Ambagaspitiya, RohanaSam, Charles2021-11-232021-11-232021-11Sam, 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.http://hdl.handle.net/1880/114138We 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.engUniversity 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.Credit riskImportance samplingNormal-Variance modelStructural single-factor modelEducation--BusinessBankingStatisticsdoctoral thesis10.11575/PRISM/39399