credit-analysis

To calculate credit risk, input issuer financials and market data to generate Altman Z-score variants and Merton/EDF default probabilities. The skill processes these inputs to quantify credit quality and recommend actions for fixed-income assets.

The best way to compute bond pricing, DV01, and duration is to input your fixed-income asset data into the skill's Python templates. It calculates YTM, convexity, and duration metrics for corporate and municipal bonds to support yield-based decisions.

Yes, you can use this skill for municipal and structured fixed-income credit risk stress testing. It applies spread analysis and sector-specific risk frameworks to run scenario stress tests and provide risk-based decision support across these asset types.

You do not need Python templates to function, but the skill provides them for quick deployment. These templates automate fixed-income credit risk workflows, allowing you to process issuer financials and generate default probabilities and bond analytics efficiently.

Use KMV-like approximations and Z-score variants to quantify default probabilities beyond static credit ratings. These frameworks analyze issuer financials dynamically, providing deeper insights into credit risk, spreads, and scenario risks for fixed-income investment choices.