analyzing-correlation-trading

Structures correlation analysis with index vs. tranche pricing and correlation skew assessment. Use when analyzing correlation products, pricing tranches, or evaluating dispersion trades.

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Best use case

analyzing-correlation-trading is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Structures correlation analysis with index vs. tranche pricing and correlation skew assessment. Use when analyzing correlation products, pricing tranches, or evaluating dispersion trades.

Teams using analyzing-correlation-trading should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/analyzing-correlation-trading/SKILL.md --create-dirs "https://raw.githubusercontent.com/CaseMark/skills/main/skills/finance/analyzing-correlation-trading/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/analyzing-correlation-trading/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How analyzing-correlation-trading Compares

Feature / Agentanalyzing-correlation-tradingStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Structures correlation analysis with index vs. tranche pricing and correlation skew assessment. Use when analyzing correlation products, pricing tranches, or evaluating dispersion trades.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Analyzing Correlation Trading

## When To Use

- Pricing or relative-value assessment of synthetic CDO tranches (equity, mezzanine, senior)
- Evaluating index vs. single-name basis (e.g., CDX.NA.IG/HY vs. constituent CDS)
- Analyzing correlation skew across the capital structure of a credit index
- Structuring or assessing dispersion trades (long index protection / short single-name protection, or vice versa)
- Reviewing implied correlation surfaces for mark-to-market or risk reporting
- Stress-testing a correlation book against regime shifts (crisis spikes, mean-reversion)

## Inputs To Gather

- **Index data**: Current and historical spreads for the relevant index (CDX, iTraxx) and series/roll
- **Tranche quotes**: Mid-market prices or upfronts for standard tranches (e.g., 0-3%, 3-7%, 7-15%, 15-30%, 30-100% for CDX.IG)
- **Single-name CDS spreads**: Constituent spreads, recovery rate assumptions, and any jump-to-default premia
- **Implied correlation levels**: Base correlations or compound correlations per tranche from dealer runs or internal models
- **Historical realized correlation**: Pairwise or average asset-correlation estimates from equity proxies or CDS co-movements over chosen lookback windows
- **Model parameters**: Copula type (Gaussian, Student-t, Marshall-Olkin), number of simulation paths, granularity of loss distribution [VERIFY: confirm model convention used by the desk]
- **Market context**: Recent credit events, index rolls, on-the-run vs. off-the-run dynamics, funding/repo costs

## Workflow

1. **Reconstruct the base correlation curve**
   - Compute compound correlation for each tranche using the standard Gaussian copula and quoted tranche spreads/upfronts
   - Convert to base correlation by bootstrapping from equity tranche up through super-senior
   - Flag any non-monotonicity in the base correlation curve — this indicates arbitrage or stale quotes

2. **Assess correlation skew**
   - Compare current base correlation levels to 3-month, 6-month, and 1-year historical ranges
   - Identify whether equity tranche implied correlation is rich or cheap relative to mezzanine/senior
   - Note skew steepness: a flat skew suggests low differentiation between subordination levels; a steep skew signals tail-risk repricing

3. **Index vs. intrinsics analysis**
   - Calculate the theoretical index spread from the sum of single-name constituents weighted by notional
   - Compute the index basis (market index spread minus intrinsic spread)
   - A persistently negative basis (index trading tight to intrinsics) may indicate correlation supply; a positive basis suggests demand for index protection or single-name tightening

4. **Dispersion trade evaluation**
   - Define the trade structure: long index vs. short basket of single names (or reverse), specifying delta-neutral notionals
   - Estimate P&L sensitivity to parallel spread moves (DV01), idiosyncratic spread moves, and correlation changes (CR01)
   - Model carry and roll-down under stable-spread scenarios
   - Stress-test against historical episodes: 2008 credit crisis, 2011 European sovereign stress, 2020 COVID dislocation [VERIFY: update with most recent relevant stress period]

5. **Risk decomposition**
   - Break total tranche risk into spread risk (CS01), default risk (jump-to-default), and correlation risk (CR01)
   - Quantify gamma/convexity for equity and thin mezzanine tranches — these exhibit significant non-linearity
   - Assess vega exposure if using stochastic-correlation or stochastic-recovery extensions

6. **Compile findings and relative-value view**
   - Summarize whether the current skew environment favors long or short correlation positioning
   - Provide specific trade recommendations with entry levels, target P&L, stop-loss thresholds, and horizon
   - Note key risks: credit event clustering, sudden correlation spikes, liquidity withdrawal in off-the-run series

## Output

- **Correlation Surface Summary**: Table of base correlations by tranche detachment with historical percentile ranks
- **Skew Analysis**: Chart-ready data showing current vs. historical skew with commentary on regime
- **Index Basis Report**: Intrinsic vs. market spread, basis time series, and drivers
- **Dispersion Trade P&L Profile**: Scenario table with carry, spread, default, and correlation P&L columns
- **Risk Dashboard**: CS01, CR01, JTD, and gamma by tranche or position
- **Trade Recommendation**: Directional view with rationale, sizing, and risk limits

## Quality Checks

- Base correlation curve must be monotonically increasing from equity to super-senior — if not, investigate quote staleness or interpolation error
- Sum of tranche expected losses must equal the index expected loss (no-arbitrage constraint)
- CR01 signs must be internally consistent: long equity tranche = long correlation; long senior tranche = short correlation
- Verify recovery rate assumptions match market convention (typically 40% for IG, 25-30% for HY) [VERIFY: confirm current market standard]
- Confirm index series and roll date — mixing on-the-run and off-the-run quotes invalidates basis calculations
- Stress scenarios should include at least one idiosyncratic (single-name blowup) and one systemic (market-wide spread widening) event
- All implied correlations should fall within [0, 1]; values near boundaries indicate model breakdown or extreme market conditions requiring manual review

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