We propose a two-stage model for dealing with the temporal degradation of credit scoring models. First, we develop a model from a classical framework, with a static supervised learning setting and binary output. Then, we introduce the time-changing economic factors, using a regression between the macroeconomic data and the internal default in the portfolio. In so doing, the specific risk is cap...

This paper is the report of a Master’s Degree project carried out at Royal Institute of Technology and in this paper we mainly apply the estimators and methods derived by P. Glasserman and J. Li (2003, 2005) of importance sampling methods in portfolio credit risk models. By using the exponential twisting method we will be able to compute the probability beyond one certain loss level (P(L>X)). W...

This paper examines a new approach for credit risk optimization. The model is based on the Conditional Value-at-Risk (CVaR) risk measure, the expected loss exceeding Value-at-Risk. CVaR is also known as Mean Excess, Mean Shortfall, or Tail VaR. This model can simultaneously adjust all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return const...

This paper employs the concepts and methods of social network theory and cluster analysis to improve the measurement of alliance portfolio similarity. Analysts have used this measure which compares nations' lists of military allies to ascertain the degree to which nations have common security interests. While this would seem to be a straightforward exercise, we have undermined our efforts in tw...

At a time of growing market turbulence and a strong shifts towards increasingly complex risk models and more stringent audit requirements, it is more critical than ever to maintain the highest quality of financial and credit information. IFC implemented an approach that helps increase data integrity and quality significantly. This approach is called “Screening”. Screening is based on linking in...

In the first part of this paper we address the non-coherence of value-at-risk (VaR) as a risk measure in the context of portfolio credit risk, and highlight some problems which follow from this theoretical deficiency. In particular, a realistic demonstration of the non-subadditivity of VaR is given and the possibly nonsensical consequences of VaR-based portfolio optimisation are shown. The seco...

In order to dynamize the static Gaussian copula model of portfolio credit risk, we introduce a model filtration made of a reference Brownian filtration progressively enlarged by the default times. This yields a multidimensional density model of default times, where, as opposed to the classical situation of the Cox model, the reference filtration is not immersed into the enlarged filtration. In ...

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 parame...

Ambivalence in the regulatory definition of capital adequacy for credit risk has recently steered the financial services industry to collateral loan obligations (CLOs) as an important balance sheet management tool. CLOs represent a specialised form of Asset-Backed Securitisation (ABS), with investors acquiring a structured claim on the interest proceeds generated from a portfolio of bank loans ...

In this paper, we assess the magnitude of model uncertainty of credit risk portfolio models, i.e., what is the maximum and minimum Value-at-Risk (VaR) that can be justified given a certain amount of available information. Puccetti and Rüschendorf (2012b) and Embrechts et al. (2013) propose the rearrangement algorithm (RA) as a general method to approximate VaR bounds when the default probabilit...