A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks / James M. Hutchinson, Andrew W. Lo, Tomaso Poggio.

Format:

Book

Author/Creator:

Hutchinson, James M.

Contributor:

National Bureau of Economic Research.

Lo, Andrew W.

Poggio, Tomaso.

Series:

Working Paper Series (National Bureau of Economic Research) no. w4718.

NBER working paper series no. w4718

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 1994.

Summary:

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.

Notes:

Print version record

April 1994.