{ "item_title" : "Applied Bayesian Garch with R", "item_author" : [" Felix O. Okolie", "Mary M. Slessor "], "item_description" : "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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/826/507/9798265071910_b.jpg", "price_data" : { "retail_price" : "18.00", "online_price" : "18.00", "our_price" : "18.00", "club_price" : "18.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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