Closed Form Integration of Artificial Neural Networks with Some Applications to Finance

Andreas Gottschling, Christian Haefke and Halbert White

University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego

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 (search for similar items in EconPapers)
Date: 1999-11-01
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Related works:
Working Paper: Closed Form Integration of Artificial Neural Networks with Some Applications to Finance (2000)
Working Paper: CLOSED FORM INTEGRATION OF ARTIFICIAL NEURAL NETWORKS WITH SOME APPLICATIONS TO FINANCE (2000)
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