Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/40272 
Year of Publication: 
1999
Series/Report no.: 
Research Notes No. 99-9
Publisher: 
Deutsche Bank Research, Frankfurt a. M.
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.
Subjects: 
Option Pricing
Neural Networks
Nonparametric Density Estimation
JEL: 
C45
G13
C63
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.