dc.contributor.author |
Masdemont Soler, Josep |
dc.contributor.author |
Ortiz-Gracia, Luís |
dc.contributor.author |
Universitat Autònoma de Barcelona. Centre de Recerca Matemàtica |
dc.date |
2011 |
dc.identifier |
https://ddd.uab.cat/record/76074 |
dc.identifier |
urn:oai:ddd.uab.cat:76074 |
dc.format |
application/pdf |
dc.language |
eng |
dc.publisher |
Centre de Recerca Matemàtica |
dc.relation |
Centre de Recerca Matemàtica. Prepublicacions ; |
dc.rights |
open access |
dc.rights |
Aquest document està subjecte a una llicència d'ús de Creative Commons, amb la qual es permet copiar, distribuir i comunicar públicament l'obra sempre que se'n citin l'autor original, la universitat i el centre i no se'n faci cap ús comercial ni obra derivada, tal com queda estipulat en la llicència d'ús |
dc.rights |
https://creativecommons.org/licenses/by-nc-nd/2.5/ |
dc.subject |
Risc de crèdit |
dc.title |
Haar wavelets-based approach for quantifying credit portfolio losses |
dc.type |
Article |
dc.type |
Prepublicació |
dc.description.abstract |
This paper proposes a new methodology to compute Value at Risk (VaR) for quantifying losses in credit portfolios. We approximate the cumulative distribution of the loss function by a finite combination of Haar wavelet basis functions and calculate the coefficients of the approximation by inverting its Laplace transform. The Wavelet Approximation (WA) method is specially suitable for non-smooth distributions, often arising in small or concentrated portfolios, when the hypothesis of the Basel II formulas are violated. To test the methodology we consider the Vasicek one-factor portfolio credit loss model as our model framework. WA is an accurate, robust and fast method, allowing to estimate VaR much more quickly than with a Monte Carlo (MC) method at the same level of accuracy and reliability. |