bayesian-methods

Bayesian methods quantify uncertainty in trading parameter estimates by producing posterior distributions for expected return and volatility, reducing overconfidence in portfolio decisions.

Black-Litterman Bayesian portfolio optimization blends market equilibrium priors with subjective views, updating posterior distributions to produce risk-aware portfolio allocations.

MCMC-based inference supports high-dimensional financial models in small-sample contexts, generating posterior distributions for parameters where traditional frequentist estimates fail.

Prior selection for trading return estimation involves specifying distributions for expected returns and volatility, then updating them with observed data to form posterior estimates.

Bayesian regression predicts asset returns with full posterior distributions, providing uncertainty intervals around estimates rather than single point predictions.

Bayesian methods may be unnecessary when sample sizes are large and parameter uncertainty is negligible, or when prior distributions cannot be justified for the trading strategy.