{"title":"A GCN-LSTM Approach for ES-mini and VX Futures Forecasting","authors":"Nikolas Michael, Mihai Cucuringu, Sam Howison","doi":"arxiv-2408.05659","DOIUrl":null,"url":null,"abstract":"We propose a novel data-driven network framework for forecasting problems\nrelated to E-mini S\\&P 500 and CBOE Volatility Index futures, in which products\nwith different expirations act as distinct nodes. We provide visual\ndemonstrations of the correlation structures of these products in terms of\ntheir returns, realized volatility, and trading volume. The resulting networks\noffer insights into the contemporaneous movements across the different\nproducts, illustrating how inherently connected the movements of the future\nproducts belonging to these two classes are. These networks are further\nutilized by a multi-channel Graph Convolutional Network to enhance the\npredictive power of a Long Short-Term Memory network, allowing for the\npropagation of forecasts of highly correlated quantities, combining the\ntemporal with the spatial aspect of the term structure.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"24 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.05659","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.