Mean-Variance mixture model for calibrating loss given default (LGD) of a credit portfolio

Simple item page
dc.contributor.advisorAmbagaspitiya, Rohana
dc.contributor.authorSam, Charles
dc.contributor.committeememberAmbagaspitiya, Rohana
dc.contributor.committeememberScollnik, David
dc.contributor.committeememberde Leon, Alexander
dc.contributor.committeememberFapojuwo, Abraham
dc.contributor.committeememberBégin, Jean-François
dc.date2022-02
dc.date.accessioned2021-11-23T23:03:32Z
dc.date.available2021-11-23T23:03:32Z
dc.date.issued2021-11
dc.description.abstractWe study the sensitivity of Value-at-Risk (VaR) and Tail-Value-at-Risk (TVaR) of credit portfolio of defaultable obligors to the tail fatness of the loss given default latent variable distribution. We consider a static structural model where obligors default and loss given default (LGD) latent variables have a common systematic risk factor. We propose the use of the Normal-Variance mixture model to model the LGD latent variable to account for certain random external risks, such as the collapse of Lehman Brothers Holdings Inc in 2008 which resulted in instability in the financial sector. We derive an analytical expression for finding the asymptotic portfolio loss rate. We also propose two importance sampling algorithms for finding conditional tail probabilities for the portfolio loss. Our approach is unique in two aspects. First, we capture the dependence between default and LGD. Second, we make LGD values to be random and between zero and one. We also show that our importance sampling algorithms are asymptotically optimal.en_US
dc.identifier.citationSam, C. (2021). Mean-Variance mixture model for calibrating loss given default (LGD) of a credit portfolio (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/39399
dc.identifier.urihttp://hdl.handle.net/1880/114138
dc.language.isoengen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectCredit risken_US
dc.subjectImportance samplingen_US
dc.subjectNormal-Variance modelen_US
dc.subjectStructural single-factor modelen_US
dc.subject.classificationEducation--Businessen_US
dc.subject.classificationBankingen_US
dc.subject.classificationStatisticsen_US
dc.titleMean-Variance mixture model for calibrating loss given default (LGD) of a credit portfolioen_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineMathematics & Statisticsen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
ucalgary.item.requestcopytrueen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ucalgary_2021_sam_charles.pdf
Size:
1.43 MB
Format:
Adobe Portable Document Format
Description:
Charles Sam final thesis
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Open Theses and Dissertations