Estimation of Stock Return Volatility Using Bayesian MCMC-Based Stochastic Volatility Model - Muhammad Bahrul Ilmi; Hanan Hamuda (Research Collaboration Community (RCC), 2026)
Parameter estimation of a distribution can be performed through two main approaches: the classical method and the Bayesian method. The Bayesian method integrates the sample distribution with the prior distribution, where random sampling is conducted via simulation techniques such as Markov Chain Monte Carlo (MCMC) with the Gibbs Sampling algorithm. This algorithm works by constructing a Markov Chain through recursive sampling from the full conditional posterior distribution for each parameter until convergence is reached. This study applies the Bayesian method with MCMC using the Gibbs Sampling algorithm to estimate the parameters of the Stochastic Volatility model, which allows asset price volatility to vary over time. The obtained Stochastic Volatility model is then used to predict the stock returns of PT. Aneka Tambang Tbk. (ANTM.JK), where the prediction results show good conformity with actual data. The resulting prediction values can be utilized by investors as a reference in making optimal investment portfolio decisions.