Using Neural Networks to Forecast the Implied Volatility: the Case of S&P100 XEO

TitleUsing Neural Networks to Forecast the Implied Volatility: the Case of S&P100 XEO
Publication TypeJournal Article
Year of Publication2014
Date PublishedMarch 2014
JournalSoutheast Europe Journal of Soft Computıng
Volume3
Issue1
Section10
Pagination8
Publication Languageeng
AuthorsCan, M, Fadda, S
Abstract

Currently the most popular method of estimating volatility is the implied volatility. It is calculated using the Black-Scholes option price formula, and is considered by traders to be a significant factor in signaling price movements in the underlying market. A trader is able to establish the proper strategic position in anticipation of changes in market trends if she/he could   accurately forecast future volatility. There is an abundance of ways to compute the volatility. For two decades neural networks has been developed to forecast future volatility, using past volatilities and other options market factors. In this article a network is created for this purpose whose performance demonstrates the value of neural networks as a predictive tool in volatility analysis.