error (RMSE) of 0.74 for training data. Testing data results in a MAPE of 18.95% and RMSE of 0.702, which remains within an acceptable range. These findings highlight the importance of incorporating volatility modelling in inflation forecasting to enhance economic decision-making. A reliable forecast mitigates economic uncertainty, thereby providing a stronger foundation for achieving long-term economic growth. This study contributes by demonstrating the practical application of ARIMA-GARCH in Indonesia’s inflation modelling, providing valuable insights for policymakers in managing inflation-related risks.
Co-Authors Adyatma, Isryad Yoga Arrofah, Aini Divayanti Fauzi, Doni Muhammad M. Fariz Fadillah Mardianto Makhbubah, Karina Rubita Permana, Made Riyo Ary Pratama, Fachriza Yosa Suliyanto Suliyanto Syahzaqi, Idruz Toha Saifudin Vanisa, Davina Shafa