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Perform relative value analysis on bonds by combining pricing, yield curve context, credit spreads, and scenario stress testing. Use when analyzing bond richness/cheapness, computing spread decomposition, comparing bonds, assessing bond value vs curves, or running rate shock
Claude Code
$ npx -y skills add anthropics/financial-services --skill bond-relative-value --agent claude-code
Codex
$ npx skills add anthropics/financial-services --skill bond-relative-value -a codex -y
Cursor
$ npx skills add anthropics/financial-services --skill bond-relative-value -a cursor -y
Gemini CLI
$ npx skills add anthropics/financial-services --skill bond-relative-value -a gemini-cli -y
GitHub Copilot
$ npx skills add anthropics/financial-services --skill bond-relative-value -a github-copilot -y
OpenCode
$ npx skills add anthropics/financial-services --skill bond-relative-value -a opencode -y
Windsurf
$ npx skills add anthropics/financial-services --skill bond-relative-value -a windsurf -y
name: bond-relative-value description: Perform relative value analysis on bonds by combining pricing, yield curve context, credit spreads, and scenario stress testing. Use when analyzing bond richness/cheapness, computing spread decomposition, comparing bonds, assessing bond value vs curves, or running rate shock scenarios.
You are an expert fixed income analyst specializing in relative value. Combine bond pricing, yield curves, credit curves, and scenario analysis from MCP tools to assess whether bonds are rich, cheap, or fair. Focus on routing tool outputs into spread decomposition and scenario tables — let the tools compute, you synthesize and recommend.
Relative value is about whether a bond's spread adequately compensates for its risks relative to comparable instruments. Always decompose total spread into risk-free + credit + residual components. The residual (what's left after rates and credit) reveals true richness or cheapness. Stress test with scenarios to confirm the view holds under different rate environments.
1. **Price the Bond(s):** Call `bond_price` for target and any comparison bonds. Extract yield, Z-spread, duration, convexity, DV01. 2. **Get Risk-Free Curve:** Call `interest_rate_curve` (list then calculate) for the bond's currency. Interpolate at bond maturity to compute G-spread. 3. **Get Credit Curve:** Call `credit_curve` for the issuer's country and type. Extract credit spread at the bond's maturity. Compute residual spread = G-spread minus credit curve spread. 4. **Run Scenarios:** Call `yieldbook_scenario` with parallel shifts (-100bp, -50bp, 0, +50bp, +100bp). Extract price changes and P&L per scenario. 5. **Historical Context (optional):** Call `tscc_historical_pricing_summaries` for the bond to assess where current spread sits vs history. 6. **Synthesize:** Combine spread decomposition, scenario results, and historical context into a rich/cheap assessment.
| Component | Spread (bp) | % of Total | |-----------|-------------|------------| | G-spread (total over govt) | ... | 100% | | Credit curve spread | ... | ...% | | Residual (liquidity + technicals) | ... | ...% |
| Scenario | Price Change | P&L (per 100 notional) | |----------|-------------|----------------------| | -100bp | ... | ... | | -50bp | ... | ... | | Base | ... | ... | | +50bp | ... | ... | | +100bp | ... | ... |
State the primary spread metric, its historical context (percentile, comparison to averages), the residual spread signal, and a clear recommendation: rich (avoid/underweight), cheap (buy/overweight), or fair (neutral). Quantify how many bp of spread move would change the recommendation.
Reference agents, skills, and data connectors for the financial-services workflows we see most — investment banking, equity research, private equity, and wealth management.
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