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 ******************************************************************************* * * * 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 * * * * * ******************************************************************************* * * * http://weber.ucsd.edu/~chaefke * * * ******************************************************************************* =========================================================================