The VIX index under scrutiny of machine learning techniques and neural networks

Hirsa, Ali; Osterrieder, Jörg Robert; Misheva, Branka Hadji; Cao, Wenxin; Fu, Yiwen; Sun, Hanze; Wong, Kin Wai (2021). The VIX index under scrutiny of machine learning techniques and neural networks Cornell University 10.48550/arXiv.2102.02119

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The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the market's expected volatility on the SP 500 Index, calculated and published by the Chicago Board Options Exchange (CBOE). It is also often referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the SP 500 index over the following 30 days, based on options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE's Volatility Index is prone to inadvertent and deliberate errors because it is weighted average of out-of-the-money calls and puts which could be illiquid. Many claims of market manipulation have been brought up against VIX in recent years. This paper discusses several approaches to replicate the VIX index as well as VIX futures by using a subset of relevant options as well as neural networks that are trained to automatically learn the underlying formula. Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, we will show that a small number of options is sufficient to replicate the VIX index. Once we are able to actually replicate the VIX using a small number of SP options we will be able to exploit potential arbitrage opportunities between the VIX index and its underlying derivatives. The results are supposed to help investors to better understand the options market, and more importantly, to give guidance to the US regulators and CBOE that have been investigating those manipulation claims for several years.

Item Type:	Working Paper
Division/Institute:	Business School > Institute for Applied Data Science & Finance Business School
Name:	Hirsa, Ali; Osterrieder, Jörg Robert0000-0003-0189-8636; Misheva, Branka Hadji; Cao, Wenxin; Fu, Yiwen; Sun, Hanze and Wong, Kin Wai
Subjects:	H Social Sciences > HG Finance
Publisher:	Cornell University
Language:	English
Submitter:	Jörg Robert Osterrieder
Date Deposited:	29 Aug 2022 14:54
Last Modified:	29 Aug 2022 15:21
Publisher DOI:	10.48550/arXiv.2102.02119
Uncontrolled Keywords:	VIX · Machine Learning · Deep Learning · VIX futures
ARBOR DOI:	10.24451/arbor.17417
URI:	https://arbor.bfh.ch/id/eprint/17417