Trading Signals in VIX Futures

Marco Avellaneda, Thomas Nanfeng Li, Andrew Papanicolaou, Gaozhan Wang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)275-298
Number of pages24
JournalApplied Mathematical Finance
Volume28
Issue number3
DOIs
StatePublished - 2021

Keywords

  • contango
  • cross validation
  • deep learning
  • feedforward neural networks
  • trading signals
  • VIX futures

ASJC Scopus subject areas

  • Finance
  • Applied Mathematics

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