Calibrating the CreditRisk<sup>+</sup> Model at Different Time Scales and in Presence of Temporal Autocorrelation <xref rid="fn1-mathematics-1268727" ref-type="fn">†</xref>

The CreditRisk<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula> model is one of the industry...

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Bibliographic Details
Main Authors: Jacopo Giacomelli, Luca Passalacqua
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/9/14/1679
Description
Summary:The CreditRisk<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula> model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mo>+</mo></msup></semantics></math></inline-formula> requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.
ISSN:2227-7390