Credit Risk Modeling with Gaussian Random Fields
نویسنده
چکیده
The literature on credit risk consists of different approaches in modeling the behavior of defaultable bonds. The structural approach is based on the evolution of the firm value to determine default and recovery. In contrast, the more recently developed intensity-based models specify the default time exogenously. In this approach the defaultable yield curve results from the risk-free yield curve plus the premium for default risk. It is therefore natural to start with an interest rate model and augment it with default risk.
منابع مشابه
Mathematical Modeling for an Integrated Inventory System with Two-level Trade Credit and Random Defectiveness in Transport
Modern business environment focuses on improving the operational efficiency of supplier, retailer and customers through integrating their inventory. Although a smoothly running integrated inventory system is ideal, the reality is to deal with imperfectness in transportation. In actual production environments, inventory items are not perfect and defectiveness occurs in random process. In this p...
متن کاملExtension of Spot Recovery Model for Gaussian Copula
Heightened systematic risk in the credit crisis has created challenges to CDO pricing and risk management. One important focus has been on the modeling of stochastic recovery. Different approaches within the Gaussian Copula framework have been proposed, but a consistent model was lacking until the recent paper of Bennani and Maetz [6] which shifted the modeling from period recovery to spot reco...
متن کاملA NEW METHOD FOR MULTI−RESOLUTION TEXTURE SEGMENTATION USING GAUSSIAN MARKOV RANDOM FIELDS (MonPmOR2)
A new approach to multi−resolution modeling of images is introduced and applied to the task of semi−unsupervised texture segmentation using Gaussian Markov random fields (GMRFs). It is shown that traditional GMRF modeling of multi−resolution coefficients is incapable of accounting for the non−Gaussian statistics which often characterize the multi−resolution coefficients. On the other hand, the ...
متن کاملPredicting the Credit Risk of Loans Using Data Mining Tools
One of the most common causes or credit phenomenon that is taken into account for credit risk is the customer’s noncompliance with the commitments. Thus, by predicting the behavior of loan applicants, the growth rate of debts can be decreased. Hence, this study is conducted on corporate applicants for loans in one of the public banks in Iran. In this paper, the main elements comprising the cus...
متن کاملTransformed Gaussian Markov Random Fields and 1 Spatial Modeling
15 The Gaussian random field (GRF) and the Gaussian Markov random field (GMRF) have 16 been widely used to accommodate spatial dependence under the generalized linear mixed 17 model framework. These models have limitations rooted in the symmetry and thin tail of the 18 Gaussian distribution. We introduce a new class of random fields, termed transformed GRF 19 (TGRF), and a new class of Markov r...
متن کامل