Cluster G: Advanced and Quant Topics
Exotic options, stochastic volatility, market microstructure, systematic strategy design, and machine learning. The quantitative frontier.
Exotic Options
Barriers, Asians, lookbacks, path-dependent payoffs, pricing challenges
Jump-Diffusion Models
Merton jump-diffusion, Kou double-exponential, calibration to skew, impact on short-dated options
Rough Volatility
Rough Heston, fractional Brownian motion, why vol is rougher than diffusion, term structure implications
Model Calibration and Validation
Fitting models to market data, overfitting dangers, out-of-sample testing, recalibration frequency
Market Microstructure for Options
Maker-taker dynamics, queue priority, informed flow detection, options market maker inventory management
Systematic Options Strategy Design
Factor-based strategy design, backtesting options strategies (challenges and pitfalls), survivorship bias, lookahead bias
Python for Options: Building a Pricing Library
Implementing BSM, binomial trees, Monte Carlo, Greeks computation, vol surface fitting in Python
Machine Learning in Options Markets
Feature engineering from options data, vol forecasting with ML, limitations and overfitting risks