Closed form integration of artificial neural networks with some applications

Arbeitspapier

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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• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Research Notes ; No. 99-9

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Neural Networks and Related Topics ; Contingent Pricing; Futures Pricing; option pricing ; Computational Techniques; Simulation Modeling
• Thema: Option Pricing ; Neural Networks ; Nonparametric Density Estimation ; Nichtparametrisches Verfahren ; Neuronale Netze ; Optionspreistheorie ; Risiko ; Theorie ; Maximum-Likelihood-Methode ; Value at Risk ; Varianzanalyse

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Gottschling, Andreas ; Haefke, Christian ; White, Halbert
• Ereignis: Veröffentlichung
• (wer): Deutsche Bank Research
• (wo): Frankfurt a. M.
• (wann): 1999

Weitere Informationen

• Handle: http://hdl.handle.net/10419/40272