A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks.
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
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 1994
- Accession Number
- ADA279879
Entities
People
- Andrew Lo
- James M. Hutchinson
- Tomaso Poggio
Organizations
- Massachusetts Institute of Technology