# Presentation: 763188

## * 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.

- * 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.

## 
Introduction-Motivation
Contributions of the paper

Database

Empirical evidence
 Event study
 Econometric modeling
 Data description
 Estimation results

Policy analysis

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

## 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?

## 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)

## 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.

## 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

## DatabaseBanco 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

## 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

## DatabaseOur credit line database

- _Our data filtering:_
- Too much data at the monthly level, so we fall back to annual level using December data from 1985 to 2005
- Construction of credit line database:
- Find credits that have non-zero undrawn amounts
- Track them through time using borrower, bank, commitment amount, collateral, etc.
- Despite short maturities, assume same credit line
- Focus on credit lines granted to firms by only one bank
- combine several credit lines obtained from a single bank into one firm-level observation
- exclude credit lines for firms with multiple banking relationships

## DatabaseOur 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.

## DatabaseOur credit line database (cont.)

- Histogram of the usage ratio of all credit lines:
- 14% of credit line-year observations are zero
- 11% of observations are at 100% usage
- Remaining 75% of observations are roughly symmetric 	around the 50% usage mark

## Empirical evidenceEvent 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

## Empirical evidenceEvent study based on default (continued)

- _Summary of results:_
- Defaulting firms draw down a credit line more than non-defaulting firms, even 5 years before the default year.
- At 5 years prior, average rate for defaulting firms is 60%, as compared to 50% for nondefaulting firms.
- Drawdown increases monotonically as default approaches.
- At the default year, the average usage rate for defaulting firms reaches its maximum of about 90%.
- The Wilcoxon rank sum test and the mean test suggest that these two sets of usage ratios are significantly different at the 1% level for event years -5 through 0.

## Empirical evidenceEvent study based on default (continued)

- We also constructed the event time graphs for each individual year in the sample and found some variation.

## Empirical evidenceEconometric modeling

- We use several regression models to examine the determinants of credit line utilization.
- Denote the usage rate of credit line *i* granted to firm *j* by bank *k *at time *t* as RDRAWNijkt.
- Transform this ratio via the logistic function in order to increase its variability:

## Empirical evidenceEconometric modeling (continued)

*Where i* denotes the credit line, *j* the firm, *k *the bank* *and* t* the time.
- δit is 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)
- ηi is a fixed effect for the credit line, and εit is an error term

## Empirical evidenceEconometric 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.

## Empirical evidenceData description

- Table 3 presents simple descriptive statistics on our dependent and explanatory variables:

## Empirical evidenceBaseline estimation results (Table 4)

## Empirical evidenceBaseline estimation results (Table 4)

- Panel data estimation technique:
- Within-groups estimation to address possible correlation between ηi and 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

## Empirical evidenceBaseline 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.

## Empirical evidenceEconometric 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*)

## Empirical evidenceFurther 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

## Empirical evidenceFurther 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

## Empirical evidenceFurther 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

## Empirical evidenceFurther 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)

## Empirical evidenceSummary 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

## Policy analysisEAD in Basel II

- Capital is held to ensure that a bank is likely to remain solvent, even if it suffers unusually large losses
- Banks establish their own economic capital, but the regulators set minimum capital requirements.
- Basel II encompasses the latest set of international capital regulations, which are risk-based and closely associated with economic capital calculations in design.
- Simplified capital calculation (w/ fixed one year maturity):
- EL = PD × Loss Given Default × Exposure at Default
- Hence, require values for these four parameters.

## Policy analysisEAD 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.

## Policy analysisPrior 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.

## Policy analysisPrior 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

## Policy analysisOur 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%

## Policy analysisOur 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