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Counterparty Credit Risk with Python: Exposure Modeling, Valuation Adjustments, Netting, and Collateral
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Publisher: Independently Published
Published: Apr 30 2026
Pages: 436
Weight: 1.28
Height: 0.89 Width: 6.00 Depth: 9.00
Language: English
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 context
- Exposure profiles, expected exposure, and potential future exposure
- Monte Carlo simulation for derivatives exposure modeling
- CVA, DVA, and FVA concepts in practical risk analysis
- Netting agreements and collateral mechanics
- Python-based workflows for risk measurement and reporting
- Model 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.
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