Machine Learning in Finance: Volatility Estimation and Strategies

This lecture by the instructor covers the applications of machine learning for volatility estimation and quantitative strategies in finance. It explains the Vapnik-Chervonenkis (VC) dimension, PAC learning for systematic trading, and the changing profile of quantitative strategies in different market regimes. The risk profiles of HFR Bank Systematic Risk Premia Multi-Asset Index and SG Trend-following CTAS are also discussed, highlighting the difference between amateur and professional applications of machine learning methods.