Derivatives Pricing Model Builder
Build production-ready derivatives pricing models with analytical and numerical methods
What You Can Do
You can architect and implement derivatives pricing frameworks using analytical solutions (Black-Scholes), tree methods (binomial/trinomial), and Monte Carlo simulations. Claude generates production-ready Python or C++ code, calibrates volatility surfaces to market data, performs sensitivity analysis, and stress-tests models against edge cases—reducing iteration cycles while maintaining mathematical rigor and compliance documentation.
Features
Generate pricing models for European/American options, barrier options, basket options, swaptions, and path-dependent instruments
Receive production-ready Python or C++ implementations with optimized algorithms for computational efficiency
Construct and calibrate implied volatility surfaces from market data with multi-dimensional interpolation and smoothing
Leverage Black-Scholes, binomial trees, trinomial trees, and Monte Carlo simulations with appropriate method selection
Perform Greeks sensitivity analysis, backtesting against historical data, and consistency checks across pricing methods
Test model robustness under extreme market conditions, jump scenarios, and correlation shifts
Build Vasicek, Hull-White, LIBOR market models, and multi-curve calibration frameworks
Develop credit valuation adjustment engines with exposure simulation and default probability curves
Example Output
Example 1: Exotic Option Pricing Model
Input: Price a barrier option on a basket of 3 equities with daily monitoring
Output:
- Mathematical formulation: Risk-neutral valuation with Monte Carlo simulation specification
- Python implementation: 250-line module with variance reduction (control variate), correlation matrix handling
- Calibration: Implied volatility extraction from market quotes, correlation coefficient estimation
- Validation report: Greeks convergence analysis, computational performance metrics (ms/price)
Example 2: Volatility Surface Builder
Input: Build 2D IV surface from option chain data (30 strikes × 8 expirations)
Output:
- Surface model: Natural cubic spline with regularization constraints
- Code: Fitting algorithm with outlier detection and smoothness penalties
- Diagnostics: Butterfly spread violations flagged, implied volatility smile characteristics
- Interpolation: Method for pricing off-grid strikes and maturities
Example 3: Model Stress Testing
Input: Validate interest rate model under 2008-crisis scenarios
Output:
- Scenario payoff matrix: 100 historical + 50 hypothetical stress paths
- Comparison metrics: Model prices vs. market benchmarks, basis point deviations
- Risk report: Maximum loss exposure, tail risk (99th percentile), correlation breakdowns
What's Included
- SKILL.md instruction file: Detailed prompt engineering for model architecture, calibration workflows, and validation procedures
- Pricing model templates: Starter code structures for Black-Scholes, binomial trees, Monte Carlo engines with placeholder parameters
- Calibration checklist: Step-by-step workflow for extracting market data, fitting volatility surfaces, and managing convergence criteria
- Validation framework: Greeks sensitivity analysis template, backtesting methodology, stress scenario specifications
- Code generation guidelines: Best practices for numerical stability, vectorization, and performance optimization across Python/C++
Who It's For
- Quantitative analysts building new derivatives pricing infrastructure or migrating legacy systems
- Risk managers validating third-party pricing libraries and implementing stress-testing frameworks
- Traders needing rapid prototyping of exotic option strategies and real-time valuation models
- Fixed income specialists developing interest rate models and multi-curve calibration systems
- Model validators and independent pricing teams conducting governance and backtesting reviews
Best For
- Implementing analytical solutions (Black-Scholes, closed-form formulas) for vanilla derivatives
- Building tree-based models (binomial, trinomial) for American options and path-dependent payoffs
- Developing Monte Carlo simulation engines with correlation structures and jump processes
- Calibrating multi-dimensional volatility surfaces and smile dynamics to market data
- Performing Greeks computation, sensitivity analysis, and model robustness testing
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