Credit Default Swap Trading Strategies

Diplomarbeit von Wolfgang Schöpf

Introduction:

Credit default swaps are by far the most often traded credit derivatives and the credit default swap markets have seen tremendous growth over the past two decades. Put simply, a credit default swap is a tradeable contract that provides insurance against the default of a certain debtor.

Initially, when the first form of a credit default swap (CDS) was traded in 1991, they were mainly used by commercial banks in order to lay off credit risk to insurance companies. However, focus shifted in the subsequent years as new players entered the market. Hedge funds became big players, money managers and reinsurers entered, and banks started to not only buy protection on their assets but also sell protection in order to diversify their portfolios. All this led to today’s CDS market being dominated by investors rather than banks and, as a consequence, CDSs are now structured to meet investors’ needs instead of those of the banks.

Over the same time as this shift to an investor orientated market took place, CDS markets grew at an astonishing rate with notional amount outstanding pretty much doubling every year until peaking in the second half of 2007 at USD 62,173.20 billions. The need to effciently transfer credit risk as well as the increasing standardization of CDS contracts by the International Swaps and Derivatives Association propelled this development. Only in 2008 did the notional amount outstanding in CDSs retract for the first time and come down to USD 31,223.10 billion in the first half of 2009. A partial reason was the full blown financial crisis in which CDSs also played a prominent role.

The demise of Lehman Brothers, for example, triggered roughly USD 400 billion in protection payments and American International Group needed to be bailed out in 2008 because it had sold too much CDS protection. Amongst other concerns, these incidents highlight the systemic importance of CDSs. Combined with the phenomenal growth of CDS markets, this makes CDSs a highly relevant component of the current financial environment and a fruitful subject for academic research.

Today, just like most other financial instruments, CDSs serve a multitude of purposes spanning hedging, speculation, and arbitrage. The aim of this thesis is to explore these uses further and answer the following research questions:

What CDS trading strategies are commonly used and how does a selection of these strategies – CDS curve trades including forward CDSs, and CDS basis trades – work in detail?

To answer these questions, the thesis is structured as follows: Chapter 2 gives an introduction to CDSs. It explains how CDSs work, what their specific characteristics are and discusses important legal considerations. CDS markets are described and the main uses of CDSs are introduced in more detail. Finally, the basic structure of forward CDSs is also introduced.

Chapter 3 then proceeds to present the valuation of CDSs. The risk neutral pricing framework in which valuation takes place is shortly touched upon. Default probabilities or, in other words, the likelihood that a debtor will not honor its commitments are explained. Various credit models are introduced and, based on all this, the actual pricing of CDSs, forward starting CDSs, and their marking-to-market is explained.

Whereas the first two chapters lay out the theoretical foundation, the remaining chapters cover the trading strategies themselves. Chapter 4 is about CDS curve trades. The CDS curve, similarly to the term structure of interest rates, gives the price of CDS protection as a function of maturity. Therefore, any trade that is intentionally exposed to changes in the CDS curve shape, falls into this category. This chapter discusses benefits and caveats of trading the curve. Determinants of the curve shape and a model to get a view on possible curve changes are explained. Once one has a view about how the curve will likely change, flatteners, steepeners, butterflies or forward CDSs can be used to profit from that view. Since each of these trades requires multiple CDS positions, various schemes for weighting these positions relative to each other are then explored. Lastly, profit and loss drivers for curve trades are analyzed.

Following CDS curve trades, Chapter 5 is all about trading the CDS basis. Loosely defined, the CDS basis is the difference between the price of credit in the CDS markets as measured by the CDS spread and the price of credit in the cash markets as measured by the asset swap spread or the Z-spread. Through arbitrage, the price of credit should be the same in the two markets and the basis be zero if it were not for a variety of technical and fundamental factors that cause the basis to generally be non-zero. These factors are explained and, following that, the chapter lines out how to trade the basis, what the profit and loss drivers for basis trades are, how to choose the weights, and, finally, what the associated risks are.

The purpose of Chapter 6 is to give an outlook to other CDS trading strategies that are frequently used and to serve as a starting point for further study. One included strategy is capital structure arbitrage which tries to profit from mispricings between a company’s debt and equity. Another strategy is trading CDSs versus equity puts which tries to profit from different payoffs in case of default. Lastly, there is convertible bond arbitrage which has the purpose of gaining cheap exposure to at least one of the underlying risk factors of a convertible bond – credit risk, interest rate risk or equity risk – by hedging the others.

Finally, Chapter 7 summarizes and concludes.

Table of Contents:

1.	Introduction	1
2.	Credit Default Swaps	4
2.1	Trading Credit Risk	5
2.2	Legal Considerations	6
2.3	The CDS Markets	7
2.4	Main Uses of CDSs	8
2.4.1	Speculation with CDSs	8
2.4.2	Arbitrage with CDSs	8
2.4.3	Hedging with CDSs	9
2.5	The Premium Leg	9
2.6	The Protection Leg	11
2.6.1	Credit Events	11
2.6.2	Settlement	11
2.7	Forward Starting CDSs	12
3.	Valuation of CDSs	14
3.1	Risk-Neutral Pricing	15
3.2	Default Probabilities	16
3.3	Credit Modeling and The Building Blocks	16
3.3.1	Zero Recovery Risky Zero Coupon Bond	18
3.3.2	The Hazard Rate Model	19
3.3.3	Survival Probability	19
3.3.4	The Value of $1 Paid at Default	20
3.4	Valuing the Premium Leg	21
3.4.1	Regularly Scheduled Premium Payments	21
3.4.2	Accrued Premium if Default Occurs Before the First Premium Payment Date	22
3.4.3	Accrued Premia if Default Occurs After the First Premium Payment Date	22
3.5	Valuing the Protection Leg	23
3.6	Marking to Market	24
3.7	Valuation of a Forward CDS	25
4.	CDS Curve Trades	27
4.1	Potential Benefits of Curve Trading	28
4.2	Caveats of Curve Trading	28
4.3	Measuring the Curve Shape	29
4.4	Determinants of the Curve Shape	29
4.4.1	Fundamental Drivers of the Timing of Default and Curve Shape . . .	31
4.4.2	Upward, Flat and Inverted CDS Curves	32
4.5	The Cross Section of CDS Curves	33
4.5.1	Models for the Cross Section	35
4.6	Curve Trading Weighting Schemes	36
4.6.1	DV01 Neutral Weighting	36
4.6.2	Notional Neutral Weighting	38
4.6.3	Flat Carry Weighting	39
4.6.4	Percentage Slope Neutral Weighting	39
4.6.5	Log Model Weighting	40
4.7	Profit and Loss Drivers for Curve Trades	41
4.7.1	Time Effects: Carry and Roll Down	41
4.7.2	DV01 and Convexity	42
4.8	Steepeners and Flatteners	43
4.9	Butterflies	45
4.10	Forward CDSs	46
4.10.1	Overestimating Future Spot Spreads	46
4.10.2	Trading Forward CDSs	48
5.	CDS Basis Trades	49
5.1	The Asset Swap	50
5.2	The Z-Spread	52
5.3	Drivers of the CDS Basis	54
5.3.1	Technical Basis Drivers	54
5.3.2	Fundamental Basis Drivers	56
5.4	Trading the Basis	57
5.4.1	Relative Value Trading	58
5.4.2	Enhancing Returns	60
5.5	Profit and Loss Drivers for Basis Trades	60
5.6	Basis Trading Weighting Schemes	61
5.6.1	Notional Neutral Weighting	61
5.6.2	Default Neutral Weighting	62
5.6.3	DV01 Neutral Weighting	63
5.7	Basis Trading Risks	64
6.	Further Strategies	66
6.1	Capital Structure Arbitrage	66
6.2	CDSs and Equity Puts	67
6.3	Convertible Bond Arbitrage	68
7.	Conclusion	70

Text Sample:

Chapter 4, CDS Curve Trades:

„Taking credit exposure to a company for two years is very different from taking credit exposure to a company for 10 years. It is this variation in a company’s credit risk through time that curve trades try to monetize”.

Just as the CDS markets have grown enormously over the past decade, liquidity in maturities other than the five year benchmark maturity has grown in accordance. As a consequence, traders have increasingly been attracted to trade CDS curves. What is more, „most users of credit default swaps have curve exposures in their portfolios, even if they do not trade curves explicitly. Similarly to the term structure of interest rates, the CDS curve gives the par spread of a CDS as a function of maturity.

Potential Benefits of Curve Trading:

„Curves provide an amazingly rich asset class, with many applications” as Rennison et al. point out. Together with Beinstein and Scott, they list the following possible advantages of trading CDS curves:

- If an investor has a view on the relative steepness of the curve, it can be better to trade the curve rather than a single point on the curve.

- With curve trades, an investor can structure a position on the curve such that it has no default risk.

- According to Anson, alpha are those returns that are contributable to investment skills whereas beta are those that comes from loading up on systemic risks, that cannot be diversified away. At that, curve trades can provide an additional source of alpha since they are less researched and more inefficiencies exist than in other asset classes.

- Curve trades can be carried out such that they have a low beta to the market.

- Directional credit views can be implemented at the most efficient point of the curve.

- Hedging can be implemented more efficiently if the full term structure is used.

- Option-like payoffs and many other risk reward profiles can be created.

- Especially when default neutral, curve trades can be very capital efficient.

However, as much as curve trades enrich the opportunity set of traders, they also increase the amount of factors that need to be taken into account. These are covered in the following section.

Caveats of Curve Trading:

One thing to consider is that curve trades can come with larger trading costs because they often require multiple positions and because tenors other than the five year benchmark have wider bid-ask spreads. Another consideration should be liquidity. During financial crisis, liquidity can drop significantly and, as markets dry up, it can become all but impossible or very expensive to exit existing positions. Even in good times, liquidity can be limited for less liquid contracts and off-benchmark maturities. Also, some curve positions can leave residual default risk that needs to be taken into account. Finally, trade economics such as carry and roll-down as described in Section 4.7 will generally be different to other asset classes.

Measuring the Curve Shape:

In order to be able to talk about CDS curve shapes, some objective measures of slope and level are needed.

One way to measure the slope or the steepness of the curve in absolute terms is the difference between the 5 and 10 year par spread, called the 5s10s slope. Setting this in relation to the 5 year spread gives the slope as a relative measure. This relative measure tries to capture how much more risky the second five years are perceived as compared to the first five years.

The level of the curve can be approximated with reasonable accuracy by the the five year benchmark spread.

In the following, I will focus mainly on the five to ten year part of the curve although most arguments apply equally well to the shorter or even longer end.

Determinants of the Curve Shape:

Most curve trading strategies are designed to profit from a particular view on the shape of the CDS curve. Therefore, it is good to know what factors determine the shape and, more importantly, what factors cause it to change.

Chen et al. find that different industries and credit rating classes show different curve dynamics. A lower rating, for example, goes together with much larger spreads for financials than for non-financials. Also, they find that credit risk is dynamically related to interest-rate factors. With regard to liquidity, their reference entity group with high liquidity has significantly higher average CDS spreads and the CDS curve is more upward sloping than for the lower liquidity group which implies that „investors choose to trade CDS contracts on firms that they perceive to have higher chances of downward rating migrations, or that high-profile firms generate more awareness of its potential risk of default.” As can be seen from Equation, the CDS par spread and therefore also the CDS curve is dependent on the assumed recovery rate, R, the LIBOR curve, Z(t, T ), and the survival curve of the reference entity, Q(t, T ). Of these three, the survival curve has the highest impact on the shape of the CDS curve. Both interest rate sensitivity and recovery rate sensitivity are much smaller than credit spread risk when spreads are not too high. The CDS curve is, therefore, determined to a great extent by market participants’ perceptions of the timing of a possible default. Against interest rates, CDS spreads are relatively insensitive because both the payments on the premium leg as well as the payments on the protection leg in Equation (3.16) are discounted using the same rate.

Let me give an example of how market participants’ expectations affect the curve. Suppose, that all traders in the market agree that it is impossible for a certain reference entity to default over the next two years. Of course, then, nobody would be willing to pay even a single basis point for protection against default within this period. As a consequence, the CDS curve would be flat at zero up until two years. If, on the other hand, market participants deem default in the close future very likely, protection against this possibility will have significant value and par spreads for close maturities will be high. Thus, different shapes of the CDS curve can be explained to a great extent by different expectations about the timing of a potential credit event. More formally, this timing is nothing else than the density of default times as expressed in Equation (3.4). The fundamental drivers of these default densities are discussed in the following section.

Interestingly, also, Rajan et al. find that the 3s10s part of the curve behaves rather differently from the 10s30s part for which they think, market segmentation is the culprit. Insurance companies, pension funds, and others that have long dated liabilities account for a large part of demand in latter section.