Bayesian Inference of Geometric Brownian Motion: An Extension with Jumps

Abstract

This analysis derives the maximum likelihood estimator and applies Bayesian inference to model geometric Brownian motion, incorporating jump diffusion to account for sudden market shifts. The Bayesian approach is implemented using Markov Chain Monte Carlo simulations on S&P 500 stock data from 2009 to 2014, providing a robust framework for analyzing stock dynamics and forecasting future trends. Exact solutions are obtained for both the standard Geometric Brownian Motion (GBM) model and the GBM model with Poisson jumps. Although both models yield reasonable results and fit the data well, the GBM with Poisson jumps exhibits superior performance, significantly enhancing model fit and capturing more complex market dynamics.