Outline

1. * The views expressed here are those of the authors and not necessarily those of the Banco de España, the Federal Reserve Bank of San Francisco or the Federal Reserve System.

2. Outline • Introduction-Motivation • Contributions of the paper • Database • Empirical evidence • Event study • Econometric modeling • Data description • Estimation results • Policy analysis

3. Overview • Corporate credit lines present a variety of interesting research questions: - Firms: Why establish? Why and when draw? - Banks: Why underwrite? How monitor? - Risk managers: What will exposure at default be?

4. Overview (continued) • Academic research on corporate credit lines has been relatively limited, most probably due to a lack of data. • Melnik and Plaut (1986): 101 survey respondents • Ham and Melnik (1987): 90 survey respondents • Berger & Udell (1995): ‘89 Nat. Survey of Small Bus. Fin. • Agarwal et al. (2004): bank proprietary dataset of 712 firms • Sufi (2006): handcrafted dataset of 300 publicly-traded firms based on SEC 10-K annual filings • Gatev and Strahan (2006): Dealscan for CP back-up lines • Our dataset based on the Banco de España’s credit register allows us to construct a comprehensive sample of firms (more than 200,000) over a long period of time (1985-2005)

5. Overview (continued)Contributions Contributions to the literature: • Corporate finance findings: • Five years prior to “default”, credit line usage is higher (60%) for these firms than for non-defaulting firms (50%). • The usage rate increases monotonically as default approaches, up to almost 90% at default. • Borrowers identified ex-ante as riskier, get less access to credit lines; analogous to Sufi’s profitability result. • Borrowers that have previously defaulted on loans, access their credit lines less, suggesting bank monitoring. • Credit line usage has cyclical characteristics; i.e., use increases in recessions and declines in expansions.

6. Overview (continued)Contributions 2. Public policy findings: • Exposure at default (EAD) exhibits procyclical behavior. To our knowledge, this is the first evidence on this point. • Various loan characteristics lower EAD: • Larger commitment size • Longer maturities • Collateral requirements • With respect to the Basel II framework, • EAD parameterization in the standard approach may be too low • For the foundation approach, it seems appropriate. • Not sufficient recognition of the procyclicality of EAD that could augment the expected procyclicality of capital

7. DatabaseBanco de España’s credit register • Banco de España maintains a credit register known as the Central de Información de Riesgos or CIR. • The CIR contains monthly loan-level information on all credits above a threshold of €6,000 granted by Spanish banking institutions (i.e., commercial banks, not-for-profit savings banks, credit cooperatives, etc.) since 1984 - hence, full census of Spanish corporate borrowing • The CIR contains detailed information on loan details: • borrower name, industry, province of headquarters • instrument type (i.e., commercial loan, etc.) • maturity • use of collateral • total commitment & amount drawn • default status

8. Database Banco de España’s credit register (cont.) • Definition of “default” status in the CIR database: • the borrower has loan payments overdue by more than 90 days, the legal definition of default in Spain, or • it has been classified as a doubtful borrower by the originating bank (i.e., the lender itself believes there is a high probability of non-payment). • Not a terminal state, so new loans can be granted. • Useful data transformations are available: • length of a banking relationship • number of loans outstanding • percent of a firm’s credit lines provided by a bank • Important shortcomings: • no pricing data (i.e., interest rates, fees, etc.) • no covenant information

9. DatabaseOur credit line database • After applying our filtering procedures, • We have a sample of 915,563 credit line-year observations • corresponding to 352,328 new credit lines • granted to 258,532 non-financial firms • by 444 banks • time period: Decembers from 1985 to 2005 • Roughly 85% of the observations are individual credit lines held by a firm with a single bank, and the remaining 15% of the observations correspond to firms that hold more than one credit line with a bank. • We examine the period from 1985 to 2005, which includes a deep recession around 1993, and two expansionary periods around the late 1980s - early 1990s and from 1997 onward.

10. Empirical evidenceEvent study based on default • Transform calendar time data into event time data • For each of the 17 years in the sample for which it is possible, define it as year zero and trace usage ratios back to year -5 • For defaulted firms, shows usage up to default point • For non-defaulted firms, just shows usage history

11. Empirical evidenceEconometric modeling (continued) Where i denotes the credit line, j the firm, k the bankand t the time. • δitis the time to default for credit lines that default at t+τ, τ>0 • two specifications • discrete, dummy variables; i.e.,δit(-5) = 1 & rest = 0 • continuous variable; i.e.,δit(-5) = -5 • Firmjt is variables that controls for firm characteristics • Bankkt is variables that control for bank characteristics • GDPGt is real, annual growth rate in Spanish GDP • RIRt is 3m real interbank interest rate (measure of funding) • ηiis a fixed effect for the credit line, andεitis an error term

12. Empirical evidenceEconometric modeling (continued) • Why include ηi ? - All credit line time-invariant characteristics, such as its maturity or collateral requirements, are included here. - The CIR has limited information on firm characteristics, and those effects not captured elsewhere go here. • What is Firmjt? - It is a measure of firm risk based just on the CIR, which is an indicator variable for whether the firm has ever defaulted on any CIR loan. • What is Bankkt? - A measure of bank risk based just on the CIR, which is non-performing loan ratio of the bank. - A measure of the size of the bank using the total share of the bank each year as a proxy.

13. Empirical evidenceBaseline estimation results (Table 4)

14. Empirical evidenceBaseline estimation results (Table 4) • Panel data estimation technique: • Within-groups estimation to address possible correlation between ηiand RHS variables • Dependent variable is ln_RDRAWNijkt RHS variables Sign Firm risk (-) Riskier firms draw down less; suggests bank monitoring Bank share (+) Large banks more confident(?) Bank NPL ratio (+) Riskier banks are more lenient GDP growth (-) Increase use in bad times Interest rates (+) High funding costs lead to increased use of credit lines

15. Empirical evidenceBaseline estimation results (continued) RHS variables Sign Years to default (+) • As a firm approaches default, it drawns down more heavily on its existing credit lines, even after controlling for CIR firm and lender characteristics as well as macroeconomic conditions. • Recall that years to default is a negative variable. • Provides confirmation & context for our event study diagram.

16. Empirical evidenceEconometric modeling (continued) • Extension to the baseline model: • Where Xit is a variable of interest that may have a differential impact on the usage rate depending on a firm’s time to default. • We use: • maturity indicator equal to one if greater than one year • collateral indicator • commitment size (with the top 5% tail winsorized) • Bank type since there was variation in non-performing loans at different points in the sample; see Salas and Saurina (2002, JFSR)

17. Empirical evidenceFurther estimation results Interacted variable Sign • Firm characteristics: Firm risk (-) -for a given time to default, lower quality borrowers draw less, suggesting banks monitor these firms more closely

18. Empirical evidenceFurther estimation results (continued) Interacted variable Sign • Line characteristics: Commitment size (-) -for a given time to default, larger credit lines are drawn less, suggesting more bank monitoring Maturity (-) -for a given time to default, credit lines with longer maturities are drawn less, suggesting more bank monitoring Collateral indicator (-) -for a given time to default, credit lines with collateral are drawn less, suggesting more bank monitoring

19. Empirical evidenceFurther estimation results (continued) Interacted variable Sign • Bank characteristics: Bank NPL ratio (+) -for a given time to default, credit lines by riskier banks are drawn more, suggesting less bank monitoring Bank share (+) -for a given time to default, credit lines by larger banks are drawn more, suggesting less bank monitoring Bank type -not statistically significant

20. Empirical evidenceFurther estimation results (continued) Interacted variable Sign • Macroeconomic conditions: GDP growth (-) -in recessions, credit line usage increases at any given time to default • Our two sets of macroeconomic results are the first empirical evidence of the procyclicality of credit line usage (and related to the Gatev & Strahan (2006) work on credit line origination and pricing)

21. Empirical evidenceSummary of empirical findings • Credit line usage is related to: • macroeconomic conditions • firm characteristics • bank characteristics • For firms that eventually default, credit line usage increases as the default approaches. • These increases are influenced • upward by bank riskiness and recessions • downward by firm riskiness, loan terms and expansions

22. Policy analysisEAD in Basel II • Basel II capital calculations: Regulatory capital = f(PD, M) * LGD * EAD, • Much work has been done on PD quantification and validation. • Much less work has been done on LGD. • Virtually no work has been done on EAD, even though it has a one-for-one effect on capital requirements.

23. Policy analysisPrior research on EAD Prior public research into EAD has focused on other forms. • Loan equivalent amounts (LEQ): EADit(τ) = DRAWNit + LEQit(τ) * UNDRAWNit -This is the more common form, since one is typically more interested in how much more of a line is at risk. • Credit conversion factors (CCF): EADit(τ) = CCFit(τ) * (DRAWNit + UNDRAWNit) Obviously related algebraically to LEQ, but perhaps a bit more intuitive for capital calculations based on total commitment amounts.

24. Policy analysisPrior research on EAD (continued) • Asarnow and Marker (1995, “the Citi study”): • presented (in an appendix) a set of LEQ estimates based on corporate loan data from 1988 to 1993 • Araten and Jacobs (2001, “the Chase study”): • based on 408 credit lines to 399 “defaulted” borrowers (i.e., no longer able to draw down) from 1994 to 2000 • Unconditional LEQ of 43.4% • i.e., borrowers that default draw down almost half of their undrawn commitment on their way down • LEQit(τ) is a decreasing function of τ • i.e., borrowers draw down more as they get closer to default

25. Policy analysisOur empirical EAD results • Based on 8,384 defaulted credit line-year observations corresponding to 2,883 credit lines • LEQit(τ) declines from 73% at τ = -5 to 36% at τ = -1 with an unconditional mean of 48%

26. Policy analysisOur empirical EAD results (continued) • Further analysis based on different characteristics: • Large credit lines have lower LEQs • One interpretation is that larger lines are more actively monitored by lenders • Another is that larger lines are granted to larger firms that don’t need bank financing as much, even in default • Credit lines with shorter maturities have higher LEQs • One interpretation is that longer maturities allow for better monitoring of the borrower • Collateralized credit lines have lower LEQs • Not surprising since a drawdown puts more collateral at risk to be seized by the lender