# Presentation: 917534

## 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 1999. 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