Monte-Carlo Risk Surrogates

overview.md

Estimating portfolio risk the honest way — a 100-million-sample Monte Carlo run — is accurate but far too slow to sit inside an interactive workflow. This project trains Gaussian-process and neural-network surrogates on that expensive simulation so the same VaR and CVaR numbers come back in milliseconds, with sub-1% error against the full baseline.

problem.txt

Monte Carlo is the ground truth for tail risk, but it doesn't fit a feedback loop — you can't re-run 100M paths every time an analyst nudges a parameter. The question: can a learned model reproduce the simulator's risk numbers closely enough to replace it at interactive speed?

architecture.drawio

1. Simulate — a 100M-sample Monte Carlo engine produces ground-truth VaR / CVaR over sampled portfolios.
2. Train — Gaussian-process and neural-network surrogates learn the mapping from portfolio inputs to risk.
3. Evaluate — surrogate estimates are scored against the held-out simulation baseline.
4. Serve — a single forward pass replaces the simulation at query time.

challenges.log

• Sampling portfolios densely enough to cover the tail behaviour that drives VaR.
• Trading off GP calibration (great uncertainty, slower) against NN speed.
• Validating that 'within 1%' holds across portfolios, not just on average.

results.json

lessons-learned.md

• A surrogate is only as good as the regions of input space you sampled — tails need oversampling.
• Two model families answer different questions: GPs for trust, NNs for throughput.
• Speed unlocks a different product: risk becomes interactive instead of a nightly batch.

future-work.md

• Active learning to sample where the surrogate is least certain.
• Extend to path-dependent and exotic instruments.
• Confidence-aware fallback to full MC when the surrogate is unsure.