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

Andreas Gottschling, Christian Haefke () and Halbert White
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Andreas Gottschling: Deutsche Bank

No 1999-24, 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 (1999)
Working Paper: Closed Form Integration of Artificial Neural Networks with Some Applications to Finance (2000)
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Persistent link: http://EconPapers.repec.org/RePEc:cdl:ucsdec:1999-24

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