A GCN-LSTM Approach for ES-mini and VX Futures Forecasting

TL;DR

This paper introduces a GCN-LSTM framework that models and forecasts E-mini S&P 500 and CBOE Volatility Index futures by capturing their interconnected dynamics through graph networks and deep learning.

Contribution

It presents a novel combination of graph convolutional networks with LSTM for futures forecasting, leveraging correlation structures among different expiration products.

Findings

Networks reveal strong correlations among futures products.

The GCN-LSTM model improves forecasting accuracy.

Insights into the interconnectedness of futures markets.

Abstract

We propose a novel data-driven network framework for forecasting problems related to E-mini S\&P 500 and CBOE Volatility Index futures, in which products with different expirations act as distinct nodes. We provide visual demonstrations of the correlation structures of these products in terms of their returns, realized volatility, and trading volume. The resulting networks offer insights into the contemporaneous movements across the different products, illustrating how inherently connected the movements of the future products belonging to these two classes are. These networks are further utilized by a multi-channel Graph Convolutional Network to enhance the predictive power of a Long Short-Term Memory network, allowing for the propagation of forecasts of highly correlated quantities, combining the temporal with the spatial aspect of the term structure.