A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks
dc.date.accessioned | 2004-10-22T20:14:45Z | |
dc.date.accessioned | 2018-11-24T10:23:42Z | |
dc.date.available | 2004-10-22T20:14:45Z | |
dc.date.available | 2018-11-24T10:23:42Z | |
dc.date.issued | 1994-04-01 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/7287 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/7287 | |
dc.description.abstract | 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. | en_US |
dc.format.extent | 397765 bytes | |
dc.format.extent | 1887637 bytes | |
dc.language.iso | en_US | |
dc.title | A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks | en_US |
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