Financial Innovation and Default Rates Samuel Maurer Hoai-Luu Nguyen Asani Sarkar Jason Wei January 2, 2009 DAY AHEAD CONFERENCE 2009, SAN FRANCISCO These views belong to the authors, and do not necessarily those of the Federal Reserve Bank of New York or the Federal Reserve System.

2 Motivation Measured default rates have been unusually low relative to history and economists’ predictions

3 Why Models Over-Predicted Defaults Business cycle (not properly accounted for in models) Structural Break in default model relationships Omitted variable in default prediction model: financing Expanded menu of financing for distressed firms since 2004 –Traditional financing (bank loans; CP issuance) –Structured financing (High-yield CLO, CDO issuances) Rescue financing for distressed firms without access to traditional financing Substitute loans for bonds, increasing flexibility –Structured financing vehicles (CLO managers) Bring in new sources of capital Major buyers of leveraged loans

4 Growth in Traditional Financing

5 Growth in Structured Financing

6 Implications for Default Rates Default delayed or avoided, depending on investment opportunities Structural model: Default occurs if firm value V below a threshold V * –Financing may increase V (no change in recovery rates) or –Lower V * (lower recovery), perhaps due to the greater flexibility of leveraged loans compared to traditional financing (e.g. less covenants or PIK terms)

7 The Number of Covenants had been on the Decline

8 Analysis: Financing and Default Rates Year-rating-cohort analysis: Is there a reduction in the proportion of bonds defaulting early? Prediction model for monthly aggregate default rates –Evidence of structural break in most recent sample Structured financing, distance to default and default rates Traditional financing, distance to default and default rates

9 Results Evidence is consistent with “delayed defaults” –Proportion of early defaults are historically low Evidence of structural break in most recent sample Financing measures significantly related to distance to default and default rates

10 Contributions Empirically link default rates to financing –A new channel for endogenous default boundary (flexibility of loan terms) –Chen (2007): simultaneously determines firm’s capital structure and default decisions –Rajan, Seru, Vig (2008): Mortgage models under-predicted defaults in recent years due to lowered incentive to monitor –Mian and Sufi (2008): expansion of mortgage supply led to defaults –Keys et al (2008): securitization reduces screening by lenders –Leland and Toft (1996), Anderson, Sundaresan, Tychon (1996): endogenizes default boundary New channel for financial innovations to affect the economy –Lower long-run default rates by reducing the compensation required for bearing credit risk Spread credit risk to less risk-averse investors Reduce macroeconomic and financial volatility –May have delayed bankruptcy in the short-run by making more distress financing available and on better terms

11 Data Moody’s data on annual cumulative default rates by yearly cohort for speculative-grade issuers, ways to default –Missed/delayed payment of principal/interest –Legal blocks to timely payment (e.g. bankruptcy) –Distressed exchange Cumulative issuer-weighted default rates at the end of year, for bonds outstanding as of the beginning of the year

12 Cumulative Default Rates By Year

13 Low Percent of Early Defaults in Recent Years Relative to Prior Expansion Years

14 Predicting Monthly Default Rates Outcome variable: Changes in the default rate Moody default rates: trailing 12-month rates Change in rates potentially affected by default events up to 12 month past Calls for including several lags of explanatory variables

15 Determinants of Default Rate (All Variables in Changes) Distance to Default Number of standard deviations of asset growth by which the asset level > firm’s liabilities V: firm value; L: liability measure (st debt + 0.5*lt debt) μ: mean asset growth σ: standard dev of asset growth Macro conditions 10 year – 3 month term spread Unemployment rates Consumer expectations Credit quality High yield – IG credit spreads Stock returns

16 Results Distance to default Macroeconomic Conditions Credit Quality and Stock Returns Growth in Corporate Leverage Explanatory Variable Estimate t-stats Estimate t-stats Estimate t-stats Estimate t-stats Dependent variable:D Intercept ** ** DDEF, Lag * * 1.81 TERM, Lag ** ** * CONEXP, Lag * * * LEV_GR, Lag VARIABLES WITH MULTIPLE LAGS UEM,3 Lags +, SIG ,SIG CQ, 10 Lags +,SIG ,SIG 0 0 SRET,6 Lags +,SIG ,SIG Lags of DEF?D included? YES Adj-R

17 In-Sample Fit of Model Since 2006, the actual changes generally less than predicted

18 Stability Tests Has historical relationship between default rates and fundamentals changed? Statistical break tests: evidence of a break in 2003 –Factor breakpoint test (10% significance) –CUSUM test (5% significance) No further breaks in sample from 2004

19 Distance to Default and Financing

20 Distance to Default and Structured Financing: Results

21 Distance to Default and Traditional Financing: Results

22 Financing and Default Rates Two channels Indirect: Effect on defaults via its effect on default boundary –Fitted DDEF: part explained by financing –Residual DDEF: part orthogonal to financing Direct

23 Structured Financing and Default Rates

24 Traditional Financing and Default Rates,

25 Conclusions Proportion of early defaults are historically low –Consistent with “delayed defaults” (since defaults are now already turning up) Structural break in recent sample, coincides with period of rapid growth in financing Including finance in prediction model is informative –Significantly related to distance to default –Component of distance to default explained by financing positively related to defaults –Residual component negatively related to defaults