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The Application of Neural Networks to the Pricing of Credit Derivatives

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  • Alessandro Ludovici

    (Università "G. d’Annunzio", Chieti-Pescara)

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.

Suggested Citation

  • Alessandro Ludovici, 2006. "The Application of Neural Networks to the Pricing of Credit Derivatives," Rivista di Politica Economica, SIPI Spa, vol. 96(6), pages 187-221, November-.
  • Handle: RePEc:rpo:ripoec:v:96:y:2006:i:6:p:187-221
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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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