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Financial Analyst - Risk & Quant

Perform CFA-level quantitative risk analysis and portfolio optimization for institutional portfolios.

7|1|Updated Feb 9, 2026
Search Tags:#stress-testing#risk-analysis#portfolio-optimization#black-litterman#factor-attribution#var-cvar#credit-analytics
One-click install
npx skills add https://github.com/fall-development-rob/corp_finance --skill financial-analyst-risk-quant
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Skill: Financial Analyst - Risk & Quant
Source: https://github.com/fall-development-rob/corp_finance/tree/main/.claude/skills/corp-finance-analyst-risk
Command: npx skills add https://github.com/fall-development-rob/corp_finance --skill financial-analyst-risk-quant

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns Claude into a CFA-equivalent quantitative risk and portfolio analyst capable of producing institutional-grade attributions, optimised allocations, risk budgets, stress tests, and execution plans with transparent assumptions and diagnostics. It removes the manual, error-prone workflow of stitching together factor models, optimisation routines, tail-risk measures, credit analytics, and macro signals by pairing expert reasoning with corp-finance MCP computation tools.

Core Features & Use Cases

  • Factor attribution & decomposition: multi-factor (CAPM, Fama-French, Carhart) attribution with R², alpha, and marginal risk contributions.
  • Portfolio optimisation & Black-Litterman: implied equilibrium returns, view incorporation, posterior returns and mean-variance/BL-derived weights.
  • Risk budgeting & tail risk: ERC / inverse-vol allocations, parametric/historical VaR, Cornish-Fisher adjustments, and CVaR component analysis.
  • Stress testing & credit analytics: historical and hypothetical scenarios, Gaussian copula credit VaR, rating migration P&L and granularity adjustments.
  • Market microstructure & execution: spread decomposition, Kyle lambda, Almgren-Chriss execution scheduling, VWAP/TWAP/IS strategies.
  • Index construction & smart beta: weighting schemes, rebalancing, tracking error estimation, reconstitution buffers and turnover analysis.
  • Use case: Run a full pre-trade portfolio change: attribute current risk to factors, propose BL-tilted weights, compute expected tracking error and CVaR, and produce an execution schedule.

Quick Start

Ask Claude to perform a factor attribution on a portfolio using monthly returns and factor returns, then generate a Black-Litterman posterior and an optimised portfolio with CVaR stress tests.

Frequently Asked Questions about Financial Analyst - Risk & Quant

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

FAQPage Schema
How do I run a Black-Litterman portfolio optimization with custom views?▼

To perform factor attribution, you provide monthly portfolio returns and factor returns to decompose risk using multi-factor models like CAPM, Fama-French, or Carhart. The analysis outputs R-squared, alpha, and marginal risk contributions for each factor.

How do I calculate VaR and CVaR for a portfolio with Cornish-Fisher adjustments?▼

VaR and CVaR calculations use parametric or historical methods with Cornish-Fisher adjustments to account for non-normal return distributions. The process accepts return series and covariance matrices to produce tail risk metrics and component CVaR analysis.

Can I stress test a credit portfolio using Gaussian copula and rating transition matrices?▼

Yes, credit portfolio stress testing supports Gaussian copula credit VaR, rating migration P&L, and granularity adjustments. You input transition matrices and scenario shock vectors to generate stressed loss distributions and credit analytics diagnostics.

How do I schedule optimal execution using Almgren-Chriss for institutional trades?▼

Almgren-Chiss execution scheduling optimizes institutional trade execution by balancing market impact and timing risk. The analysis incorporates Kyle lambda and spread decomposition to produce VWAP, TWAP, or implementation shortfall strategies with optimal execution trajectories.

What data inputs are needed for risk parity and ERC portfolio allocations?▼

Risk parity and equal risk contribution allocations require covariance matrices or return series as primary inputs. The optimization produces inverse-volatility weights and risk budgets that equalize marginal risk contributions across portfolio assets.

How do I estimate tracking error for a smart beta index construction?▼

Smart beta index construction estimates tracking error by comparing custom weighting schemes and rebalancing rules against a benchmark. You input constituent returns and weights to receive tracking error metrics, turnover analysis, and reconstitution buffer recommendations.

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