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Neural Networks as Semiparametric Option Pricing Tool
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SYSNO ASEP 0367688 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Ostatní články Title Neural Networks as Semiparametric Option Pricing Tool Author(s) Baruník, Jozef (UTIA-B) RID, ORCID
Baruníková, M. (CZ)Number of authors 2 Source Title Bulletin of the Czech Econometric Society - ISSN 1212-074X
Roč. 18, č. 28 (2011), s. 66-83Number of pages 18 s. Language eng - English Country CZ - Czech Republic Keywords option valuation ; neural network ; S&P 500 index options Subject RIV AH - Economics R&D Projects GD402/09/H045 GA ČR - Czech Science Foundation (CSF) GA402/09/0965 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation We study the ability of artificial neural networks to price the European style call and put options on the S&P 500 index covering the daily data for the period from June 2004 to June 2007. We divide the data set into several categories according to moneyness and time to maturity. We then price all options within the categories. The results show that neural networks outperform benchmark ad hoc Black-Scholes model with significantly lower pricing errors across all categories for both call and put options. Moreover, the differences between ad hoc Black-Scholes and neural networks errors widen with deepness of moneyness or longer time to maturity. We show that neural networks, even without the volatility input, can correct for the Black-Scholes maturity and moneyness bias. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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