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Gabriel Jiménez Vicente Salas Jesús Saurina Banking Regulation General Directorate Banco de España

Determinants of Collateral. Gabriel Jiménez Vicente Salas Jesús Saurina Banking Regulation General Directorate Banco de España. Summary. Objectives and contribution Hypothesis Database Econometric model Results Conclusions. Objectives.

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Gabriel Jiménez Vicente Salas Jesús Saurina Banking Regulation General Directorate Banco de España

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  1. Determinants of Collateral Gabriel Jiménez Vicente Salas Jesús Saurina Banking Regulation General Directorate Banco de España 2nd International Conference on Credit Risk - April 2004

  2. Summary • Objectives and contribution • Hypothesis • Database • Econometric model • Results • Conclusions 2nd International Conference on Credit Risk - April 2004

  3. Objectives • To study the determinants of the use and amount of collateral in loan contracts • To discern among various theoretical hypothesis • To contribute to the limited empirical literature with a huge sample of individual bank loans in Spain 2nd International Conference on Credit Risk - April 2004

  4. Contributions of the paper • New test of the relationship between collateral and the quality of the borrower (ex ante and ex post credit risk) • Impact of relationship lending on collateral • Impact on collateral of: • Macro variables (GDP and interest rates) • Competition in the loan market • Efficiency of lenders • Determinants of the amount of collateral 2nd International Conference on Credit Risk - April 2004

  5. Hypothesis – Asymmetric Information (I) • Lenders may face hidden information and hidden action problems. • Hidden information: the lender does not know the quality of the borrower at the time pf the loan contract. • Hidden action: the lender does not observe the action taken by the borrower after the loan contract. 2nd International Conference on Credit Risk - April 2004

  6. Hypothesis – Asymmetric Information (II) 1. With hidden information, borrowers will sort themselves according to the “private information paradigm”: • High quality borrowers will choose contracts with collateral and low interest rates • Chan and Kanatas (1985), Bester (1985), Besanko and Thakor (1987) • Degryse and Van Cayseele (2000) 2nd International Conference on Credit Risk - April 2004

  7. Hypothesis – Asymmetric Information (III) 2. With no hidden information but with hidden action, lenders will sort borrowers according to the “observed risk paradigm”: • Good quality borrowers will get loans with no collateral • The amount of collateral will be: • An increasing function of real risk-less interest rate (to cope with the increase of moral hazard due to the higher loan interest rates) • A decreasing function of the size of the loan (moral hazard is reduced since larger projects increase the pay off to the borrower) • Boot, Thakor and Udell (1991) • Berger and Udell (1990), Jiménez and Saurina (2003) 2nd International Conference on Credit Risk - April 2004

  8. Hypothesis – Asymmetric Information (IV) 3. With hidden information and hidden action, most loans will be collateralized • The amount of collateral will be: • An increasing function of real risk-free interest rate • Boot, Thakor and Udell (1991) 2nd International Conference on Credit Risk - April 2004

  9. Hypothesis – Asymmetric Information (V) • Quality of the borrower: • Default t-1: a previous loan in default at the time the loan decision is made. • Default t+1: if the borrower defaults next year and not defaulted previously. • Private information: negative relationship between collateral and Default t+1. • Observed risk: + rel colateral and default t+1. • Clearer test; usually, risk premium and collateral (simultaneous). 2nd International Conference on Credit Risk - April 2004

  10. Hypothesis – Macroeconomic Conditions • No previous theory available. • We expect lenders to ask collateral more frequently in recessions than in upturns. • We expect less collateral in periods of high interest rate, because lenders concentrate in their best borrowers. • The effect of the geographic risk might be positive as banks ask for more collateral in local markets in difficulties. 2nd International Conference on Credit Risk - April 2004

  11. Hypothesis– Relationship Banking (I) • Relationship banking allows lenders to learn about hidden attributes and hidden actions of the borrowers. Thus, we expect a negative relationship between the use of collateral and the length and scope of the relationship. • Boot and Thakor (1994) • Berger and Udell (1995), Harhoff and Körting (1998) • If the relationship banking results in a “hold-up” situation (monopoly position of the lender), a positive sign will be expected. • Greenbaum et al (1989), Sharpe (1990), Rajan (1992) • Degryse and Van Cayseele (2000) 2nd International Conference on Credit Risk - April 2004

  12. Hypothesis– Relationship Banking (II) • We use three variables to test the impact of relationship banking: • Duration of the relationship bank-borrower. • Number of loans the borrower has with the bank. • Number of banks with which the borrower has loans. • + or – sign can be expected in the 3 variables depending on reputation or hold up dominance. 2nd International Conference on Credit Risk - April 2004

  13. Hypothesis – Competition in Loan Markets • Without hidden action problems, a monopolistic lender will never ask for collateral. • Besanko and Thakor (1987a) • Competition has an indirect effect in the relationship: in more competitive markets the incentives to invest in information will be reduced. Thus, collateral is the way to hedge from default. • Chang et al (1986), Diamond (1991), Petersen and Rajan (1995) • No previous empirical work: We expect a negative relationship between market concentration (less competitive market) and collateral 2nd International Conference on Credit Risk - April 2004

  14. Hypothesis – Experience and Preferences of Lender • More specialized banks and those of larger size should have comparative advantage in terms of evaluating credit risk. • Banks with less experience could use collateral as a substitute of screening. • Manove and Padilla (1999, 2001) • We expect a negative relationship between collateral and experience. • Savings banks are more conservative and have less historical specialization than commercial banks. Thus, they might ask for more collateral. 2nd International Conference on Credit Risk - April 2004

  15. Control variables • Characteristics of the loan: • Size of the loan • Characteristics of the borrower: • Industry (11 groups) • Region (50 provinces) 2nd International Conference on Credit Risk - April 2004

  16. Database (I) • Central de Información de Riesgos (CIR). • Credit Register run by Banco de España. • Institutions to declare: banks, savings banks, credit cooperatives and specialized finance firms. • For every loan: • Type of instrument / Currency / Maturity / Collateral / Amount of collateral / Amount / Asset quality (defaulted or non-defaulted). • NO information on interest rates. 2nd International Conference on Credit Risk - April 2004

  17. Database (II) • Information of each borrower: • Individuals/companies. • Region. • Industry (NACE 3 digits). • Number of banking relationships / No of years with the bank / No of loans with each bank. • Every loan of more than 6,000€ made by any credit institution must be recorded. • Monthly information since 1984. 2nd International Conference on Credit Risk - April 2004

  18. Database (III) – Sample Used • All financial loans (2 millions) at the year that were granted by commercial and savings banks. • Complete business cycle. • Decembers from 1985 to 2002 • Group of study: loans to non-financial firms • 4 sub-samples to get more homogeneous groups: • In terms of the availability of past information in the CIR • Old borrowers: there is past information on the database at the time the loan is granted • New borrowers: appear in the database for the first time • In terms of maturity • Short term: 1 to 3 years • Long term: More than 3 years 2nd International Conference on Credit Risk - April 2004

  19. 2nd International Conference on Credit Risk - April 2004

  20. Summary statistics - Old borrowers 2nd International Conference on Credit Risk - April 2004

  21. Summary statistics – New borrowers 2nd International Conference on Credit Risk - April 2004

  22. Econometric model (I) • Probit model that explains Collateral as a function of the previous explanatory variables. • Size of the loan might be endogenous. • “Two Stage Conditional Maximum Likelihood (2SCML)” (Rivers and Vuong, 1988). • First stage: regress log(size of the loan) on all the exogenous variables to obtain the standardized residuals. • Second stage: run a Probit model for the pool of 18 years including the previous residual as a new variable. The binary variable is yit = 1 if the loan i has collateral in t yit = 0 otherwise • Instrument: non-performing loans ratio of the economic sector of the borrower in t-1 • This model allows for an endogeneity test 2nd International Conference on Credit Risk - April 2004

  23. Econometric model (II) • The Probit model is run four times following the previous procedure for short-term and long-term loans and for old and new borrowers. • Subsample of loans of old borrowers controlling for fixed effects as a robustness test of results. • Amount of collateral: • Multinomial Logit mode. • 100% collateral, 50% or more collateral, no collateral. 2nd International Conference on Credit Risk - April 2004

  24. Results - Probit Estimation More Collateral (higher Prob.) Less Collateral (lower Prob.) • Borrower’s Risk • Ex ante risk: • Defaultedt-1 No of years of the firm • Ex post risk: • Defaultedt+1 • Economic Condition • Recessions Upturns (Asymmetric effect) • Geographic risk (ST) Higher real interest rate • Relationship banking • No of loan operations Duration of the relationship with the same bank (ST) No of lenders of the borrower • Competition • Less competition • Efficiency • Size of the lender • Commercial Banks • More specialized banks in firms • Control variable • Size of the loan (endogenous) 2nd International Conference on Credit Risk - April 2004

  25. Old Borrowers. Probit Estimation 2nd International Conference on Credit Risk - April 2004

  26. NewBorrowers. Probit Estimation 2nd International Conference on Credit Risk - April 2004

  27. Results - Comparisons Borrower’s Risk • Overall evidence of observed risk paradigm But • Private information paradigm is more present in: • New borrowers: marginal effect of Defaultedt+1 is only half for old borrowers • Long term loans: semielasticities (%) for old borrowers Short term Long term Defaltedt-1 86.0 31.0 Defaltedt+1 44.7 9.6 • Ex post risk: Defaltedt+1, as before Economic Conditions • GDP growth has more impact in long term loans Relationship Banking • Scope is relevant only for short term loans Control Variable • Size of the loan has more impact for new borrowers 2nd International Conference on Credit Risk - April 2004

  28. MarginalEffects 2nd International Conference on Credit Risk - April 2004

  29. Robustness test - Old Borrowers – Fixed effects 2nd International Conference on Credit Risk - April 2004

  30. Results - Amount of Collateral • Results obtained for 100% collateralized loans are almost the same than before (good approximation of the multinomial Logit model) • The results comparing 100% and 50% collateralized loans are consistent with the predictions of Boot, Thakor and Udell (1991): The amount of collateral is • An increasing function of real risk-free interest rate . • A decreasing function of the size of the loan. • The other results ask for more theoretical work. 2nd International Conference on Credit Risk - April 2004

  31. Amount of Collateral - Multinomial Logit Estimation 2nd International Conference on Credit Risk - April 2004

  32. Information content of collateral signal • We can use these results to evaluate (using Bayes rule) the estimated probability of default one year ahead of a collateralized loan. • Collateral is more informative of higher credit risk in short term loans that in long term ones, which is in line with the observed risk paradigm. 2nd International Conference on Credit Risk - April 2004

  33. Information content of collateral signal • Predicted probabilities of Default in t+1 2nd International Conference on Credit Risk - April 2004

  34. Conclusions • Lenders can observe the quality of the borrowers (sorting them by observed risk paradigm). More deeply for: • Short term loans and older borrowers • When the borrower is likely to have information advantage over the lender, there is relatively more sorting by private information: • Long term loans and new borrowers • Adverse macroeconomic conditions (recessions) increase the use of collateral • Higher real interest rates decrease the use of collateral. 2nd International Conference on Credit Risk - April 2004

  35. Conclusions • The longer the relationship, the lower the likelihood to pledge collateral • The larger the number of lenders, the lower the likelihood to pledge collateral • More competition implies more collateral • More specialized and larger banks reduce the use of collateral • Savings banks ask for more collateral 2nd International Conference on Credit Risk - April 2004

  36. Conclusions • The amount of collateral: • increases with higher real risk-free interest rates • decreases with the size of the loan 2nd International Conference on Credit Risk - April 2004

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