Publicação

Term structure of credit spreads with affine processes

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Detalhes bibliográficos
Resumo:A brief review and a comparison between Structural and Intensity mode is presented in the first section with an argument in favor of Intensity models based on the quality of information available to market participants. Next, an Intensity model with an affine (constant plus linear) parametrization of the intensity parameter driven a by a set of latent variables is formulated for the Euro Credit Default Swap (CDS) curve. Latent variables in the intensity parameter are assumed to follow uncorrelated CIR processes. Furthermore the model parameters, the latent variable processes and implicit risk-neutral default probabilities are estimated with an application of a Linearized Kalman Filter approach and a Likelihood maximization algorithm. A conclusion is reached that a model with two latent variables is able to account for 95%, 89%, 98% and 99% of the variations in 3, 5, 7 and 10-year maturities of the CDS curve.
Autores principais:Shibaev, Dmitry
Assunto:credit risk affine intensity models CIR term-structure kalman filter risco de crédito affine modelos de forma intensiva CIR estrutura temporal filtro kalman
Ano:2009
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade de Lisboa
Idioma:inglês
Origem:Repositório da Universidade de Lisboa
Descrição
Resumo:A brief review and a comparison between Structural and Intensity mode is presented in the first section with an argument in favor of Intensity models based on the quality of information available to market participants. Next, an Intensity model with an affine (constant plus linear) parametrization of the intensity parameter driven a by a set of latent variables is formulated for the Euro Credit Default Swap (CDS) curve. Latent variables in the intensity parameter are assumed to follow uncorrelated CIR processes. Furthermore the model parameters, the latent variable processes and implicit risk-neutral default probabilities are estimated with an application of a Linearized Kalman Filter approach and a Likelihood maximization algorithm. A conclusion is reached that a model with two latent variables is able to account for 95%, 89%, 98% and 99% of the variations in 3, 5, 7 and 10-year maturities of the CDS curve.