Normal Inverse Gaussian Factor Copula Model

Anna Schlösser ()
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Anna Schlösser: Hedging and Derivatives Strategies

Chapter Chapter 5 in Pricing and Risk Management of Synthetic CDOs, 2011, pp 129-163 from Springer

Abstract: Abstract We have seen in the previous section, that a heavy tailed distribution of factors in the one factor copula model may help solving the correlation smile problem of the Gaussian copula model. Thus, finding a different heavy tailed distribution that is similar to the Student-t but stable under convolution would help to decrease the computation time tremendously. As computation time is an important issue for a large range of applications such as the determination of an optimal portfolio asset allocation (including CDO tranches), where CDO tranches have to be repriced in each scenario path at each time step in the future, the usage of such a distribution is crucial.

Keywords: Tail Dependence; Copula Model; Default Intensity; Tail Dependence Coefficient; Normal Inverse Gaussian (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-642-15609-0_5

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DOI: 10.1007/978-3-642-15609-0_5

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