The Application of Neural Networks to the Pricing of Credit Derivatives

Alessandro Ludovici ()
Additional contact information
Alessandro Ludovici: Università "G. d’Annunzio", Chieti-Pescara

Rivista di Politica Economica, 2006, vol. 96, issue 6, 187-221

Abstract: The present paper deals with a new approach to the pricing of credit derivatives, which are innovative financial instruments able to immunize a securities portfolio from the default risk of the issuers, using neural networks. After an essential analysis of the most important topics inherent to these nonlinear statistical instruments, particular emphasis, due to their diffusion, has been put on the characters of Credit Default Swaps and on the particularities of the structural and reduced form approaches proposed for their analysis. In the final part of the paper the effectiveness of neural networks in approximating the evaluation of credit derivatives and in improving the timing in the default prevision is illustrated.

JEL-codes: C45 G12 G32 G33 (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.rivistapoliticaeconomica.it/2006/nov_dic/pdf/Ludovici_eng.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rpo:ripoec:v:96:y:2006:i:6:p:187-221

Access Statistics for this article

Rivista di Politica Economica is currently edited by Gustavo Piga

More articles in Rivista di Politica Economica from SIPI Spa
Bibliographic data for series maintained by Sabrina Marino ().