casemark/modeling-portfolio-optimization

Modeling Portfolio Optimization

Builds mean-variance, Black-Litterman, and risk parity optimization models with constraint management and rebalancing rules.

When To Use

• Constructing or rebalancing a multi-asset or multi-factor portfolio against a risk/return objective
• Incorporating subjective market views into equilibrium weights via Black-Litterman
• Implementing risk parity or equal risk contribution across asset classes or factors
• Evaluating constraint sets (position limits, sector caps, turnover budgets) and their impact on the efficient frontier
• Stress-testing portfolio allocations under regime-change or tail-risk scenarios

Inputs To Gather

• Return estimates: Historical return series (frequency, lookback window, asset universe) or forward-looking expected returns from a separate alpha model
• Covariance / risk model: Sample covariance, shrinkage estimator (Ledoit-Wolf), factor-based risk model, or DCC-GARCH specification — document which and why
• Benchmark or equilibrium reference: Market-cap weights for Black-Litterman implied returns; benchmark index if tracking error is a constraint
• Investor views (Black-Litterman): Absolute or relative views, confidence levels (tau, omega matrix calibration)
• Constraints: Min/max position sizes, sector/geography/factor exposure limits, long-only vs. long-short, turnover cap, transaction cost estimates
• Risk budget (risk parity): Target risk contribution per asset or factor; marginal risk contribution tolerances
• Rebalancing rules: Calendar-based (monthly, quarterly) vs. threshold-based (drift bands), tax-lot considerations if applicable

Workflow

1. Select optimization framework

   • Mean-variance (Markowitz): Use when you have credible expected return estimates and want to target a point on the efficient frontier or maximize Sharpe ratio.
   • Black-Litterman: Use when starting from equilibrium (market-cap) weights and blending in discretionary or model-driven views. Specify tau (scaling factor for uncertainty in equilibrium returns) and construct the pick matrix (P) and view vector (Q) with confidence-weighted omega.
   • Risk parity / equal risk contribution: Use when the goal is balanced risk allocation without relying on return forecasts. Solve for weights where each asset's marginal contribution to portfolio volatility is equal (or proportional to a risk budget).

2. Prepare inputs

   • Clean return series: handle missing data, survivorship bias, corporate actions. State lookback period and frequency.
   • Estimate covariance matrix. For large universes (>50 assets), apply shrinkage or factor decomposition to avoid singular or unstable matrices. Document eigenvalue floor if regularizing.
   • For Black-Litterman: derive implied equilibrium returns (π = δΣw_mkt), then combine with views using the BL formula. State delta (risk aversion coefficient) derivation.

3. Formulate and solve

   • Define the objective function (e.g., maximize w'μ − (λ/2)w'Σw for mean-variance; minimize Σ(RC_i − RC_target)^2 for risk parity).
   • Encode all constraints as linear or second-order cone constraints for convex solvers.
   • Solve using quadratic programming (mean-variance), sequential least-squares (risk parity), or closed-form BL posterior.
   • If the solver fails to converge, relax the tightest binding constraint incrementally and document the trade-off.

4. Analyze outputs

   • Report optimal weights, expected return, expected volatility, Sharpe ratio, and max drawdown (historical backtest).
   • Decompose risk: contribution by asset, by factor, and by sector. Identify concentration risks.
   • Run sensitivity analysis: perturb expected returns ±50–100 bps, shift correlations ±0.05–0.10, vary tau (BL) across 0.01–0.10 range. Report weight stability.
   • Compare to benchmark or current portfolio: active weights, tracking error, information ratio.

5. Define rebalancing protocol

   • Specify trigger mechanism: calendar schedule or drift threshold (e.g., rebalance when any weight deviates >2% from target).
   • Incorporate transaction cost model (fixed + proportional) into the rebalance decision — only rebalance if expected utility gain exceeds estimated round-trip cost.
   • For tax-sensitive accounts, apply tax-lot optimization and short-term vs. long-term gain awareness. [VERIFY: tax-lot rules per jurisdiction]

6. Document and deliver

   • Produce a model specification sheet: objective, constraints, solver, input sources, date range, key parameters.
   • Include an assumptions register with explicit flags for any estimated or inferred input.
   • Attach backtest results with appropriate caveats (in-sample vs. out-of-sample, transaction cost assumptions, look-ahead bias checks).

Output

• Optimal weight table: Asset/factor, target weight, current weight, trade direction, position size
• Risk decomposition: Marginal and percentage contribution to risk by asset, factor, and sector
• Efficient frontier chart (mean-variance) or risk contribution bar chart (risk parity)
• Sensitivity matrix: Weight changes under perturbed inputs (returns, correlations, tau)
• Rebalancing rule summary: Trigger type, cost threshold, expected annual turnover
• Model specification sheet: Full parameter documentation for reproducibility and audit

Quality Checks

• Weights sum to 1.0 (or target net exposure for long-short) and satisfy all stated constraints
• Covariance matrix is positive semi-definite — check smallest eigenvalue > 0 (or applied regularization)
• Black-Litterman posterior returns lie between equilibrium returns and views — extreme tilts signal omega miscalibration
• Risk parity solution achieves risk contributions within tolerance (e.g., ±0.5% of target) — if not, flag solver convergence issue
• Backtest Sharpe ratio is plausible relative to asset class history — ratios above 2.0 for traditional assets warrant scrutiny for overfitting
• No single asset exceeds concentration limit; sector/factor exposures within policy bands
• Transaction cost assumptions are realistic for the asset class and trade size [VERIFY: market-specific bid-ask and commission schedules]
• All assumptions, data sources, and parameter choices are documented — no "magic numbers" without justification