A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7287

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DC Field	Value	Language
dc.creator	Hutchinson, James M.	-
dc.creator	Lo, Andrew	-
dc.creator	Poggio, Tomaso	-
dc.date	2004-10-22T20:14:45Z	-
dc.date	2004-10-22T20:14:45Z	-
dc.date	1994-04-01	-
dc.date.accessioned	2013-10-09T02:48:58Z	-
dc.date.available	2013-10-09T02:48:58Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1471	-
dc.identifier	http://hdl.handle.net/1721.1/7287	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis functions, multilayer perceptrons, and projection pursuit. To illustrate practical relevance, we also apply our approach to S&P 500 futures options data from 1987 to 1991.	-
dc.format	397765 bytes	-
dc.format	1887637 bytes	-
dc.format	application/octet-stream	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1471	-
dc.title	A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks	-
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