Bayesian modeling of volatility in stock price using ARCH-GARCH with stan in R

Stock price data naturally exhibits volatility. Specialized models are necessary to accurately represent and account for this volatile nature when modeling stock prices. This study employs a Bayesian approach to analyze ARCH and GARCH models applied to Apple stock price data. Leveraging the Stan probabilistic programming language within R, the research estimates model parameters and assesses volatility dynamics. Through Bayesian methods, the analysis incorporates prior information, enhancing parameter estimation and providing uncertainty measures for stock price forecasts. Markov chain Monte Carlo sampling techniques are utilized to obtain posterior distributions, revealing insights into volatility patterns, including clustering and persistence, within Apple stock prices. The study showcases the versatility of Bayesian techniques in financial econometrics, enabling model comparison and predictive performance assessment. Results offer valuable insights for investors, risk managers, and policymakers, aiding in understanding and managing stock price volatility.