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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika)
Damayanti, Rismania Hartanti Putri Yulianing
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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STRENGTHENING SYARIAH FINANCIAL MARKETS WITH GARCH-BASED STOCK PRICE FORECASTING AND VAR-RISK ASSESSMENT Darmanto, Darmanto; Darti, Isnani; Astutik, Suci; Nurjannah, Nurjannah; Lee, Muhammad Hisyam; Damayanti, Rismania Hartanti Putri Yulianing; Irsandy, Diego
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1217-1236

Abstract

Indonesia, as the largest Muslim-majority country, has significant potential to enhance its Shariah financial sector, which has been growing rapidly, around 7.43% from 2023 to 2024, and contributing to the national economy. However, political and natural disasters have influenced the economy and Shariah-compliant stocks. This study focuses on forecasting Shariah-compliant stock prices using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models and estimating investment risks via Value at Risk (VaR) for four Islamic banks listed in IDX: BRIS, BTPS, BANK, and PNBS. The findings indicate that GARCH models effectively capture stock price dynamics and provide accurate 10-day forecasts. Additionally, the models reliably predict VaR, validated through backtesting at various confidence levels. These insights are valuable for financial regulators and risk managers, aiding in policy design to ensure market stability by enabling the implementation of measures such as stricter capital reserve requirements for institutions with high-risk exposure and mandatory adoption of advanced risk management techniques like dynamic stress testing. Such policies not only mitigate systemic risks during periods of financial volatility but also enhance the overall resilience and robustness of the financial system. For investors, accurate risk predictions support informed decision-making, enhance portfolio protection, and optimize risk management.
An Informative Prior of Bayesian Kriging Approach for Monthly Rainfall Interpolation in East Java Damayanti, Rismania Hartanti Putri Yulianing; Astutik, Suci; Astuti, Ani Budi
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.31027

Abstract

In spatial data analysis, interpolation is used to estimate values at unobserved locations, but often faces challenges in capturing complex spatial patterns and estimation uncertainty. One of the main obstacles is the small sample size, which makes the empirical variogram difficult to define well in conventional Kriging methods. The Bayesian Kriging approach overcomes this problem by integrating prior information, so it can still produce stable estimates despite limited data. This study is a quantitative, spatial-based research aimed at interpolating monthly rainfall in East Java Province using the Bayesian Kriging approach. The data consist of monthly rainfall measurements from 11 rain gauge stations distributed across East Java, obtained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) for the period of January to April 2024. The entire analysis was conducted using R software. A spherical semivariogram model was selected due to its superior fit to the spatial characteristics of the rainfall data in the study area with the smallest RMSE 37.17. This study demonstrates the effectiveness of Bayesian Kriging for rainfall interpolation in tropical regions with sparse data, providing more stable and accurate estimates compared to conventional methods. The scientific contribution of this research lies in showcasing how the integration of informative priors and Bayesian inference enhances interpolation accuracy in data-limited tropical environments. The resulting interpolated maps can inform land-use planning and flood risk mitigation by identifying areas of high rainfall for improved water infrastructure and lower-rainfall regions for targeted irrigation planning. 
Co-Authors Ani Budi Astuti Darmanto Darmanto Irsandy, Diego Isnani Darti Lee, Muhammad Hisyam Nurjannah Nurjannah Suci Astutik