Commercial banks have devoted many resources to develop models aiming to better quantify their financial risk and to assign economic capital. These efforts have been recognized and encouraged by bank regulators, and have been extended into the field of credit risk modeling. To differentiating credit quality, a heterogeneous population of identical firms is divided into a finite number of classes. In order to have a dynamical model, the partition can change during the time. A suitable exchangeability assumption is made to preserve the property of having identical items with different credit levels. In a partially observed setting, given the cardinality of the class of the firms already defaulted, the aim is to recover information about the time to default of the remaining firms. This topic is discussed by using stochastic filtering techniques.

Modeling and Filtering of Credit Health for a Set of Firms

TARDELLI, PAOLA
2015-01-01

Abstract

Commercial banks have devoted many resources to develop models aiming to better quantify their financial risk and to assign economic capital. These efforts have been recognized and encouraged by bank regulators, and have been extended into the field of credit risk modeling. To differentiating credit quality, a heterogeneous population of identical firms is divided into a finite number of classes. In order to have a dynamical model, the partition can change during the time. A suitable exchangeability assumption is made to preserve the property of having identical items with different credit levels. In a partially observed setting, given the cardinality of the class of the firms already defaulted, the aim is to recover information about the time to default of the remaining firms. This topic is discussed by using stochastic filtering techniques.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/108508
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