Simulation Option Pricing

objective

Execute simulation option pricing work with reproducible research, explicit controls, and deployable outputs.

workflow

1. •define pricing objective, calibration universe, and hedge policy constraints.
2. •calibrate model parameters with reproducible and versioned routines.
3. •measure pricing error and greek drift across strikes and maturities.
4. •stress jump, skew, and vol-of-vol shocks with hedge rebalancing costs.
5. •release only after model error and hedge slippage stay within limits.

required diagnostics

• •pricing residual by tenor, moneyness, and liquidity bucket.
• •surface smoothness and no-arbitrage consistency checks.
• •greek exposure concentration and hedge tracking error.
• •stress outcomes under volatility spikes and gap-risk events.

risk controls

• •enforce per-book greek limits and rehedge thresholds.
• •enforce model fallback when calibration fails or destabilizes.
• •enforce event-risk reductions before scheduled macro releases.

outputs

• •run python scripts/simulation_option_pricing_diagnostics.py input.csv --output diagnostics.json and keep the json artifact.
• •write an implementation memo using references/simulation-option-pricing-playbook.md with assumptions, tests, limits, and rollout plan.

resources

• •use scripts/simulation_option_pricing_diagnostics.py for deterministic diagnostics.
• •use references/simulation-option-pricing-playbook.md for the domain-specific checklist and delivery structure.