Trading Signals In VIX Futures

2021/03/02 by M. Avellaneda, Avellaneda, M., T. N. Li +5
Decision Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Machine Learning (stat.ML) #Stock Market Forecasting Methods #Trading and Market Microstructure (q-fin.TR)

paper · pdf · doi:10.48550/arxiv.2103.02016

openalex publication_date 2021/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a new approach for trading VIX futures. We assume that the term structure of VIX futures follows a Markov model. Our trading strategy selects a position in VIX futures by maximizing the expected utility for a day-ahead horizon given the current shape and level of the term structure. Computationally, we model the functional dependence between the VIX futures curve, the VIX futures positions, and the expected utility as a deep neural network with five hidden layers. Out-of-sample backtests of the VIX futures trading strategy suggest that this approach gives rise to reasonable portfolio performance, and to positions in which the investor will be either long or short VIX futures contracts depending on the market environment.

Citations

• On the use of cross-validation for time series predictor evaluation
• To understand deep learning we need to understand kernel learning

Related

• Tracking VIX with VIX Futures: Portfolio Construction and Performance
• The VIX index under scrutiny of machine learning techniques and neural networks
• A general framework for a joint calibration of VIX and VXX options
• A Market Model for VIX Futures
• Trading VIX Futures under Mean Reversion with Regime Switching