Entropy Pooling
R2026bIncorporate subjective views into empirical distributions using
entropy pooling to compute posterior scenario probabilities
Use entropy pooling to blend subjective views on means and
volatilities with an empirical distribution of market variables. The
entropyViews object produces scenario probabilities that
are consistent with specified views while minimizing relative entropy
(Kullback-Leibler divergence).
Objects
entropyViews | Create entropyViews object for entropy pooling of views on
empirical distributions (Since R2026b) |
Functions
setMeanViews | Set views on variable means for entropyViews object (Since R2026b) |
setVolatilityViews | Set views on variable volatilities for entropyViews
object (Since R2026b) |
posteriorProbabilities | Compute posterior probabilities for entropyViews
object (Since R2026b) |
showViews | Display views for entropyViews object (Since R2026b) |
deleteViews | Delete views from entropyViews object (Since R2026b) |
Topics
- Portfolio Optimization Theory
Portfolios are points from a feasible set of assets that constitute an asset universe.
- Incorporate Nonlinear View Constraints Using Sequential Entropy Pooling
Iterate the entropy pooling algorithm to incorporate nonlinear view constraints.
- Mean-Variance Portfolio Optimization with Entropy Pooling
Incorporate subjective views into mean-variance portfolio optimization.
