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Neural networks and arbitrage in the VIX

Author

Listed:
  • Joerg Osterrieder

    (Zurich University of Applied Sciences)

  • Daniel Kucharczyk

    (Wroclaw University of Science and Technology)

  • Silas Rudolf

    (Nexoya Ltd.)

  • Daniel Wittwer

    (AGCO International GmbH)

Abstract

The Chicago Board Options Exchange Volatility Index (VIX) is considered by many market participants as a common measure of market risk and investors’ sentiment, representing the market’s expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options on the S&P 500 index. While smaller deviations between implied and realized volatility are a well-known stylized fact of financial markets, large, time-varying differences are also frequently observed throughout the day. Furthermore, substantial deviations between the VIX and its futures might lead to arbitrage opportunities on the VIX market. Arbitrage is hard to exploit as the potential strategy to exploit it requires buying several hundred, mostly illiquid, out-of-the-money (put and call) options on the S&P 500 index. This paper discusses a novel approach to predicting the VIX on an intraday scale by using just a subset of the most liquid options. To the best of the authors’ knowledge, this the first paper, that describes a new methodology on how to predict the VIX (to potentially exploit arbitrage opportunities using VIX futures) using most recently developed machine learning models to intraday data of S&P 500 options and the VIX. The presented results are supposed to shed more light on the underlying dynamics in the options markets, help other investors to better understand the market and support regulators to investigate market inefficiencies.

Suggested Citation

  • Joerg Osterrieder & Daniel Kucharczyk & Silas Rudolf & Daniel Wittwer, 2020. "Neural networks and arbitrage in the VIX," Digital Finance, Springer, vol. 2(1), pages 97-115, September.
    • Handle: RePEc:spr:digfin:v:2:y:2020:i:1:d:10.1007_s42521-020-00026-y
    DOI: 10.1007/s42521-020-00026-y
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    References listed on IDEAS

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    1. Chester Spatt, 2014. "Security Market Manipulation," Annual Review of Financial Economics, Annual Reviews, vol. 6(1), pages 405-418, December.
    2. Torben G. Andersen & Oleg Bondarenko & Maria T. Gonzalez-Perez, 2015. "Exploring Return Dynamics via Corridor Implied Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 28(10), pages 2902-2945.
    3. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    2. Nikolas Michael & Mihai Cucuringu & Sam Howison, 2024. "A GCN-LSTM Approach for ES-mini and VX Futures Forecasting," Papers 2408.05659, arXiv.org.
    3. Gunnarsson, Elias Søvik & Isern, Håkon Ramon & Kaloudis, Aristidis & Risstad, Morten & Vigdel, Benjamin & Westgaard, Sjur, 2024. "Prediction of realized volatility and implied volatility indices using AI and machine learning: A review," International Review of Financial Analysis, Elsevier, vol. 93(C).
    4. Ali Hirsa & Joerg Osterrieder & Branka Hadji Misheva & Wenxin Cao & Yiwen Fu & Hanze Sun & Kin Wai Wong, 2021. "The VIX index under scrutiny of machine learning techniques and neural networks," Papers 2102.02119, arXiv.org.

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    More about this item

    Keywords

    VIX; SPX; Neural network; LSTM; Deep learning; Arbitrage; Market manipulation; Random forests;
    All these keywords.

    JEL classification:

    • A00 - General Economics and Teaching - - General - - - General
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • G00 - Financial Economics - - General - - - General

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