Risk factor contributions in portfolio credit risk models

Rosén, Dan; Saunders, David

doi:10.1016/j.jbankfin.2009.08.002

Cited by 65 publications

(43 citation statements)

References 33 publications

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“…Following Rosen and Saunders (2010) or Dorfleitner et al (2012), we identify each obligor with a so called creditworthiness index, which is an obligor specific random variable. In general, the creditworthiness index is based on the Merton model, which was originally formulated for asset values.…”

Section: Prerequisitesmentioning

confidence: 99%

Justification of Per-Unit Risk Capital Allocation in Portfolio Credit Risk Models

Dorfleitner

Pfister

2014

Int. J. Theor. Appl. Finan.

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Risk capital allocation is based on the assumption that the risk of a homogeneous portfolio is scaled up and down with the portfolio size. In this article we show that this assumption is true for large portfolios, but has to be revised for small ones. On basis of numerical examples we calculate the minimum portfolio size that is necessary to limit the error of gradient risk capital allocation and the resulting error in a portfolio optimization algorithm or pricing strategy. We show the dependence of this minimum portfolio size on different parameters like the probability of default and on the credit risk model that is used.

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Section: Prerequisitesmentioning

confidence: 99%

Justification of Per-Unit Risk Capital Allocation in Portfolio Credit Risk Models

Dorfleitner

Pfister

2014

Int. J. Theor. Appl. Finan.

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“…A brief overview is provided for the relevant theory of risk contributions, with a focus on Euler allocation principles, for which a more detailed introduction is provided in [33], Section 6.3, [45] and [41], Section 3.…”

Section: Risk Contributions and Capital Allocationsmentioning

confidence: 99%

Sequential Monte Carlo Samplers for Capital Allocation Under Copula-Dependent Risk Models

2014

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In this paper we assume a multivariate risk model has been developed for a portfolio and its capital derived as a homogeneous risk measure. The Euler (or gradient) principle, then, states that the capital to be allocated to each component of the portfolio has to be calculated as an expectation conditional to a rare event, which can be challenging to evaluate in practice. We exploit the copuladependence within the portfolio risks to design a Sequential Monte Carlo Samplers based estimate to the marginal conditional expectations involved in the problem, showing its efficiency through a series of computational examples.

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“…As indicated in Section 2.2 and similarly to [39,61,78,79], the vector f t of factor returns at time t is normally distributed with mean 0 and VC matrix Q, and the vectors of factor returns at different periods are independent of each other. There is no independence assumption between the returns of factors j and j at the same period t (f t j and f t j are not independent).…”

Section: Second-order Cone Reformulation Of Stochastic Constraintmentioning

confidence: 99%

Construction of Risk-Averse Enhanced Index Funds

Lejeune

Samatlı-Paç

2013

INFORMS Journal on Computing

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We propose a partial replication strategy to construct risk-averse enhanced index funds. Our model takes into account the parameter estimation risk by defining the asset returns and the return covariance terms as random variables. The variance of the index fund return is forced to be below a low-risk threshold with a large probability, thereby limiting the market risk exposure of the investors and the moral hazard associated with the wage structure of fund managers. The resulting stochastic integer problem is reformulated through the derivation of a deterministic equivalent for the risk constraint and the use of a block decomposition technique. We develop an exact outer approximation method based on the relaxation of some binary restrictions and the reformulation of the cardinality constraint. The method provides a hierarchical organization of the computations with expanding sets of integer-restricted variables and outperforms the Bonmin and the Cplex 12.1 solvers. The method can solve very large (up to 1000 securities) instances, converges fast, scales well, and is general enough to be applicable to problems with buy-in threshold constraints. Cross-validation tests show that the constructed funds track closely and are consistently less risky than the benchmark on the out-of-sample period.

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Risk factor contributions in portfolio credit risk models

Cited by 65 publications

References 33 publications

Justification of Per-Unit Risk Capital Allocation in Portfolio Credit Risk Models

Justification of Per-Unit Risk Capital Allocation in Portfolio Credit Risk Models

Sequential Monte Carlo Samplers for Capital Allocation Under Copula-Dependent Risk Models

Construction of Risk-Averse Enhanced Index Funds

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