<p>Spatial stochastic volatility models are essential for capturing complex spatial dependencies and variable dynamics in real-world processes. This study introduces a novel framework for generating spatial stochastic volatility using a spatial moving average approach, which models spatially correlated random effects through spatially weighted averages of latent stochastic processes. To estimate the model parameters, we employ a Bayesian Markov Chain Monte Carlo algorithm, transforming the outcome equation into log-squared terms formulation for computational efficiency and robustness. Simulation results demonstrate that the Bayesian estimator exhibits satisfactory finite-sample properties. We further showcase the practical applicability of the proposed model and estimation method by analyzing various countries’ stock market values, highlighting the model’s effectiveness in capturing spatial dependence and volatility clustering. This framework offers a versatile tool for researchers and practitioners working with spatial processes characterized by heteroskedasticity and complex dependency structures.</p>