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NN-OPT: Neural Network for Option Pricing Using Multinomial Tree

  • Hung-Ching (Justin) Chen
  • Malik Magdon-Ismail
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

We provide a framework for learning to price complex options by learning risk-neutral measures (Martingale measures). In a simple geometric Brownian motion model, the price volatility, fixed interest rate and a no-arbitrage condition suffice to determine a unique risk-neutral measure. On the other hand, in our framework, we relax some of these assumptions to obtain a class of allowable risk-neutral measures. We then propose a framework for learning the appropriate risk-neural measure. In particular, we provide an efficient algorithm for backpropagating gradients through multinomial pricing trees. Since the risk-neutral measure prices all options simultaneously, we can use all the option contracts on a particular stock for learning. We demonstrate the performance of these models on historical data. Finally, we illustrate the power of such a framework by developing a real time trading system based upon these pricing methods.

Keywords

Option Price Trading Cost Martingale Measure Forward Propagation Term Interest Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hung-Ching (Justin) Chen
    • 1
  • Malik Magdon-Ismail
    • 1
  1. 1.Dept. of Computer ScienceRensselaer Polytechnic InstituteTroyUSA

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