Neural networks and arbitrage in the VIX

Joerg Osterrieder (), Daniel Kucharczyk (), Silas Rudolf () and Daniel Wittwer ()
Additional contact information
Joerg Osterrieder: Zurich University of Applied Sciences
Daniel Kucharczyk: Wroclaw University of Science and Technology
Silas Rudolf: Nexoya Ltd.
Daniel Wittwer: AGCO International GmbH

Digital Finance, 2020, vol. 2, issue 1, No 5, 97-115

Abstract: 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.

Keywords: VIX; SPX; Neural network; LSTM; Deep learning; Arbitrage; Market manipulation; Random forests (search for similar items in EconPapers)
JEL-codes: A00 C00 G00 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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DOI: 10.1007/s42521-020-00026-y

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