MODEL SELECTION WORKBENCH

Pick the first model before writing the simulator.

Turn your surface, data, and compute constraints into a ranked research path. Scores are planning heuristics, never prices or forecasts.

Scenario

Research constraints

Calibration tolerance
Stress the decision

Prototype first

CGMY

Best fit for asymmetric jumps, with a heavier simulation and calibration burden.

78%decision confidence

What changed

The sample favors jump flexibility over implementation speed.

Weakest assumption

The score assumes dense enough strikes to identify four CGMY parameters.

Do next

Freeze one date, calibrate CGMY and Merton, then compare residual smile structure and runtime.

Trade-offs

See the reason, not just the rank.

Each score is a weighted planning signal from the controls above.

ModelJump realismVol dynamicsPath costCalibrationPrototype
Transparent scoring assumptions

What this cannot tell you

These scores do not estimate option values, forecast returns, test arbitrage constraints, or replace calibration against clean market data. They only order a research backlog from stated preferences.

Durable output

Leave with a testable decision.

Save the scenario, restore it later, or export the assumptions and next experiment for review.

Representative sample loaded.

Saved scenarios

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