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JoelLewis
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fixed-income-corporate

Analyze corporate bonds and calculate credit spreads with Python scripts.

164|33|Updated Feb 15, 2026
Search Tags:#oas#corporate bonds#credit risk#z-spread#expected loss#credit migration
One-click install
npx skills add https://github.com/JoelLewis/finance_skills --skill fixed-income-corporate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fixed-income-corporate
Source: https://github.com/JoelLewis/finance_skills/tree/main/plugins/wealth-management/skills/fixed-income-corporate
Command: npx skills add https://github.com/JoelLewis/finance_skills --skill fixed-income-corporate

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps users understand and analyze corporate bonds, including their credit risk, pricing, and various yield measures, making complex fixed-income analysis more accessible.

Core Features & Use Cases

  • Credit Spread Analysis: Calculate and compare G-spread, Z-spread, and Option-Adjusted Spread (OAS) to assess compensation for risk.
  • Risk Metrics: Evaluate Expected Loss (EL) based on Probability of Default (PD) and Loss Given Default (LGD).
  • Credit Migration: Analyze the probability of credit rating changes over time.
  • Use Case: A user wants to understand the risk associated with a specific corporate bond. They can use this skill to calculate its Z-spread and compare it to the risk-free rate, and also estimate the potential loss if the issuer defaults.

Quick Start

Analyze the credit risk of a corporate bond by calculating its Z-spread and expected loss.

Frequently Asked Questions about fixed-income-corporate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate OAS and Z-spread for corporate bonds?▼

To calculate OAS and Z-spread for corporate bonds, you can use Python scripts to analyze bond characteristics and measure the spread against the risk-free rate. This approach quantifies credit risk compensation for callable bonds.

What is the difference between G-spread and Z-spread in credit risk analysis?▼

G-spread and Z-spread are credit spread metrics used in corporate bond analysis to assess risk compensation. This skill calculates both, alongside OAS, using Python to evaluate the yield premium over benchmark rates for investment grade and high yield debt.

How do I estimate expected loss for high yield debt using probability of default?▼

You estimate expected loss for high yield debt by combining probability of default (PD) and loss given default (LGD). This skill calculates these risk metrics to evaluate the potential loss exposure if a corporate bond issuer defaults.

Can I use Python with numpy and scipy to analyze credit migration probabilities?▼

Yes, you can use Python with numpy and scipy dependencies to analyze credit migration probabilities. This skill utilizes these libraries to calculate the likelihood of corporate bond credit rating changes over time.

What is the best way to evaluate credit risk for callable corporate bonds?▼

The best way to evaluate credit risk for callable corporate bonds is by calculating the Option-Adjusted Spread (OAS) and expected loss. This skill provides Python scripts for quantitative analysis of these specific bond characteristics and risks.

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