dispersion-correlation-arbitrage

You can measure hedge slippage and convergence uncertainty by calibrating pricing models, tracking greek drift, and stress-testing market shocks to quantify pricing error in relative-value trading strategies. This produces diagnostics artifacts for reproducible analysis.

Dispersion correlation arbitrage exploits relative-value dislocations between index components. Model calibration works by fitting reproducible, versioned parameters to pricing objectives, enabling accurate measurement of pricing error and stress outcomes under market shocks.

Automate dispersion correlation arbitrage workflows by running the diagnostics script with an input CSV file and output flag, generating a JSON diagnostics artifact and implementation memo for production controls and reproducible quant research.

Yes, the workflow supports pandas and argparse dependencies for arbitrage model calibration and risk assessment. It processes CSV input data, calibrates parameters, and outputs diagnostics JSON artifacts compatible with standard Python quantitative research environments.

Stress-testing arbitrage strategies under market shocks is limited by convergence uncertainty and model calibration accuracy. Pricing error measurements and greek drift diagnostics help quantify these constraints, but extreme market dislocations can still produce unpredictable hedge slippage outcomes.

Arbitrage pricing error measurement needs a diagnostics JSON and implementation memo to ensure reproducible research, explicit risk controls, and deployable production outputs. These artifacts document model calibration, stress test results, and greek drift for audit trails.