The Restructuring Debate Rumbles On The restructuring credit event is currently the most controversial issue in credit derivatives documentation as it can be much softer than failure to pay or bankruptcy. Soft credit events can expose the protection buyer to Cheapest-to-Deliver risk. In hard restructurings it is likely that same seniority debt will trade at a similar cash price irrespective of maturity or currency. However, in a soft scenario debt may still trade on a yield basis and cash prices may not converge. The protection buyer's Cheapest-to-Deliver (CTD) option is therefore of greater value in soft restructuring scenarios where the protection seller could be delivered an obligation trading substantially lower than the restructured obligation. The Conseco case led the U.S. market to adopt modified restructuring (mod-R) although this has not gained traction in Europe.
Market participants were absorbed in negotiations through last year seeking a wording that meets everyone's requirements. However, reconciling the requirements of hedging bank regulators, especially in Europe, and investors, such as U.S. insurance companies, who have been burned by existing wording looks increasingly like a quest for the mythical philosopher's stone. What may emerge is Europe adopts a form of modified-modified-restructuring that is less restrictive than the current mod-R with the U.S. sticking to mod-R. At the same time a variety of credits and counterparties will likely trade on a parallel basis excluding the restructuring event completely, no-R.
A Framework, Not A Rule Of Thumb
Market participants naturally crave a convenient rule of thumb relationship, such as default swaps with restructuring trade 20% wider than those without. However as all distress situations differ, a little analysis can go a long way. More useful is a matrix with which company specific scenarios can be weighed and which can be used equally for any version of the restructuring credit event.
As a basis for this, we can use the established methodology of adding the following two variables:
* the proportion of credit events that are restructurings (M);
* the expected recovery following a restructuring credit event.
Unfortunately There Is Insufficient
Historical Data On Either...
In particular, since the credit derivative market is young, there is not a long enough track record from which to make statistical inferences. This is especially the case in Europe, which is yet to experience a major restructuring credit event.
Credit rating agency default rate data are a starting point. These capture, bankruptcy and failure to pay credit events. The data also includes hard restructurings such as distressed exchanges but exclude softer restructurings, such as maturity extensions.
...And At Least One Is Not Independent
It should not be assumed that restructuring probability is an independent variable in the valuation equation. In particular banks are the dominant buyers of protection and use the market to hedge loans held on banking books. Thus decisions on how to deal with troubled corporate relationships may involve balancing conflicting economic forces of minimizing the loss on the loan and maximizing the profit on the credit derivative. In some large banks, portfolio hedging is organizationally separated from the lending book. However, if the credit-default swap documentation changes, it would probably be naïve to assume that banking behavior remains constant. The removal of restructuring as a credit event would tend to encourage bankers not to agree loan restructurings for troubled clients until a harder credit event, such as a missed coupon or bankruptcy, had been triggered.
In summary we believe that market convention on the restructuring credit event could itself exert an influence on the relative frequency of that credit event occurring.
A Restructuring Matrix
With a view about the recovery rate post a restructuring credit event and the likelihood of restructuring relative to default, we can calculate the theoretical drop in default-swap premium if the restructuring credit event, however defined, is removed from a contract.
The matrix approach is useful because the relative likelihood of restructuring differs between companies. This can be hard to estimate, however, many of the key drivers are components of any credit analysis and valuation. For example, a viable company which is overly reliant on short-term bank borrowings and has liquidity concerns is probably at high risk of suffering a restructuring credit event versus failure to pay or bankruptcy. Against this, the post restructuring recovery would likely be relatively high. The discount that no-restructuring should trade relative to restructuring should probably be somewhere in the bottom right corner of the matrix. By contrast, a more leveraged entity whose capital structure is comprised of widely-held long-term bond debt could well have a similar overall-default risk but a lower restructuring risk and a lower expected recovery if a restructuring actually occurred.
Case Study: France Telecom
In Europe some dealers have started quoting five-year France Telecom protection both with and without the restructuring credit event. These levels imply a range of recovery rates and relative restructuring probabilities.
Day One:
On Nov. 27, France Telecom five-year restructuring versus no restructuring was quoted at 65-105 basis points. What does this quote mean? It implies that the broker is prepared to:
* Buy five-year France Telecom protection with restructuring
and sell five-year France Telecom protection without
restructuring paying 105bps.
* Sell five-year France Telecom protection with restructuring
and buy five-year France Telecom protection without
restructuring picking up 65bps.
The five-year France Telecom protection with R was quoted at 295-305bps at that time. Therefore the implied quote for five-year France Telecom without restructuring was 200-230bps.
Next Day:
On Nov. 28, five-year France Telecom restructuring versus no restructuring was offered at 70bps. At this time five-year France Telecom protection with restructuring was quoted at 260-280bps implying a bid of 210bps for five-year France Telecom protection without restructuring.
Assuming a failure to pay/bankruptcy recovery (RD) of 30%, we can plot the curves of possible restructuring recover (RR) and relative likelihood (M) combinations for each of the above cases (Chart 1). Given these assumptions, we conclude the following:
* The market was implying lower and upper bounds for
M corresponding to RR=0% and RR=100% respectively.
We calculate the following boundary values for M: 0.35
and 4.36 for Day 1; 0.23 and 3.19 for Day 2.
* For a given M, case 2 suggested a higher restructuring
recovery rate, RR, implying a lower loss from the
restructuring credit event and therefore a lower offer.
* For a given RR, case 2 implies a lower likelihood of
restructuring and hence we observe a lower offer of 70bps
versus 105bps.
Investors who believed that implied RR values were too low for a given M, or implied M is too high for a given RR, could have sold five-year France Telecom with restructuring versus the same trade without restructuring and vice versa.
Alternatively investors could have pursued the following strategies:
* Buy a bond and buy protection with restructuring versus
protection without restructuring: This strategy made sense if
the investor believed that the restructuring credit event was
substantially more likely than the other two credit events
and was looking for protection solely from the restructuring
credit event. This was a cheaper way to buy protection.
* Buy bond and sell protection with restructuring versus with
restructuring: This strategy made sense if the investor
believed that the restructuring credit event was highly
unlikely and is looking to enhance yield by selling
protection on only the restructuring credit event.
| Table 1: Restructuring Matrix : Fall in Premium of a 150bps CDS (with Restructuring) | |||||||||
| Recovery Rate Following a Restructuring | |||||||||
| Likelihood of Restructuring Relative to other Two Credit Events ("M") | |||||||||
| 9-Jan | 4-Jan | 3-Jan | 2-Jan | 1 | 1.5 | 2 | 3 | 4 | |
| 15% | -13% | -24% | -30% | -39% | -56% | -66% | -72% | -79% | -84% |
| 25% | -11% | -22% | -28% | -36% | -53% | -63% | -69% | -77% | -82% |
| 35% | -10% | -20% | -25% | -33% | -50% | -60% | -66% | -75% | -80% |
| 45% | -9% | -18% | -22% | -30% | -46% | -56% | -63% | -72% | -77% |
| 55% | -7% | -15% | -19% | -26% | -41% | -51% | -58% | -68% | -74% |
| 65% | -6% | -12% | -16% | -22% | -36% | -46% | -53% | -63% | 69% |
| 75% | -5% | -10% | -12% | -17% | -29% | -38% | -45% | -55% | -62% |
| 85% | -3% | -7% | -8% | -12% | -21% | -29% | -35% | -45% | -52% |
| Assuming a 30% recovery following a Bankruptcy of Failure to Pay credit event. | |||||||||
| Source: Merrill Lynch |
This week's Learning Curve was written by Chris Francis, head of international credit research, Atish Kakodkar, and Barnaby Martin, in the credit derivatives research department, at Merrill Lynch in London.
