Dear colleagues,
You can find the paper
"Closed Form Integration of Artificial Neural Networks With Some
Applications to Finance"
by Andy Gottschling, Christian Haefke and Halbert White
at my webpage:
http://weber.ucsd.edu/~chaefke/papers/Annint_chaefke.ps.gz
or
http://weber.ucsd.edu/~chaefke/papers/Annint_chaefke.pdf
Abstract:
Many economic and econometric applications require the integration of functions
lacking a closed form antiderivative, which is therefore a task that can only
be solved by numerical methods. We propose a new family of probability
densities that can be used as substitutes and have the property of closed
form integrability.
This is especially advantageous in cases where either the complexity of a
problem makes numerical function evaluations very costly, or fast information
extraction is required for time-varying environments.
Our approach allows generally for nonparametric maximum likelihood density
estimation and may thus find a variety of applications, two of which are
illustrated briefly:
Estimation of Value at Risk based on approximations to the
density of stock returns.
Recovering risk neutral densities for the valuation of options from
the option price -- strike price relation.
Keywords: Option Pricing, Neural Networks, Nonparametric Density
Estimation, Hypergeometric Functions;
The paper prints out to 35 pages,
Filesize is 150K for the gzipped version and 350K for the pdf file.
Comments are highly appreciated.
Best regards,
Christian
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* Christian Haefke INTERNET: chaefke(a)weber.ucsd.edu *
* University of California, San Diego *
* Department of Economics, 0508 *
* 9500 Gilman Drive *
* La Jolla, CA 92093-0508 *
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http://weber.ucsd.edu/~chaefke *
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