A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks
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...
| Main Authors: | , , |
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| Language: | en_US |
| Published: |
2004
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| Online Access: | http://hdl.handle.net/1721.1/7287 |
| _version_ | 1826214636165267456 |
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| author | Hutchinson, James M. Lo, Andrew Poggio, Tomaso |
| author_facet | Hutchinson, James M. Lo, Andrew Poggio, Tomaso |
| author_sort | Hutchinson, James M. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T16:08:39Z |
| id | mit-1721.1/7287 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:08:39Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/72872019-04-15T00:40:29Z A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks Hutchinson, James M. Lo, Andrew Poggio, Tomaso 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. 2004-10-22T20:14:45Z 2004-10-22T20:14:45Z 1994-04-01 AIM-1471 http://hdl.handle.net/1721.1/7287 en_US AIM-1471 397765 bytes 1887637 bytes application/octet-stream application/pdf application/octet-stream application/pdf |
| spellingShingle | Hutchinson, James M. Lo, Andrew Poggio, Tomaso A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks |
| title | A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks |
| title_full | A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks |
| title_fullStr | A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks |
| title_full_unstemmed | A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks |
| title_short | A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks |
| title_sort | nonparametric approach to pricing and hedging derivative securities via learning networks |
| url | http://hdl.handle.net/1721.1/7287 |
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