Credit migration and basket derivatives pricing with copulas

Berrada, Tony; Dupuis, Debbie J.; Jacquier, Éric; Papageorgiou, Nicolas; Rémillard, Bruno

doi:10.21314/jcf.2006.145

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…In one line of thinking, to relax the assumption of Gaussian distribution in the one factor Gaussian copula model, student-t copula [5][6][7][8][9][10][11][12], double-t copula [13,14], Clayton copula [12,[15][16][17][18][19][20], Archimedian copula [21,22], Marshall Olkin copula [23][24][25][26] are studied. And default correlations are made stochastic and correlated with the systematic factor in [27,28] to relax the assumption that default correlations are constant through time and independent of the firms default probabilities.…”

Section: Mathematical Challenges In Modeling the Mechanism Of Cdosmentioning

confidence: 99%

On the Mechanism of CDOs behind the Current Financial Crisis and Mathematical Modeling with Levy Distributions

Hong-wen¹,

Yang³

2010

IIM

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This paper aims to reveal the mechanism of Collateralized Debt Obligations (CDOs) and how CDOs extend the current global financial crisis. We first introduce the concept of CDOs and give a brief account of the de-velopment of CDOs. We then explicate the mechanism of CDOs within a concrete example with mortgage deals and we outline the evolution of the current financial crisis. Based on our overview of pricing CDOs in various existing random models, we propose an idea of modeling the random phenomenon with the feature of heavy tail dependence for possible implements towards a new random modeling for CDOs

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Section: Mathematical Challenges In Modeling the Mechanism Of Cdosmentioning

confidence: 99%

On the Mechanism of CDOs behind the Current Financial Crisis and Mathematical Modeling with Levy Distributions

Hong-wen¹,

Yang³

2010

IIM

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“…Until lately very little quantitative assistance was available and only ad hoc guidelines existed. As shown in Berrada et al (2006), different copulas can result in dramatically different dependence structures. Quite recently, some statistical procedures were proposed to address the problem of goodnessof-fit to copula families, e.g.…”

Section: Journal Of Futures Markets Doi: 101002/futmentioning

confidence: 99%

“…Journal of Futures Markets DOI: 10.1002/fut Berrada et al (2006), for example, provide empirical evidence that the choice of the copula greatly affects the pricing of joint default risk and show the impact on the pricing of nth to default credit default swaps. To do this, they model the joint dynamics of credit ratings of several firms.…”

Section: Journal Of Futures Marketsmentioning

confidence: 99%

“…This is especially important given the recent evidence that the choice of copula can greatly affect pricing, see e.g. Berrada, Dupuis, Jacquier, Papageorgiou, and Rémillard (2006). 1 One can also make the default probabilities dependent on background variables as in Duffie and Garleanu (2001) or use the infectious correlation approach proposed by Jarrow and Yu (2001).…”

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confidence: 99%

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Empirical evidence on the dependence of credit default swaps and equity prices

Dupuis

Jacquier

Papageorgiou

et al. 2009

Journal of Futures Markets

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We investigate the common practice of estimating the dependence structure between credit default swap prices on multi-name credit instruments from the dependence structure of the equity returns of the underlying firms. We find convincing evidence that the practice is inappropriate for high-yield instruments and that it may even be flawed for instruments containing only firms within a sector. To do this, we model individual credit ratings by univariate continuous time Markov chains, and their joint dynamics by copulas. The use of copulas allows us to incorporate our knowledge of the modeling of univariate processes, into a multivariate framework. However, our test and results are robust to the choice of copula.

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“…In that context, the copula of default times of different companies can be estimated using joint prices for financial instruments (e.g., credit default swaps), but not the margins. To estimate the latter, different data need to be used, e.g., credit ratings as in Berrada et al (2006).…”

Section: Can the Estimation Of The Dependence Structure Only Be A Matmentioning

confidence: 99%