Applied Bayesian Garch with R: Theory, Implementation, and Case Studies in Financial Volatility

By Felix O. Okolie and MARY M. SLESSOR

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 errorsModel extensions: EGARCH, GJR-GARCH, and asymmetric volatilityPosterior predictive checks and model diagnosticsForecasting volatility, Value-at-Risk, and Expected ShortfallAdvanced topics: multivariate GARCH, hierarchical structures, and model averagingEach 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.

• Format: Paperback
• Language: English
• ISBN: B0FR1NVSCQ
• ISBN13: 9798265071910
• Release Date: September 2025
• Publisher: Independently Published
• Length: 160 Pages
• Weight: 0.49 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"