dispersion-correlation-arbitrage

Name: dispersion-correlation-arbitrage
Availability: InStock
Author: ghostof0days

Automate dispersion correlation arbitrage workflows with diagnostics JSON and implementation memo outputs.

Updated Feb 10, 2026

Search Tags:#risk management #quantitative finance #trading strategies #arbitrage #model calibration #hedge slippage

Ready to run

npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill dispersion-correlation-arbitrage

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dispersion-correlation-arbitrage
Source: https://github.com/GhostOf0days/codex-quant-skills/tree/main/dispersion-correlation-arbitrage
Command: npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill dispersion-correlation-arbitrage

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, argparse, json, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the process of executing dispersion correlation arbitrage workflows, ensuring reproducible research, explicit controls, and deployable outputs for quantitative finance.

Core Features & Use Cases

Model Calibration: Calibrate model parameters with reproducible and versioned routines.
Risk Assessment: Measure pricing error, greek drift, and stress outcomes under various market shocks.
Use Case: Use this skill to analyze and control for hedge slippage and convergence uncertainty in relative-value trading strategies.

Quick Start

Run python scripts/dispersion_correlation_arbitrage_diagnostics.py input.csv --output diagnostics.json and keep the json artifact.

Frequently Asked Questions about dispersion-correlation-arbitrage

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I measure hedge slippage and convergence uncertainty in relative-value trading strategies?▼

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.

What is dispersion correlation arbitrage and how does model calibration work for it?▼

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.

How do I automate dispersion correlation arbitrage workflows for quant research?▼

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.

Can I use pandas and argparse for arbitrage model calibration and risk assessment?▼

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.

What are the limitations of stress-testing arbitrage strategies under market shocks?▼

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.

Why does my arbitrage pricing error measurement need a diagnostics JSON and implementation memo?▼

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.