Trading Signals In VIX Futures
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 33 references that are in the map. First posted on arXiv 2 Mar 2021.
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References · 8
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- 2016Stochastic Gradient Descent in Continuous Timecited by 14
- 2019Deep Q-Learning for Nash Equilibria: Nash-DQNcited by 14
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