We tested whether reasoning-focused LLMs can implement Black-Scholes, calibrate local vol, and price exotic options from scratch.
Large language models can generate correct financial code. But can they reason about stochastic calculus and produce correct pricing for path-dependent options?
We gave DeepSeek-R1 three progressively harder tasks:
| Task | Accuracy | Time |
|---|---|---|
| Black-Scholes | 100% | 30s |
| Barrier MC | 97.2% | 2 min |
| Heston Calibration | 89.4% | 8 min |
The model excels at closed-form solutions and standard MC. Where it struggles: ensuring numerical stability in optimization, handling edge cases in barrier monitoring, and correctly implementing Sobol sequences for quasi-random sampling.
Our conclusion: LLMs are excellent coding assistants for quant work but still require human verification for numerical edge cases.
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