Chapter 14: Bayesian Methods in Financial Modeling

In this section

• Bayesian Updating: Priors, Likelihoods, and Posteriors
  Learn how Bayesian methods update beliefs in real-time using prior distributions, likelihood functions, and resulting posterior distributions for more accurate financial forecasting and risk analysis.
• Markov Chain Monte Carlo (MCMC) and Convergence Diagnostics
  An in-depth exploration of MCMC algorithms such as Metropolis-Hastings and Gibbs Sampling, their significance in Bayesian finance, and practical methods for diagnosing chain convergence.
• Bayesian Regression and Hierarchical Models
  Discover how Bayesian Regression integrates prior beliefs with new data, yielding flexible models that handle parameter uncertainty and capture nuanced group-level variations. Learn derivations, advantages over frequentist methods, and real-world finance applications, including hierarchical frameworks for partial pooling.
• Portfolio Construction with Bayesian Techniques
  Learn how Bayesian updating can enhance portfolio construction by integrating investor views and market data, with a focus on the Black-Litterman framework and practical optimization steps.
• Vignette: Forecasting with Bayesian Approaches
  Learn how to apply a Bayesian framework to forecast monthly stock returns for an emerging-market portfolio, incorporating new macro data and exploring posterior updates, credible intervals, and visualization strategies.