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

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Author Info
James M. Hutchinson
Andrew W. Lo
Tomaso Poggio

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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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 4718.

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Date of creation: Feb 1995
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Handle: RePEc:nbr:nberwo:4718

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G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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  1. Patrick Gagliardini & C. Gourieroux & E. Renault, 2005. "Efficient Derivative Pricing by Extended Method of Moments," University of St. Gallen Department of Economics working paper series 2005 2005-05, Department of Economics, University of St. Gallen. [Downloadable!]
  2. Olivier Bardou & Yoshua Bengio, 2002. "Régularisation du prix des options : Stacking," CIRANO Working Papers 2002s-44, CIRANO. [Downloadable!]
  3. Khurshid M. KIANI & Terry L. KASTENS, 2006. "Using Macro-Financial Variables To Forecast Recessions. An Analysis Of Canada, 1957-2002," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 6(3). [Downloadable!] (restricted)
  4. Kiani, K.M., 2009. "Neural Networks to Detect Nonlinearities in Time Series: Analysis of Business Cycle in France and the United Kingdom," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 9(1). [Downloadable!] (restricted)
  5. Darsinos, T. & Satchell, S.E., 2001. "Bayesian Analysis of the Black-Scholes Option Price," Cambridge Working Papers in Economics 0102, Faculty of Economics, University of Cambridge. [Downloadable!]
  6. Tkacz, Greg & Hu, Sarah, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Working Papers 99-3, Bank of Canada. [Downloadable!]
  7. Peter Christoffersen & Kris Jacobs, 2003. "The Importance of the Loss Function in Option Valuation," CIRANO Working Papers 2003s-52, CIRANO. [Downloadable!]
    Other versions:
  8. T. Van Gestel & B. Baesens & J. A.K. Suykens & D. Van Den Poel & D.-E. Baestaens & Bm. Willekens, 2004. "Bayesian Kernel-Based Classification for Financial Distress Detection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/247, Ghent University, Faculty of Economics and Business Administration. [Downloadable!]
    Other versions:
  9. René Garcia & Eric Ghysels & Éric Renault, 2004. "The Econometrics of Option Pricing," CIRANO Working Papers 2004s-04, CIRANO. [Downloadable!]
  10. Bildirici, Melike & Alp, Aykaç, 2008. "The Relationship Between Wages and Productivity: TAR Unit Root and TAR Cointegration Approach," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 5(1), pages 93-110. [Downloadable!]
  11. Panayiotis Andreou & Chris Charalambous & Spiros Martzoukos, 2006. "Robust Artificial Neural Networks for Pricing of European Options," Computational Economics, Springer, vol. 27(2), pages 329-351, May. [Downloadable!] (restricted)
  12. Bams, Dennis & Lehnert, Thorsten & Wolff, Christian C, 2005. "Loss Functions in Option Valuation: A Framework for Model Selection," CEPR Discussion Papers 4960, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  13. Jasmina Hasanhodzic & Andrew W. Lo & Emanuele Viola, 2009. "A Computational View of Market Efficiency," Quantitative Finance Papers 0908.4580, arXiv.org. [Downloadable!]
  14. Ming Yuan, 2009. "State price density estimation via nonparametric mixtures," Quantitative Finance Papers 0910.1430, arXiv.org. [Downloadable!]
  15. Khurshid Kiani & Terry Kastens, 2008. "Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures," Computational Economics, Springer, vol. 32(4), pages 383-406, November. [Downloadable!] (restricted)
  16. N. K. Chidambaran & Chi-Wen Jevons Lee & Joaguin R. Trigueros, 1998. "An Adaptive Evolutionary Approach to Option Pricing via Genetic Programming," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-086, New York University, Leonard N. Stern School of Business-. [Downloadable!]
  17. Bossaerts, P. & Hillion, P., 1995. "Local Parametric Analysis of Hedging in Discrete Time," Discussion Paper 23, Tilburg University, Center for Economic Research. [Downloadable!]
    Other versions:
  18. Philippe Paquet, 1997. "L'utilisation des réseaux de neurones artificiels en finance," Working Papers 1997-1, Laboratoire Orléanais de Gestion - université d'Orléans. [Downloadable!]
  19. Dimitris Bertsimas & Leonid Kogan & Andrew W. Lo, 1997. "Pricing and Hedging Derivative Securities in Incomplete Markets: An E-Aritrage Model," NBER Working Papers 6250, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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