APPLIED BAYESIAN GARCH WITH R: Theory, Implementation, and Case Studies in Financial Volatility - Couverture souple
Synopsis
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
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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APPLIED BAYESIAN GARCH WITH R: Theory, Implementation, and Case Studies in Financial Volatility
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Applied Bayesian Garch with R (Paperback)
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Paperback. Etat : 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. N° de réf. du vendeur 9798265071910
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Applied Bayesian Garch with R
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Applied Bayesian Garch with R (Paperback)
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Paperback. Etat : 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. N° de réf. du vendeur 9798265071910
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