Counterparty Credit Risk with Python: Exposure Modeling, Valuation Adjustments, Netting, and Collateral

By James Preston and Oliver J. Thatch

Reactive Publishing

Counterparty credit risk is a central concern in modern derivatives markets, where exposure, collateral, netting, and valuation adjustments all affect how financial institutions measure and manage risk.

Counterparty Credit Risk with Python provides a practical introduction to modeling counterparty exposure and related valuation adjustments using Python. The book explains how credit exposure develops over time, how netting and collateral agreements affect risk, and how valuation adjustments are incorporated into derivatives analysis.

Inside, readers will explore:

Counterparty credit risk concepts and market contextExposure profiles, expected exposure, and potential future exposureMonte Carlo simulation for derivatives exposure modelingCVA, DVA, and FVA concepts in practical risk analysisNetting agreements and collateral mechanicsPython-based workflows for risk measurement and reportingModel interpretation, assumptions, and limitations

Designed for finance professionals, quantitative analysts, risk managers, and technically minded students, this book connects the theory of counterparty credit risk with hands-on implementation. Rather than treating valuation adjustments as isolated formulas, it shows how exposure, credit risk, collateral, and market simulation interact within a complete risk modeling framework.

Clear, structured, and implementation-focused, this guide offers a practical foundation for understanding counterparty credit risk in derivatives portfolios using Python.

• Format: Paperback
• Language: English
• ISBN: B0GZ7JJGW1
• ISBN13: 9798195076085
• Release Date: April 2026
• Publisher: Independently Published
• Length: 436 Pages
• Weight: 1.28 lbs.
• Dimensions: 0.9" x 6.0" x 9.0"