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Probability of default calibration for low default portfolios: revisiting the Bayesian approach

Thesis (MCom)--Stellenbosch University, 2016

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Main Author: Venter, Edward Stevens
Other Authors: Conradie, W. J.
Format: Thesis
Language:en_ZA
Published: Stellenbosch : Stellenbosch University 2016
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access_status_str Open Access
author Venter, Edward Stevens
author2 Conradie, W. J.
author_browse Conradie, W. J.
Venter, Edward Stevens
author_facet Conradie, W. J.
Venter, Edward Stevens
author_sort Venter, Edward Stevens
collection Thesis
dc_rights_str_mv Stellenbosch University
description Thesis (MCom)--Stellenbosch University, 2016
format Thesis
id oai:scholar.sun.ac.za:10019.1/98723
institution Stellenbosch University (South Africa)
language en_ZA
last_indexed 2026-06-10T12:47:10.728Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository
publishDate 2016
publishDateRange 2016
publishDateSort 2016
publisher Stellenbosch : Stellenbosch University
publisherStr Stellenbosch : Stellenbosch University
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source_str SUNScholar — Stellenbosch University Repository
spelling oai:scholar.sun.ac.za:10019.1/98723 Probability of default calibration for low default portfolios: revisiting the Bayesian approach Venter, Edward Stevens Conradie, W. J. Stellenbosch University. Economic and Management Sciences. Dept. of Statistics and Actuarial Science Probability of default (PD) Low default portfolios (LDP) Credit risk Financial risk management Bayesian statistics UCTD Thesis (MCom)--Stellenbosch University, 2016 ENGLISH ABSTRACT : The Probability of Default is one of the fundamental parameters used in the quantification of credit risk. When estimating the Probability of Default for portfolios with a low default nature the Probability of Default will always be underestimated. Therefore, a need exists for calibrating the Probability of Default for Low Default Portfolios. Various approaches have been considered in the literature review, with the main approaches being the Confidence Based Approach and Bayesian Approach. In this study the Bayesian Approach for calibrating the Probability of Default for portfolios of high grade credit is reconsidered. Two alternative prior distributions that can be used in the Bayesian Approach are proposed; these are an informative, Strict Pareto distribution and a non-informative Jeffreys prior. The performance of these proposals are then compared to existing calibration techniques by using real data. AFRIKAANSE OPSOMMING : Die Waarskynlikheid van Wanbetaling is een van die fundamentele parameters in die beraming van kredietrisiko. Wanneer die Waarskynliheid van Wanbetaling beraam word vir ’n portefeulje met lae wanbetaling observasies in die historiese data, vind onderberaming altyd plaas. Dus bestaan daar ’n nood vir kalibrasie tegnieke vir die Waarskynlikhied van Wanbetling vir Lae Wanbetaling Portefeuljes. ’n Verskeidenheid van benaderings word in die literatuur voorgestel, waaronder die Vertroue Gebasseerde Benadering en die Bayesiaanse Benadering die bekendste is. In hierdie studie word die Bayesiaanse Benadering vir die kalibrasie van die Waarskynlikheid van Wanbetaling vir portefeuljes van hoë vlak krediet heroorweeg. Twee alternatiewe apriori verdelings word voorgestel om in die Bayesiaanse Benadering te gebruik. Hierdie apriori verdelings is die streng Pareto verdeling wat ’n inligting-gewende apriori verdeling is en die Jeffreys apriori verdeling wat ’n nie-inligting-gewende apriori verdeling is. Die prestasie van die tegnieke wat voortvloei uit die gebruik van die voorgenoemde twee apriori verdelings word dan vergelyk met bestaande kalibrasie tegnieke deur gebruik te maak van werklike data. Masters 2016-03-09T14:53:48Z 2016-03-09T14:53:48Z 2016-03 Thesis http://hdl.handle.net/10019.1/98723 en_ZA Stellenbosch University xiv, 153 pages : illustrations (chiefly colour) application/pdf Stellenbosch : Stellenbosch University
spellingShingle Probability of default (PD)
Low default portfolios (LDP)
Credit risk
Financial risk management
Bayesian statistics
UCTD
Venter, Edward Stevens
Probability of default calibration for low default portfolios: revisiting the Bayesian approach
title Probability of default calibration for low default portfolios: revisiting the Bayesian approach
title_full Probability of default calibration for low default portfolios: revisiting the Bayesian approach
title_fullStr Probability of default calibration for low default portfolios: revisiting the Bayesian approach
title_full_unstemmed Probability of default calibration for low default portfolios: revisiting the Bayesian approach
title_short Probability of default calibration for low default portfolios: revisiting the Bayesian approach
title_sort probability of default calibration for low default portfolios revisiting the bayesian approach
topic Probability of default (PD)
Low default portfolios (LDP)
Credit risk
Financial risk management
Bayesian statistics
UCTD
url http://hdl.handle.net/10019.1/98723
work_keys_str_mv AT venteredwardstevens probabilityofdefaultcalibrationforlowdefaultportfoliosrevisitingthebayesianapproach