Accession Number : ADA279879

Title :   A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks.

Descriptive Note : Technical memo.,

Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Personal Author(s) : Hutchinson, James M. ; Lo, Andrew ; Poggio, Tomaso

Report Date : APR 1994

Pagination or Media Count : 32

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. Option pricing, Learning, Finance, Black-Scholes, Hedging

Descriptors :   *FINANCE, *NONPARAMETRIC STATISTICS, *INVESTMENTS, APPROACH, COMPARISON, ESTIMATES, LEARNING, MODELS, NETWORKS, TRAINING, FORMULAS(MATHEMATICS).

Subject Categories : Statistics and Probability
      Economics and Cost Analysis

Distribution Statement : APPROVED FOR PUBLIC RELEASE