New Paper on Neural Networks, Filtering, and Derivatives Pricing

[Dirk Ormoneit]

Dear Colleagues,

Please take notice of the following working paper, available online at

http://www-stat.stanford.edu/~ormoneit/clearn.ps  .

Best regards,

Dirk

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A REGULARIZATION APPROACH TO CONTINUOUS LEARNING WITH AN APPLICATION TO
                    FINANCIAL DERIVATIVES PRICING

                           by Dirk Ormoneit

    We consider the training of neural networks in cases where the
    nonlinear relationship of interest gradually changes over
    time. One possibility to deal with this problem is by
    regularization where a variation penalty is added to the usual
    mean squared error criterion. To learn the regularized network
    weights we suggest the Iterative Extended Kalman Filter (IEKF) as
    a learning rule, which may be derived from a Bayesian perspective
    on the regularization problem.  A primary application of our
    algorithm is in financial derivatives pricing, where neural
    networks may be used to model the dependency of the derivatives'
    price on one or several underlying assets.  After giving a brief
    introduction to the problem of derivatives pricing we present
    experiments with German stock index options data showing that a
    regularized neural network trained with the IEKF outperforms
    several benchmark models and alternative learning procedures.  In
    particular, the performance may be greatly improved using a newly
    designed neural network architecture that accounts for
    no-arbitrage pricing restrictions.


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Dirk Ormoneit
Department of Statistics, Room 206
Stanford University
Stanford, CA 94305-4065

ph.: (650) 725-6148
fax: (650) 725-8977

ormoneit at stat.stanford.edu
http://www-stat.stanford.edu/~ormoneit/