Generalized Correlation Measures of Causality and Forecasts of the VIX Using Non-Linear Models

The paper features an analysis of causal relations between the VIX, S&P500, and the realised volatility (RV) of the S&P500 sampled at 5 minute intervals, plus the application of an Artificial Neural Network (ANN) model to forecast the VIX. Causal relations are analysed using the recently developed concept of general correlation Zheng et al. (2012) and Vinod (2017). The neural network analysis is performed using the Group Method of Data Handling (GMDH) approach. The results suggest that causality runs from lagged daily RV and lagged continuously compounded return on the S&P500 index to the VIX. Out of sample tests suggest an ANN model can successfully predict the VIX using lagged RV and lagged S&P500 Index continuously compounded returns as inputs