A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks, Issue 4718

Front Cover
National Bureau of Economic Research, 1994 - Options (Finance) - 49 pages
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. Option pricing, Learning, Finance, Black-Scholes, Hedging.

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