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

51 Pages Posted: 16 Aug 2000 Last revised: 13 Sep 2004

See all articles by James M. Hutchinson

James M. Hutchinson

Paralation Capital LLC

Andrew W. Lo

Massachusetts Institute of Technology (MIT) - Sloan School of Management; National Bureau of Economic Research (NBER); Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Tomaso Poggio

Massachusetts Institute of Technology (MIT) - Department of Brain and Cognitive Sciences

Date Written: April 1994

Abstract

We propose a nonparametric method for estimating the pricing formula of a derivative asset using learning networks. Although not a substitute for the more traditional arbitrage-based pricing formulas, network pricing formulas may be more accurate and computationally more efficient alternatives when the underlying asset's price dynamics are unknown, or when the pricing equation associated with no-arbitrage condition cannot be solved analytically. To assess the potential value of network pricing formulas, we 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 function networks, multilayer perceptron networks, and projection pursuit. To illustrate the practical relevance of our network pricing approach, we apply it to the pricing and delta-hedging of S&P 500 futures options from 1987 to 1991.

Suggested Citation

Hutchinson, James M. and Lo, Andrew W. and Poggio, Tomaso, A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks (April 1994). NBER Working Paper No. w4718. Available at SSRN: https://ssrn.com/abstract=236673

James M. Hutchinson

Paralation Capital LLC

53 Stonehedge Rd
Lincoln, MA 01773
United States

Andrew W. Lo (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-618
Cambridge, MA 02142
United States
617-253-0920 (Phone)
781 891-9783 (Fax)

HOME PAGE: http://web.mit.edu/alo/www

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Massachusetts Institute of Technology (MIT) - Computer Science and Artificial Intelligence Laboratory (CSAIL)

Stata Center
Cambridge, MA 02142
United States

Tomaso Poggio

Massachusetts Institute of Technology (MIT) - Department of Brain and Cognitive Sciences ( email )

Artificial Intelligence Labratory
Cambridge, MA 02139
United States
(617) 253-5230 (Phone)

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