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Applied Bayesian Garch with R: Theory, Implementation, and Case Studies in Financial Volatility
Paperback
Publisher: Independently Published
Published: Sep 12 2025
Pages: 160
Weight: 0.49
Height: 0.34 Width: 6.00 Depth: 9.00
Language: English
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.
- 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
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