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Trading Signals In VIX Futures

Author

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  • M. Avellaneda
  • T. N. Li
  • A. Papanicolaou
  • G. Wang

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.

Suggested Citation

  • M. Avellaneda & T. N. Li & A. Papanicolaou & G. Wang, 2021. "Trading Signals In VIX Futures," Papers 2103.02016, arXiv.org, revised Nov 2021.
    • Handle: RePEc:arx:papers:2103.02016
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    References listed on IDEAS

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    1. M. Avellaneda & A. Papanicolaou, 2019. "Statistics Of Vix Futures And Applications To Trading Volatility Exchange-Traded Products," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-30, February.
    2. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    3. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    4. P. Burman & D. Nolan, 1992. "Data‐Dependent Estimation Of Prediction Functions," Journal of Time Series Analysis, Wiley Blackwell, vol. 13(3), pages 189-207, May.
    5. Christian Bayer & Peter Friz & Jim Gatheral, 2016. "Pricing under rough volatility," Quantitative Finance, Taylor & Francis Journals, vol. 16(6), pages 887-904, June.
    6. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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    Cited by:

    1. Rama Cont, 2023. "In memoriam: Marco Avellaneda (1955–2022)," Mathematical Finance, Wiley Blackwell, vol. 33(1), pages 3-15, January.

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