Estimating expected lifetime of revolving credit facilities in an IFRS 9 framework
(2016) FMS820 20161Mathematical Statistics
 Abstract (Swedish)
 This paper sets out to estimate expected lifetime of revolving credit facilities
(e.g. credit card products) and is motivated by the introduction of the International
Financial Reporting Standard 9 (IFRS 9) and its requirements for loan
impairments. The reporting entity is required to estimate lifetime expected
credit losses for certain nancial instruments. In practice, maximum contractual
period for revolving credit facilities cannot be used in dening lifetime for
the facility and credit risk mitigation actions need to be considered. A data
set for a retail credit card portfolio was provided by a Nordic bank and for
the lifetime denition derived, a model based on a conditional Markov chain
was selected. Expected lifetime was... (More)  This paper sets out to estimate expected lifetime of revolving credit facilities
(e.g. credit card products) and is motivated by the introduction of the International
Financial Reporting Standard 9 (IFRS 9) and its requirements for loan
impairments. The reporting entity is required to estimate lifetime expected
credit losses for certain nancial instruments. In practice, maximum contractual
period for revolving credit facilities cannot be used in dening lifetime for
the facility and credit risk mitigation actions need to be considered. A data
set for a retail credit card portfolio was provided by a Nordic bank and for
the lifetime denition derived, a model based on a conditional Markov chain
was selected. Expected lifetime was estimated and an analytical expression for
expected lifetime of revolving credit facilities was derived and validated. (Less)  Popular Abstract
 What period should credit losses be estimated over in IFRS 9?
When the standard on how to account for credit losses moves to an expected loss approach, there is a need to find out how far into the future to look for losses. This is known as the expected lifetime, and I have dived into interpreting how the IFRS 9 standard can be implemented in a model for expected lifetime for credit cards and similar instruments. I propose and validate a model, along with a methodology for estimation.
The Great Financial Crisis had a grand effect on most economies. Banks were assessed and it was recognized that in order to reduce effects of future downturns, provisions that were more forwardlooking and higher were needed for credit losses. IFRS 9 is the... (More)  What period should credit losses be estimated over in IFRS 9?
When the standard on how to account for credit losses moves to an expected loss approach, there is a need to find out how far into the future to look for losses. This is known as the expected lifetime, and I have dived into interpreting how the IFRS 9 standard can be implemented in a model for expected lifetime for credit cards and similar instruments. I propose and validate a model, along with a methodology for estimation.
The Great Financial Crisis had a grand effect on most economies. Banks were assessed and it was recognized that in order to reduce effects of future downturns, provisions that were more forwardlooking and higher were needed for credit losses. IFRS 9 is the answer to this (for reporting purposes) and the provisioning based on expected credit losses (rather than incurred) is the ingredient enabling this. A relevant consideration in this context is the expectation of lifetime for the instrument (e.g. a credit card) and since the phase of IFRS 9 concerning provisioning for loan losses is recently published, how to measure expected lifetime is not yet established.
For this purpose, I introduce a concept called “End of lifetime event”. This concept brings together a common form of credit risk model for credit card portfolios and similar (where risk rating is based on how late the borrower is on a payment) with events related to expectations of how this type of instrument is managed. The result is a list of End of lifetime events that will function as absorbing states in the Markov chain implemented to estimate lifetimes.
The Markov chain is a popular form of model, suitable for this task, both in its connection to common credit risk models and broad applicability. The selection methodology is based on testing if different candidate models possess the Markov property (that the distribution of future state depends on the past only in the present state), and finding a good tradeoff between complexity (as low as possible) and accurate modelling of the data. For this, two statistical tests are used and a firstorder Markov chain is selected, shown to be dependent on maximum historical risk of a borrower (which is reasonable, since this would explain a lot about a borrower beyond how late he or she currently is on a payment).
The parameters for all allowed transitions in this extended model are estimated based on data from a portfolio of credit cards provided by a bank, where transitions between internal risk ratings (or states) were used for estimating the transition probabilities in the model. It is convenient at this point that a Markov chain is used, since expected lifetime now corresponds to what is known as expected absorption time, a straightforward tool in analyzing absorbing Markov chains.
There is a need to modify the way expected lifetime for this model is calculated, since the End of lifetime event for removal of what is known as the undrawn commitment component (the difference between the credit limit on e.g. a credit card and how much has been drawn) occurs with a delay from when the process reaches the corresponding absorbing state in the Markov chain. So, (expected) credit losses should be estimated over the period given by the following expression: (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/8568367
 author
 Berglund, Jonas
 supervisor

 Magnus Wiktorsson ^{LU}
 organization
 course
 FMS820 20161
 year
 2016
 type
 H2  Master's Degree (Two Years)
 subject
 language
 English
 id
 8568367
 date added to LUP
 20160126 11:46:26
 date last changed
 20160126 11:47:09
@misc{8568367, abstract = {This paper sets out to estimate expected lifetime of revolving credit facilities (e.g. credit card products) and is motivated by the introduction of the International Financial Reporting Standard 9 (IFRS 9) and its requirements for loan impairments. The reporting entity is required to estimate lifetime expected credit losses for certain nancial instruments. In practice, maximum contractual period for revolving credit facilities cannot be used in dening lifetime for the facility and credit risk mitigation actions need to be considered. A data set for a retail credit card portfolio was provided by a Nordic bank and for the lifetime denition derived, a model based on a conditional Markov chain was selected. Expected lifetime was estimated and an analytical expression for expected lifetime of revolving credit facilities was derived and validated.}, author = {Berglund, Jonas}, language = {eng}, note = {Student Paper}, title = {Estimating expected lifetime of revolving credit facilities in an IFRS 9 framework}, year = {2016}, }