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

Author(s)
Hutchinson, James M.; Lo, Andrew; Poggio, Tomaso
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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.
Date issued
1994-04-01
URI
http://hdl.handle.net/1721.1/7287
Other identifiers
AIM-1471
Series/Report no.
AIM-1471

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  • AI Memos (1959 - 2004)

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