Predicting VIX with adaptive machine learning

This paper investigates the predictability of the CBOE Volatility Index (VIX) and explores the sources of its predictability using machine learning (ML) techniques. We establish that daily VIX can be predicted with higher accuracy than previously documented, yielding forecasts of significant economic value. Our analysis underscores the efficacy of dynamic training, nonlinear methods and a comprehensive set of economic variables in predicting VIX trends. We identify the weekly jobless claim data as a pivotal variable, revealing its substantial influence on market volatility, an area not extensively explored in prior research. While accurately forecasting VIX spikes poses a challenge, our algorithms demonstrate remarkable adaptability to new data, thereby significantly enhancing the resilience of trading strategies. This research not only contributes to the understanding of VIX predictability but also offers valuable insights for the development of more robust quantitative investment and risk management strategies.