Analitika: Journal of Economics, Management, and Business

Stock Price Forecasting Using the ARMA-GARCH Model

2026-04-24T06:25:41+00:00

The capital market plays a crucial role as a global economic indicator. This study aims to analyze the volatility and forecast the stock prices of PT Asuransi Tugu Pratama Indonesia using the ARMA-GARCH method with STATA application on stock price data from 2019 to 2023. The analysis process involves transforming the data into a time series, stationarity testing, ARCH-LM testing, and model evaluation using AIC criteria. The results indicate that the ARMA-GARCH method is effective with a low error rate of 9.61% (MAPE), providing accurate stock price forecasts to support investment decision-making. These findings offer insights into volatility dynamics and better investment strategies in the stock market.

Comparison of Brass Logit and Kostaki Accuracy in Life Table Reconstruction for Insurance Premiums

2026-04-26T12:04:51+00:00

Despite the widespread use of interpolation methods in mortality studies, limited research has conducted a comprehensive comparison of their statistical accuracy and financial implications in the Indonesian context. This study addresses the limited availability of complete mortality data in Indonesia, where mortality information is commonly presented in abridged life tables with five-year age intervals, creating challenges for accurate premium determination. The objective of this research is to compare the performance of the Brass Logit and Kostaki methods in reconstructing complete life tables and to evaluate their impact on term life insurance premium estimation. A quantitative comparative approach is employed using secondary data from the Coale Demeny West Model, Indonesia’s life expectancy published by Statistics Indonesia, and the Indonesian Mortality Table IV as the benchmark. The analysis involves model level selection, interpolation, accuracy evaluation using Root Mean Square Error, and application to premium calculations. The results show that the Brass Logit method provides higher statistical accuracy, while the Kostaki method produces premium estimates closer to the benchmark. These findings highlight a trade-off between statistical precision and financial relevance, offering practical guidance for method selection and supporting more robust actuarial decision making in the insurance industry

Evaluating the Geometric Brownian Motion Model for Gold Price Movements (XAU/USD) across Market Conditions

2026-04-26T12:03:56+00:00

This study evaluates the performance of the Geometric Brownian Motion (GBM) model in predicting prices and estimating the risk of XAU/USD under stable and unstable economic conditions. Daily closing price data are divided into in-sample and out-of-sample periods, where return and volatility parameters are estimated from the in-sample data and applied in Monte Carlo simulations. Prediction accuracy is assessed using the Mean Absolute Percentage Error (MAPE), while risk is measured using Value at Risk (VaR) and Expected Shortfall (ES). The results indicate that MAPE values exceed 20% in both economic conditions, suggesting that the predictive accuracy of the GBM model remains moderate. Although the model performs slightly better during stable periods, the difference is not statistically significant. In contrast, risk estimation results show that VaR and ES range between 58% and 59%, with higher values observed during unstable periods. This reflects an increase in market risk driven by higher volatility. Overall, the findings suggest that while GBM is capable of capturing the general movement of gold prices, it has limitations in accurately predicting price levels and capturing extreme market fluctuations, particularly during periods of economic instability.

BUMDes as a Driver of Rural Economic Development in Hanggira Village

2026-04-28T04:39:48+00:00

This study aims to analyze the role of Village-Owned Enterprises (BUMDes) in increasing Village Original Income (PADes) and empowering the local economy in Hanggira Village, Poso Regency, Central Sulawesi. This research employed a qualitative case study approach. Data were collected through in-depth interviews and direct observation involving the village head, BUMDes managers, and community members who directly benefited from BUMDes programs. The findings reveal that BUMDes plays an important role in increasing PADes through the management of agricultural equipment rental services. Furthermore, BUMDes contributes to reducing agricultural production costs and improving community access to productive resources. The effectiveness of BUMDes is influenced by several factors, including government support, managerial capacity, community awareness, and business development strategies. This study confirms that BUMDes can become a driving force for the village economy when supported by good governance and strong community participation.