Analyzing Credit Behavior of an Asset Class using Historical Data:
A Case Study
April 22, 2013
Acknowledgements
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My sincere thanks to Bama for letting me use certain slides from her
presentation
In memory of…
all the ping pong balls sacrificed in the North-West corner
Image source: http://cargocollective.com/rmattgarcia/Ping-Pong-Trophy-1
Agenda
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Asset Class and Data
Scope of the study
Methodology and findings
Application
Asset class and Data
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More than 115000 loans covering three asset classes: Two-wheeler loans,
Commercial Vehicle loans, and MSME loans
Following details were available over a period of 5 years:
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Monthly billing and recovery
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Loan amount
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Borrower Occupation
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Borrower income
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LTV (for a certain asset class)
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Tenure
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City/State
Only such loans were considered for the study where the repayment
information is available for at least 12 months
More than 67000 loans were selected for the study
We would refer the three asset classes as A, B and C (in random order)
Scope of the Study
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Estimate transition probabilities to migrate from one PAR state to another
Observe payment behaviour to understand the time a loan typically spends
as PAR0 or PAR30 and whether loans default early or late in their
‘lifetime/tenure’
Observed transition probabilities for sub-portfolios of loans with certain
seasoning, loan tenure and LTV for collateralized loans
Methodology
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Estimating transition proportions: proxy for probabilities
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Suppose ‘x’ loans were observed in dpd-1 bucket during any time of the loan
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Of these ‘x’ loans, ‘y’ loans were also observed to be in dpd-30 bucket
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So, x/y is the proportion of loans which ever moved from dpd-1 to dpd-30
Methodology
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Estimating transition proportions: proxy for probabilities
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Suppose ‘x’ loans were observed in dpd-1 bucket during any time of the loan
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Of these ‘x’ loans, ‘y’ loans were also observed to be in dpd-30 bucket
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So, x/y is the proportion of loans which ever moved from dpd-1 to dpd-30
Figure 1: Transition Probabilities
Methodology
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Estimating time-to-PAR
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For each loan which was ever observed in dpd-30, determine the time elapsed before it first
entered the dpd-30 bucket
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Time elapsed could be observed in absolute scale (e.g. months) or in scale relative to its
tenure (e.g. 30% of loan tenure)
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A frequency distribution of the time elapsed for all such loans could be useful
Methodology
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Estimating time-to-PAR
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For each loan which was ever observed in dpd-30, determine the time elapsed before it first
entered the dpd-30 bucket
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Time elapsed could be observed in absolute scale (e.g. months) or in scale relative to its
tenure (e.g. 30% of loan tenure)
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A frequency distribution of the time elapsed for all such loans could be useful
Asset A
Asset B
Figure 3: Time to Hit PAR30
Asset C
Methodology
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Estimating Time-spent-in-delinquency
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As per the latest observation, a loan could be ‘Current/Mature’ or ‘Delinquent/Default’
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For each ‘Current’ loan, determine the time spent as delinquent (Note that a loan, current
now, could have been delinquent in the past)
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Similarly for each delinquent loan (no matter dpd-1 or dpd-180 or default), determine the
time spent as delinquent
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‘Time’ could be absolute or relative (though relative helps in comparing across tenures)
Methodology
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Estimating Time-spent-in-delinquency
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As per the latest observation, a loan could be ‘Current/Mature’ or ‘Delinquent/Default’
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For each ‘Current’ loan, determine the time spent as delinquent (Note that a loan, current
now, could have been delinquent in the past)
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Similarly for each delinquent loan (no matter dpd-1 or dpd-180 or default), determine the
time spent as delinquent
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‘Time’ could be absolute or relative (though relative helps in comparing across tenures)
Asset A
Asset B
Figure 2: Life Spent in Delinquency
Asset C
Application
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Consider a portfolio of loans of asset class A
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‘Time-to-Default’ analysis would provide us the distribution of defaults over the tenure of
loans (say 80% dpd-1 or PAR0 cases occur after 3 month or 25% of loan tenure and rarely
after 75% of loan tenure)
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Use transition proportions to calculate losses and recoveries in subsequent periods
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Stress the assumptions on transition proportions and ‘time-to-default’ to test the portfolio
Thank you