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

Listed author(s):
  • Gottschling, Andreas
  • Haefke, Christian
  • White, Halbert

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

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Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number qt0wz7n7nm.

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Date of creation: 01 Nov 1999
Handle: RePEc:cdl:ucsdec:qt0wz7n7nm
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