Browse CFA Level 2

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.
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