Download presentation

Presentation is loading. Please wait.

Published byKarlie Googe Modified over 2 years ago

1
Analyzing Credit Behavior of an Asset Class using Historical Data: A Case Study April 22, 2013

2
Acknowledgements ► My sincere thanks to Bama for letting me use certain slides from her presentation

3
In memory of… all the ping pong balls sacrificed in the North-West corner Image source: http://cargocollective.com/rmattgarcia/Ping-Pong-Trophy-1

4
Agenda ► Asset Class and Data ► Scope of the study ► Methodology and findings ► Application

5
Asset class and Data ► 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: ► Monthly billing and recovery ► Loan amount ► Borrower Occupation ► Borrower income ► LTV (for a certain asset class) ► Tenure ► 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)

6
Scope of the Study ► 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

7
Methodology ► Estimating transition proportions: proxy for probabilities ► Suppose ‘x’ loans were observed in dpd-1 bucket during any time of the loan ► Of these ‘x’ loans, ‘y’ loans were also observed to be in dpd-30 bucket ► So, x/y is the proportion of loans which ever moved from dpd-1 to dpd-30

8
Methodology ► Estimating transition proportions: proxy for probabilities ► Suppose ‘x’ loans were observed in dpd-1 bucket during any time of the loan ► Of these ‘x’ loans, ‘y’ loans were also observed to be in dpd-30 bucket ► So, x/y is the proportion of loans which ever moved from dpd-1 to dpd-30 Figure 1: Transition Probabilities

9
Methodology ► Estimating time-to-PAR ► For each loan which was ever observed in dpd-30, determine the time elapsed before it first entered the dpd-30 bucket ► Time elapsed could be observed in absolute scale (e.g. months) or in scale relative to its tenure (e.g. 30% of loan tenure) ► A frequency distribution of the time elapsed for all such loans could be useful

10
Methodology ► Estimating time-to-PAR ► For each loan which was ever observed in dpd-30, determine the time elapsed before it first entered the dpd-30 bucket ► Time elapsed could be observed in absolute scale (e.g. months) or in scale relative to its tenure (e.g. 30% of loan tenure) ► A frequency distribution of the time elapsed for all such loans could be useful Figure 3: Time to Hit PAR30 Asset AAsset BAsset C

11
Methodology ► Estimating Time-spent-in-delinquency ► As per the latest observation, a loan could be ‘Current/Mature’ or ‘Delinquent/Default’ ► For each ‘Current’ loan, determine the time spent as delinquent (Note that a loan, current now, could have been delinquent in the past) ► Similarly for each delinquent loan (no matter dpd-1 or dpd-180 or default), determine the time spent as delinquent ► ‘Time’ could be absolute or relative (though relative helps in comparing across tenures)

12
Methodology ► Estimating Time-spent-in-delinquency ► As per the latest observation, a loan could be ‘Current/Mature’ or ‘Delinquent/Default’ ► For each ‘Current’ loan, determine the time spent as delinquent (Note that a loan, current now, could have been delinquent in the past) ► Similarly for each delinquent loan (no matter dpd-1 or dpd-180 or default), determine the time spent as delinquent ► ‘Time’ could be absolute or relative (though relative helps in comparing across tenures) Figure 2: Life Spent in Delinquency Asset AAsset BAsset C

13
Application ► Consider a portfolio of loans of asset class A ► ‘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) ► Use transition proportions to calculate losses and recoveries in subsequent periods ► Stress the assumptions on transition proportions and ‘time-to-default’ to test the portfolio

14
Thank you

Similar presentations

OK

Credit Risk Management Chapters 11 & 12. Credit Risk Management uniqueness of FIs as asset transformers –What do we mean? –What type of risk do FIs.

Credit Risk Management Chapters 11 & 12. Credit Risk Management uniqueness of FIs as asset transformers –What do we mean? –What type of risk do FIs.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on pi in maths games Ppt on thyroid function test Ppt on duty roster Download ppt on mind controlled robotic arms in medicine Powerpoint template free download ppt on pollution Ppt on sanskritization in india Ppt on rational numbers for class 8 Ppt on word association test saturday Atoms for kids ppt on batteries Ppt on family tree