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Gottschling, Andreas
Haefke, Christian
White, Halbert
Year of Publication: 
Series/Report no.: 
Research notes in economics & statistics 99-9
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.
Option Pricing
Neural Networks
Nonparametric Density Estimation
Document Type: 
Working Paper

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