Fundamentals-Based versus Market-Based Cross-Sectional Models of CDS Spreads by S. Das, P. Hanouna and A. Sarin Discussed by J. Helwege FDIC September 2006

Summary  Uses a panel dataset of CDS spreads to evaluate which factors determine the magnitude of credit spreads  Cross-section but also time-series  Runs a horse race between accounting data determinants of spreads and market-based variables.  Important to evaluate usefulness of accounting if private firms  Structural models tend to ignore accounting data, which may be a mistake if it has a lot of explanatory power

Motivation  Does this help answer the question of what determines yield spreads?  Is this a better alternative to studies such as Collin-Dufresne, Goldstein and Martin or Elton, Gruber, Aggarwal, and Mann?  Put more emphasis on liquidity?  Longstaff vs. Sundaresan on liquidity issues in CDS  No comparison to old papers that ask what determines yield spreads on corporate bonds (e.g., Fisher 1959)  Argument that ranking in the cross section is all one needs for convergence trades  Can we do more with this?

Accounting vs market  Which distinction is more relevant?  market data vs. book data  Structural model vs Altman type prediction of probability of default  If former, volatility of operating earnings for the industry is a good replacement variable for equity volatility (see Helwege and Liang, JFE)  If latter, want option inputs, esp. vol, separate from accounting vars

Estimation issues  In any unbalanced panel, have to ask whether the sample is a random, representative sample  Can get long time series on some firms and not on others  Is existence in the dataset random?  Use fixed effects  Are multiple obs in the dataset giving a fair sense of weight of a firm?  With corp fin get about 20 obs for each firm, so weights are fairly even  With stocks, might even toss out of dataset if not at least 60 obs per firm  If more obs in time series, is it liquidity? If so, create a variable for the number of times it shows up?

Estimation issues  With bonds, there are multiple obs on the same firm at a give point in time, depending on the firm’s capital structure  Need to weight data  With CDS, can also get variation by maturity of contract with same underlying collateral?  In corp bond lit, use three schemes (see Warga and Welch 1993)  Use all the data  One bond per firm, preferably a representative one  Average features of bonds for a firm, put in one ob  Table 10 helps by using only 5 year contracts

Rankings  To determine whether accounting or market variables better explain the ranking of CDS spreads, the authors use CAP curves.  What is disadvantage of Wilcoxon rank sum tests?

Exposition  When can we get rid of these kinds of sentences?  “The growth of the credit derivatives market since the turn of the century has been astounding. The …OCC reported credit derivative volumes of $287 billion at the end of Various estimates now put this volume at over $15 trillion.”  “Credit Default Swaps are contingent claims with payoffs that are linked to the credit risk of a given entity.”  A CDS is a default insurance contract…”

What I especially like  Gets back to credit risk instead of trying to say that yield spreads are all about liquidity  Finds an important role for accounting data  Further proof that the Merton model’s probability of default and KMV’s EDF are not sufficient statistics  Gives us confidence that we can do something with private firm’s credit risk  Puts a large weight on the ability to rank a group of credit risky products – maybe the best way to approach the analysis if we cannot get a handle on liquidity premia