Penerapan Metode Monte Carlo Simulation untuk Estimasi Risiko Portofolio Saham pada Pasar Modal Indonesia - Sunarmi Sunarmi; Siti Kholifah (Journal of New Trends in Sciences, 2023)
This research explores the application of Monte Carlo Simulation in estimating portfolio risk in the Indonesian stock market. The primary objective is to assess the effectiveness of this method in predicting portfolio return distribution and managing risk compared to traditional methods like Value at Risk (VaR). Data from the Indonesia Stock Exchange (IDX) were used to analyze stock returns, focusing on sectors such as telecommunications and property. Monte Carlo Simulation was applied to generate multiple scenarios of stock returns based on historical data and probabilistic distributions. The findings show that Monte Carlo Simulation provides a more comprehensive risk estimation, especially for stocks with high volatility and small market capitalization. Unlike VaR, which assumes a normal distribution, Monte Carlo Simulation accounts for extreme risk events and market uncertainties. The study also highlights the importance of diversification, as portfolios with a mix of high and low volatility stocks demonstrate a more stable risk profile. The results suggest that Monte Carlo Simulation is an effective tool for investors looking to manage risk in dynamic market conditions, providing more accurate and reliable estimations compared to traditional risk assessment methods. This research recommends further exploration of Monte Carlo Simulation in other sectors or with varied data for broader applications in risk management.