Basket default swaps, CDOs and factor copulas

Gregory, Jon; Laurent, Jean Paul

doi:10.21314/jor.2005.115

Cited by 176 publications

(109 citation statements)

References 12 publications

Supporting

Mentioning

107

Contrasting

Unclassified

Order By: Relevance

“…Although we have stressed the application of this method in the widely used Gaussian copula, it extends easily to other models' dependence, including multifactor versions of the various factor copula models in Laurent and Gregory (2003). Also, eventhough we have focused on basket default swaps, similar ideas can be used for other portfolio credit derivatives; this is a topic of current investigation.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Fast Pricing of Basket Default Swaps

Chen¹,

Glasserman

2008

Operations Research

View full text Add to dashboard Cite

A basket default swap is a derivative security tied to an underlying basket of corporate bonds or other assets subject to credit risk. The value of the contract depends on the joint distribution of the default times of the underlying assets. Valuing a basket default swap often entails Monte Carlo simulation of these default times. For baskets of high-quality credits and for swaps that require multiple defaults to trigger payment, pricing the swap is a rare-event simulation problem. The JoshiKainth algorithm is an innovative importance-sampling technique for this problem that forces a predetermined number of defaults to occur on each path. This paper analyzes, extends, and improves the Joshi-Kainth algorithm. We show that, in its original form, the algorithm can actually increase variance; we present an alternative that is guaranteed to reduce variance, even when defaults are not rare. Along the way, we provide a rigorous underpinning in a setting sufficiently general to include both the original method and the version proposed here.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Simple cases of this and related models can be evaluated using transform inversion and numerical integration techniques, as in Laurent and Gregory (2003). However, more general cases typically require Monte Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

Fast Pricing of Basket Default Swaps

Chen¹,

Glasserman

2008

Operations Research

View full text Add to dashboard Cite

show abstract

“…17 Hence, the sharp rise of prices cannot be attributed to the increase of correlations. We should rather think of it as a result of the change in risk attitude of investors.…”

Section: A Numerical Examplementioning

confidence: 99%

“…5 After an appropriate change of measures based on the multivariate 1 See, e.g., Hull and White (2004) and Laurent and Gregory (2005) for details of the one-factor Gaussian copula model.…”

Section: Introductionmentioning

confidence: 99%

Pricing of CDOs based on the multivariate Wang transform

Kijima

Motomiya²,

Suzuki³

2010

Journal of Economic Dynamics and Control

View full text Add to dashboard Cite

Abstract. This paper shows that the one-factor Gaussian copula model, the standard market model for valuing CDO's, can be derived from the multivariate Wang transform, which is consistent with Bühlmann's equilibrium pricing model, whence it has a sound economic interpretation. The Gaussian copula model is then extended within the Bühlmann's framework. Unlike the existing models, our model calibrates the parameter associated with risk aversion index of the representative investor, not the correlation parameter. A t-copula model is also considered to describe the fat-tail distribution observed in the actual markets.

show abstract

“…The marginal distribution of each default time (under a pricing measure) is typically inferred from the market prices of assets linked to an individual obligor, such as a bond or a credit default swap (see, e.g., Duffie and Singleton [7]); the joint distribution may then be specified through a copula function, as in Li [16]. Simple cases of this approach lead to pricing through transform inversion and numerical integration techniques, or other numerical methods, as in Andersen et al [1], Hull and White [13], and Laurent and Gregory [15]. But more general cases require Monte Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity estimates for portfolio credit derivatives using Monte Carlo

Chen¹,

Glasserman

2008

Finance Stoch

View full text Add to dashboard Cite

Portfolio credit derivatives are contracts that are tied to an underlying portfolio of defaultable reference assets and have payoffs that depend on the default times of these assets. The hedging of credit derivatives involves the calculation of the sensitivity of the contract value with respect to changes in the credit spreads of the underlying assets, or, more generally, with respect to parameters of the default-time distributions. We derive and analyze Monte Carlo estimators of these sensitivities. The payoff of a credit derivative is often discontinuous in the underlying default times, and this complicates the accurate estimation of sensitivities. Discontinuities introduced by changes in one default time can be smoothed by taking conditional expectations given all other default times. We use this to derive estimators and to give conditions under which they are unbiased. We also give conditions under which an alternative likelihood ratio method estimator is unbiased. We illustrate the application and verification of these conditions and estimators in the particular case of the multifactor Gaussian copula model, but the methods are more generally applicable.

show abstract

Basket default swaps, CDOs and factor copulas

Cited by 176 publications

References 12 publications

Fast Pricing of Basket Default Swaps

Fast Pricing of Basket Default Swaps

Pricing of CDOs based on the multivariate Wang transform

Sensitivity estimates for portfolio credit derivatives using Monte Carlo

Contact Info

Product

Resources

About