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bond-relative-value

该 Skill 指导固定收益相对价值分析:调用债券定价、政府/互换收益率曲线、信用曲线、情景分析和历史定价等 MCP 工具,拆解 G-spread、信用利差与残差,生成情景损益表,并综合作出债券偏贵、偏便宜或公允的判断与建议。工具负责计算,Skill 侧重编排与综合判断。

anthropics
热度 0.00
最近更新 2026-10-19 02:04:00
分类:数据依赖:MCP 服务应用场景:财务 / 市场分析 / 报告撰写 / 研究

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add anthropics/financial-services/bond-relative-value
Metadata
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.

Bond Relative Value Analysis

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.

Core Principles

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.

Available MCP Tools

  • bond_price — Price bonds. Returns clean/dirty price, yield, duration, convexity, DV01, Z-spread. Accepts ISIN, RIC, or CUSIP.
  • interest_rate_curve — Government and swap yield curves. Two-phase: list then calculate. Use to compute G-spreads.
  • credit_curve — Credit spread curves by issuer type. Two-phase: search by country/issuerType, then calculate. Use to isolate credit component.
  • yieldbook_scenario — Scenario analysis with parallel rate shifts. Returns price change and P&L under each scenario.
  • tscc_historical_pricing_summaries — Historical pricing data. Use for historical spread context and Z-score analysis.
  • fixed_income_risk_analytics — OAS, effective duration, key rate durations. Use for callable bonds and deeper risk decomposition.

Tool Chaining Workflow

  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.

Output Format

Spread Decomposition

Component Spread (bp) % of Total
G-spread (total over govt) ... 100%
Credit curve spread ... ...%
Residual (liquidity + technicals) ... ...%

Scenario P&L

Scenario Price Change P&L (per 100 notional)
-100bp ... ...
-50bp ... ...
Base ... ...
+50bp ... ...
+100bp ... ...

Rich/Cheap Summary

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.