ISBN: 9798265071910 - applied bayesian garch with r: theory, implementation, and case studies in financial volatility (5 resultados)

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Paperback. Condición: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…