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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Neural networks and arbitrage in the VIX
Authors: Osterrieder, Jörg
Kucharczyk, Daniel
Rudolf, Silas
Wittwer, Daniel
et. al: No
DOI: 10.1007/s42521-020-00026-y
Published in: Digital Finance
Volume(Issue): 2
Issue: 1-2
Page(s): 97
Pages to: 115
Issue Date: 13-Aug-2020
Publisher / Ed. Institution: Springer
ISSN: 2524-6186
Language: English
Subjects: Arbitrage; Deep learning; LSTM; Market manipulation; Neural network; Random forests; SPX; VIX
Subject (DDC): 006: Special computer methods
332: Financial economics
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.
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Engineering
Organisational Unit: Institute for Financial Management (IFI)
Appears in collections:Publikationen School of Engineering

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Osterrieder, J., Kucharczyk, D., Rudolf, S., & Wittwer, D. (2020). Neural networks and arbitrage in the VIX. Digital Finance, 2(1-2), 97–115.
Osterrieder, J. et al. (2020) ‘Neural networks and arbitrage in the VIX’, Digital Finance, 2(1-2), pp. 97–115. Available at:
J. Osterrieder, D. Kucharczyk, S. Rudolf, and D. Wittwer, “Neural networks and arbitrage in the VIX,” Digital Finance, vol. 2, no. 1-2, pp. 97–115, Aug. 2020, doi: 10.1007/s42521-020-00026-y.
OSTERRIEDER, Jörg, Daniel KUCHARCZYK, Silas RUDOLF und Daniel WITTWER, 2020. Neural networks and arbitrage in the VIX. Digital Finance. 13 August 2020. Bd. 2, Nr. 1-2, S. 97–115. DOI 10.1007/s42521-020-00026-y
Osterrieder, Jörg, Daniel Kucharczyk, Silas Rudolf, and Daniel Wittwer. 2020. “Neural Networks and Arbitrage in the VIX.” Digital Finance 2 (1-2): 97–115.
Osterrieder, Jörg, et al. “Neural Networks and Arbitrage in the VIX.” Digital Finance, vol. 2, no. 1-2, Aug. 2020, pp. 97–115,

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