xva

Utility libraries to interact with discs, filesystem formats and more

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

Quantitative finance library in Rust for derivatives pricing, curve bootstrapping, risk factor simulations, and XVA, all with AD support.

R Packing Calculating Credit Risk Valuation Adjustments

Part of the Neutryx Lab ecosystem for differentiable finance.

XVA Calculations with QuantLib

Extract IMG disk images from XVA files

Wrong-Way Risk (WWR) estimation for counterparty credit risk - a minimal, hexagonal, numpy-only Python library (CVA, alpha multiplier, Hull-White & copula models).

Portfolio risk an AI agent can drive and a person can read. Historical VaR and Expected Shortfall, signed linear exposure, explicit stress scenarios, counterparty exposure and XVA through a local ORE project. 4 MCP tools, 5 skills, a local dashboard and a hosted service. Research software, not investment advice. Noncommercial licence.

Institutional-grade derivatives pricing and risk. Open source. IRS · Swaptions · XVA · SABR vol surface · Joint rate+vol scenarios.

XVA Principles, Nested Monte Carlo Strategies, and GPU Optimizations

End-to-end Python pipeline for derivatives valuation, valuation adjustments, funding valuation adjustment (FVA), and portfolio reporting using NSE NIFTY option-chain data.

Illustrative Jupyter notebooks covering yield curves, fixed income, derivatives, XVA, Monte Carlo simulation, and quantitative risk analytics.

Counterparty credit risk, xVA and ALM from public data — exposure simulation, SA-CCR, PD scorecards, CVA with wrong-way risk, mortgage prepayment and hedge sizing. Every number in the README traces to the script that produced it.

Educational desktop app that teaches OTC derivatives counterparty-credit underwriting end to end: Monte Carlo exposure (EE/PFE), CVA/DVA/FVA, CSA collateral, limits, and an underwriting memo — plus a guided role-play simulator. PySide6/Qt6, runs offline on synthetic data.

Open model validation, monitoring, stress testing and risk analytics workflows in Python.

Derivative Valuation & Fair Value Adjustment (XVA) Toolkit: OOP instrument hierarchy, Black-Scholes, Monte Carlo, DCF, yield curve bootstrapping, Greeks/DV01 sensitivities, and FVA/CVA/Bid-Offer reserves.

Commodity hedging & structuring workbench: real-data extension of the Citi MQA Forage simulation — frozen futures curves, carry calibration, hedge program, structured note, xVA

Deux parties échangent des intérêts pendant plusieurs années. Mesurer comment leur risque de non-paiement et le calendrier des échanges changent la valeur du contrat.

Python pricing library built on QuantLib: curves, fixed income, derivatives, exotic options, stochastic simulation, and XVA.