APPLIED BAYESIAN GARCH WITH R: Theory, Implementation, and Case Studies in Financial Volatility

Volatility modeling is central to financial econometrics, risk management, and quantitative trading. The GARCH family of models has been a cornerstone for capturing time-varying volatility, but traditional estimation approaches often underestimate uncertainty.
Applied Bayesian GARCH with R provides a hands-on guide to Bayesian inference for GARCH models, combining theoretical intuition with reproducible R code and case studies. You’ll learn how to specify priors, run Markov chain Monte Carlo (MCMC), evaluate convergence, and forecast volatility with full uncertainty quantification.
Topics covered include:

• Bayesian GARCH(1,1) with Gaussian and heavy-tailed errors
• Model extensions: EGARCH, GJR-GARCH, and asymmetric volatility
• Posterior predictive checks and model diagnostics
• Forecasting volatility, Value-at-Risk, and Expected Shortfall
• Advanced topics: multivariate GARCH, hierarchical structures, and model averaging

Each chapter contains R code, exercises, and datasets to reinforce learning. Whether you are a graduate student, researcher, or practitioner in finance, this book equips you with modern Bayesian tools to model volatility with confidence.