Mandates and investment opportunity sets

Question answered

For a bond/equity/private-equity mandate, aggregate the risky sleeve to one effective asset, calibrate the initial MV-optimal allocation, and compute terminal moments, survival, quantiles, and the exact buy-and-hold benchmark.

Workflow

  1. Choose bond share w_bd and equity share AdvisorSpec.q of the non-bond sleeve.

  2. Use portfolio_sigma_unc and portfolio_eta_quadrature through build_effective_asset to obtain the effective regime-switching asset.

  3. compute_opportunity_point calibrates the target return to AdvisorSpec.omega_0 and computes one analytical mandate.

  4. build_opportunity_set repeats this over bond weights.

  5. Compare E, Std, S, F, and O with E_BH, Std_BH, and r_impl_BH.

The quickstart executes exactly one balanced point. The full plotting source is examples/investment_opportunity_set.py.

Important outputs

Key

Meaning

E, Std

unconditional terminal-wealth mean and standard deviation

S, F, O

survival, floor-atom, and overshoot probabilities

Es, Stds

moments conditional on survival

q5 … q95

terminal-wealth quantiles

r_impl

annual continuous return implied by expected terminal wealth

E_BH, Std_BH, r_impl_BH

exact buy-and-hold benchmark moments

floor_cost_pct

relative terminal-value difference from floor protection

Model boundary

Mandate aggregation reduces multiple assets to one effective risky process using a fixed correlation/parameter specification. It is not a general constrained optimiser and does not model rebalancing costs. Use the choice guide when the task is discrete asset-level portfolio construction.

Common mistakes

  • Treating floor_cost_pct as a charged fee or promised realised cost.

  • Mixing percentage and decimal inputs (0.02, not 2, for 2%).

  • Changing paper asset assumptions while continuing to cite paper table values.

  • Interpreting quantiles without the floor atom and overshoot decomposition.

API: client solver and opportunity-set modules.