Options & Derivatives Blogs and Articles

Machine Learning for Derivative Pricing and Crash Prediction

Recent research demonstrates that machine learning can dramatically accelerate the pricing of complex derivatives and improve crash‑prediction analytics. A two‑stage framework using a Gaussian Process Regressor (GPR) trained on full volatility‑surface inputs delivers near‑instant valuations with sub‑percent errors for variance swaps and American puts, cutting computation time by three to four orders of magnitude. Parallel work compares logistic regression, random forest, and LSTM models for forecasting market crashes, finding that LSTM offers the best precision‑recall balance while logistic regression provides a high‑recall early‑warning signal. The author will discuss these advances at the Futures Alpha Conference in New York.