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YES 2011 Discussions Dubrovnik Economic Conference

YES 2011 Discussions Dubrovnik Economic Conference. Paul Wachtel Stern School of Business. New York University. Are some banks more lenient in implementation of placement classification rule?. Tomislav Ridzak. Overview. A fascinating piece of applied banking research

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YES 2011 Discussions Dubrovnik Economic Conference

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  1. YES 2011 DiscussionsDubrovnik Economic Conference Paul Wachtel Stern School of Business. New York University

  2. Are some banks more lenient in implementation of placement classification rule? Tomislav Ridzak

  3. Overview • A fascinating piece of applied banking research • Will be of interests to bank regulators, policy makers and, of course, the banks themselves. • Simple idea with big implications that need to be explored

  4. Idea • Companies have more than one banking relationship. • Do different banks rate the same bank differently? • There is going to be some random variation. • Can be important tool for bank examiners. • Can be informative about bank behavior • But… more to do…relate it back to policy

  5. Policy implications to explore • Do banks with less capital rate loans more leniently? • Do banks that are less profitable rate loans more leniently? • If answers are yes, then • Banks are ‘gaming’ the regulators. • Risk regulations of unclear value

  6. Data • Loans to non financial companies by 33 Croatian banks 2006-09 • Need to ‘prepare’ data • Define default • Handle collateral SINCE THERE IS SOME ARBITRARINESS, ROBUSTNESS TO DEFINTIONS SHOULD BE EXAMINED.

  7. Method from educational stats BANKS (or Graders) __________________ COMPAN IES (or Students) WHICH BANK IS GRADING IN A SIGNIFICANTLY DIFFERENT WAY?

  8. Method from educational stats BANKS (or Graders) __________________ COMPAN IES (or Students) WHICH BANK IS GRADING IN A SIGNIFICANTLY DIFFERENT WAY?

  9. RESULTS • There are differences – 2-6 banks are significantly grading away from the pack • But, how much should we expect? I need a benchmark of some kind. How much behavioral variation is ‘normal’? Appendix figures hint at some answers. • Small, insignificant relationship between relative leniency and coverage ratio (is that average for all of banks’ loans?) • Collateral correction should be for each loan

  10. CONCLUSION • Imaginative application. • But, what is the goal • So, regulators know more about banks • Or, research on bank behavior

  11. The role of demand and supply in cyclical fluctuations of household debt in Coratia Ivana Herceg

  12. Overview • Nice paper are a really important issue (not just a Croatia issue) • But, I am not sure why • I can understand what the paper sets out to do • But, it is hard to figure out from the paper what was actually done. • Which makes it hard to know what the results are

  13. The issue • It is common (lots of references shown) to attribute credit booms / crises to easy bank lending standards – supply shift • But credit booms occur when economy is growing and the income elasticity of the demand for credit is high – So it could be a demand shift. • So, which is it? S or D?

  14. Approach • Standard econometrics – identify S and D curves and see which is moving more in the boom. • Hard to find identifying restrictions • Not clear what data to use other than aggregates • Use information from Croatia household survey to infer bank supply behavior and household demand behavior. • Paper bogs down in confusing explanations of the econometrics and never tells us what it can accomplish.

  15. Infer supply • Look at households who took at loans (this is the bank’s product) and estimate a production frontier – standard application of stochastic frontier analysis

  16. Understanding Frontier Two inputs – efficient frontier Extent to which individual is below frontier – weakness of demand Extent to which frontier moves over time – change in supply. In crisis – Did frontier shift in or did demand [inefficiency so to speak] increase?

  17. Frontier results • 2008 and 2009 – are estimates (overall) significantly different? Seem to unstable to be so. • Frontier estimation does not include existing loans outstanding as a control.

  18. Alternative approach • Quantile regression estimates?Give me some intuition about what this does. • The results are shown in figures – and I have no idea what the figures show.

  19. Fig. 6 Usage of available credit limits WHAT AM I LOOKING AT? What is on each axis? 1 to 321 Households with loans? How ordered? Resutls from SFA or QR? How presented?

  20. Probability of loan and supply • Some kind of probit estimates for S and D (same 0-1 variable for both) • Never see the specification • Or the estimates • Or any tests of the identifying variables (in footnote 14). Too big an issue for a footnote. And, existence of prior loan seems relevant to both S and D

  21. Little puzzles • “Creditoworthiness….deteriorated” • Can we really treat 2008 and 2009 as different? • The one comparison does not answer original question – does S or D drive credit boom? • When is survey conducted? • Are othere waves available?

  22. Conclusion • In crisis/recession, banks tightened selection of households to offer loans. • Banks offered selected households larger loans • Households took down less. • Important result – • Need to clarify methodology • And show how you got the results

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