Machine Learning for Volatility Prediction and Options Trading: A Comprehensive Analysis

This paper examines the application of machine learning (ML) techniques-Support Vector Machines (SVMs), XGBoost, Long Short-Term Memory (LSTM) networks, and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models-in predicting stock market volatility and optimizing options trading strategies, with a particular emphasis on short straddles. Leveraging computational scripts and recent academic literature, the study demonstrates that ML can significantly enhance forecasting accuracy and profitability in trading. Key findings indicate that LSTMs excel in short-term volatility prediction, while SVMs and XGBoost effectively identify profitable trading opportunities based on the Volatility Risk Premium (VRP). The analysis addresses challenges such as data quality, overfitting, and computational complexity, providing a balanced perspective on ML's transformative potential in financial applications.