portfolio-construction

Portfolio optimization blends mean-variance efficient frontiers with Black-Litterman views. You provide expected returns, covariance, and constraints to generate implementable asset weights and risk metrics.

Risk budgeting allocates portfolio risk across assets rather than capital. It requires covariance inputs and risk aversion parameters to output weight distributions that match specific risk constraints.

Yes, constraint-driven rebalancing supports leverage management and policy-driven mandates. You input constraints and benchmarks to output rebalancing rules that maintain target allocations.

Generating efficient frontiers requires expected returns, covariance matrices, and risk aversion inputs. The optimization outputs frontier curves mapping return objectives against risk levels.

Black-Litterman integration combines equilibrium returns with your explicit views. It requires covariance data and view matrices to output adjusted expected returns and optimized portfolio weights.

Rebalancing discipline incorporates tax considerations alongside governance outputs. You apply constraint-driven rules to minimize tax impacts while generating rebalancing guidance for investment committees.