credit-analysis

To calculate bond duration, DV01, and credit spreads for fixed-income securities, you can apply ready-to-run Python templates that compute YTM, convexity, and spread decomposition. This framework supports scenario analysis for rate moves and basic refinancing risk assessment.

The best way to assess corporate bond default probability and credit risk is by applying quantitative models like Merton/EDF, KMV, and Altman Z-score scoring. These models quantify issuer versus debt-item ratings to provide structured credit quality evaluation.

Yes, you can use Python to price convertible bonds by floor value and run scenario analysis on ABS/MBS instruments. The framework supports rapid scenario shifts, yield-curve fitting, and risk monitoring for asset-backed securities and LGFV debt.

Yes, this credit analysis approach supports OAS spread decomposition and allows you to compare corporate versus government debt. It evaluates credit spreads, applies hedging strategies, and monitors risk across various fixed-income instruments.

Evaluating yield curve fitting and convexity requires fixed-income analytics including YTM, duration, DV01, and bond pricing. These mathematical modeling components enable accurate curve analyses and comprehensive risk monitoring.