Overview
What this challenge is about.
Simulate price paths, train a neural net to hedge American options, compare with Longstaff-Schwartz, and earn a verifiable certificate.
The scenario
The fund (around USD 4 billion assets under management, 18 researchers) hedges a book of equity-linked structured products and is exploring whether to switch from analytic Greek hedging to a learned hedger for high-transaction-cost names.
The Brief
What you'll do, and what you'll demonstrate.
Build a reference notebook comparing deep hedging to Longstaff-Schwartz Monte Carlo on a vanilla American put with realistic transaction costs.
Earning criteria — what you'll demonstrate
- Implement Longstaff-Schwartz Monte Carlo for American option pricing
- Train a deep hedger with a variance-plus-cost objective
- Design a fair comparison between a classical and a learning-based method
- Write clean research commentary new colleagues can learn from
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
AI and Quantitative Finance
Master · Applied Ai
Strong alignment
This challenge maps to AI and Quantitative Finance at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Deep Learning
Design and train neural networks for complex pattern recognition tasks.
- Stochastic Modeling
Apply stochastic modeling to solve real industry problems and demonstrate production-level capability.
- Derivatives Pricing
Apply derivatives pricing to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Monte Carlo Methods
Apply monte carlo methods to solve real industry problems and demonstrate production-level capability.
- Research Writing
Apply research writing to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
ML Researcher
Reproducing a paper and writing honest commentary on its limits is the literal day-one task for an ML researcher inside a quant fund.
This challenge sharpens
- deep-learning
- research-writing
- monte-carlo-methods
Research Scientist
Designing a fair comparison and grounding it in primary literature is exactly the rigor expected from a junior research scientist's first internal review.
This challenge sharpens
- stochastic-modeling
- derivatives-pricing
- research-writing