Single name credit default swaps are by far the most popular credit derivative instruments traded today.
Credit default swaps (CDSs) allow investors to buy protection against bond/obligation issuers defaulting on their obligations. In the case of a credit event, the protection seller either purchases the underlying bond at par, or cash settles the difference.
The flexibility of CDSs makes them an invaluable instrument as an arbitrage, speculation or hedging product.
According to a recent British Banker Association survey of its members, credit derivatives are expected to hit $5 trillion by the year 2004. Hedge funds are becoming a significant force in this trend and are expected to account for 13% of the total credit derivatives market.
It is easy to see why hedge funds have gravitated to credit derivatives. The flexibility of trading over-the-counter contracts that mirrors the more rigid cash bond market instruments provides numerous benefits.
Using a portfolio of credit default derivatives, firms can customise a desired credit risk profile based on specific reference entities and maturities not available in cash markets. Such portfolios can be created to exploit credit arbitrage opportunities within the credit market.
For various financial institutions, CDSs provide efficient means of diversifying credit risks to reduce regulatory capital requirements.
Furthermore, CDSs are non-funded transactions, enabling firms to leverage economic capital. Finally, the instruments' end users avoid issues of liquidity and funding/borrowing costs characteristic of the cash repo market.
Building on credit default swaps
CDSs can be used to build composite portfolio instruments with varying risk and return profiles based on a number of reference entities.
The transaction can be an outright portfolio or basket CDS, or a credit-linked note featuring an embedded CDS or basket CDS. In a basket default swap, protection sellers can enter into a transaction where payment is triggered by a 'first-to-default' or 'nth to default' security within the underlying basket of reference entities.
Typically, sellers of such baskets receive a higher spread than that for an individual name in the basket. Basket products enable buyers to partially hedge a portfolio against default, without having to buy single name CDSs for every underlying name.
As underlying credit names' liquidity continues improving and the vanilla market starts maturing for some names, we are starting to see growth in popularity of second generation credit derivatives such as credit default digitals, options on credit default swaps and credit default swaps that can be cancelled.
These options on CDSs are attractive for offering increased choice and flexibility in managing credit risks, while the default digital contracts have a predetermined (percentage of trade notional) payout unlike the recovery rate dependent payout of vanilla CDSs.
This evolution has been complemented with the introduction and growth of credit indices, and swaps and options on these indices. These indices are based upon a standardised portfolio of credits, like Morgan Stanley and JP Morgan's joint TRACX CDS index.
These are expected to pave the way to volatility trading in CDSs, as well as a slew of new options that provide customised risk/reward profiles similar to a particular tranche of a CDOs.
CDS swaps have also contributed to development of the CDO market, specifically synthetic CDOs. In a conventional CDO, asset-backed products are issued through tranches backed by a portfolio of loans or corporate/sovereign debts.
In synthetic structures, the actual portfolio of debts is replaced synthetically, by selling credit protection (that is, single name CDSs) and buying highly rated government or investment grade bonds.
Investors in synthetic CDOs receive the premium from the default swap and returns from high-grade debt. Hedge funds play a significant role of selling protection into this market.
While first generation credit derivatives primarily involved credit risk transfer and credit risk management, second generation credit derivatives have facilitated convergence between the derivatives and securitisation markets.
Those experienced with CDSs are moving into basket CDSs and are also looking at correlation plays with, for example, single-tranche CDOs and first-loss baskets, which mimic the loss characteristics of the equity tranche of CDOs.
Challenges to the industry
The nascent credit derivative market faces many challenges. Aside from expected counterparty risk and liquidity issues, lack of market data, standardisation of document and pricing flexibility continue to slow the product's growth.
As the credit derivatives market matures, pricing and calibration techniques are becoming more advanced and standardised. However, the problem of data availability and quality remains an issue with more illiquid reference assets or names.
Under ideal conditions, firms would price a credit derivative from a name-specific credit curve. In the absence of reliable market data, generic credit curves (for example, AA utilities) can be used.
However, whatever the approach, curves are generally constructed using available market information, whether term structure of spreads for a generic curve, or one-year, two-year, five-year market-quoted CDS rates for a specific name.
After the curve of benchmark securities is constructed, the credit derivative pricing model must be calibrated to the market which, assuming deterministic recovery rates, generates hazard rates.
Calibration flexibility is key, especially with the variability in, and often unreliable quality of, data. For example, recovery rate assumptions are generally based around historically observed recoveries by sector and seniority provided by rating agencies.
These recovery rates are a particularly unreliable input into the calibration framework and, as such, the ability to stress these assumptions and see their effect on model risk is crucial. This can be seen in graph one on page 28, which shows the results of calibrating to a credit spread curve while making different recovery rate assumptions.
One serious issue that continues to impede greater adoption worldwide is the lack of quality market data and hence potential inconsistencies in valuation and risk management. As noted above, accurate pricing relies heavily on the ability to calibrate to market-observed rates.
Yet, market data is so scarce on some names it is critical to adopt a technological framework enabling the capture of available data to model interpolated data points while stress testing all the assumptions inherent to the credit market.
Until sources of market data become more standardised and widely available, users should select a system offering flexible calibration tools to imply default probabilities from traded debt or CDS prices.
At a minimum, users need the option of entering their own curve data for pricing.
Moreover, while single instrument valuation is an important issue, users must be able to co-manage interest and credit risk at portfolio level across different assets, liabilities and derivatives.
With such a demanding and complex market, it is critical that organisations have the ability to generate proprietary credit reports and risk analysis to manipulate and represent key data as well as apply changing credit analytics.
These gaps are being addressed by new providers, many from broker-dealers with years of compiled data from their own business, or service bureaux in the business of collating reference data.
The most well known credit derivatives inter-dealer brokers offering data services are Creditex and CreditTrade. Their data consists of quotes and actual transaction prices coming directly off their broking desks.
One recent entrant to this market is Mark-It Partners, which has as its sponsors 11 banks including Deutsche Bank, Goldman Sachs, Lehman Brothers, Merrill Lynch, Morgan Stanley, ABN Amro, Bank of America, Salomon Smith Barney, CSFB, Dresdner Kleinwort Wasserstein and TD Securities.
Mark-it's pricing service offers an alternative to the credit derivatives data services provided by interdealer brokers. Although brokers provide daily credit derivatives prices, Mark-It's offering differentiates itself in part by offering both derivative and cash prices
Definition of credit events and reference assets
When underlying bond issuers default on their obligations, CDS buyers must deliver the debt and receive the agreed reference price (typically par value).
While straightforward in concept, the exact reference asset is not always clearcut and in recent cases, investors have mistakenly bought protection for a wrong, but related, entity including subsidiaries of the bond-issuer.
One important project is working towards a single reference entity database (RED Project), which has been built to establish a comprehensive list of all the relevant reference entities in the credit derivatives market.
The group of banks that has developed this is selling the database to make it available to the entire market, and it Mark-It have now announced they will launch RED.
RED is designed to eliminate a mismatch in credit default contracts resulting from dealers on either side of the trade using slightly different reference entities.
This mismatch is another reason why standardisation of documentation is paramount to the market's long-term success.
Another issue that complicated credit derivatives transactions in the past has been the definition of a credit event, specifically restructuring.
The 1999 ISDA derivatives documentation identifies restructuring as a credit event, yet inadequately covers cases of restructuring.
The recently adopted 2003 ISDA definition for credit derivatives amends the 2001 modified restructuring to provide for four types of restructuring treatments: modified modified restructuring, modified restructuring, old restructuring, no restructuring. In the US, the preferred treatment is the modified restructuring which reduces the maturity of the obligations that can be delivered following a restructuring credit event, and limits the cheapest-to-deliver option.
The table below shows the standardisation that is present in the main geographical areas.
Market standard credit events
Jurisdiction Restructuring Guarantees
US Modified Qualifying affiliates
European Modified modified All
Asian Old All
Obligation acceleration and repudiation/moratorium are no longer used as standard. Reportedly, the 2003 ISDA definitions smoothly took effect on June 20, 2003, although there have been some discussions about whether the European adopted modified modified restructuring will work for credit default swaps on subordinated insurance companies.
Already, dealers are looking for arbitrage opportunities on single name CDSs with and without restructuring provisions.
In particular, the current market spread for contracts with restructuring is about 5%-10%; however, some dealers value it somewhere between 2%-5%.
Devil in the detail
From a systems perspective, the trading of credit derivatives introduces new issues in data management, risk analysis and operational processes beyond those that current fixed income/derivatives platforms can handle.
Accurate pricing and consistent risk management of these instruments requires the ability to capture all pertinent information to a credit derivative, for example, varying credit events, ratings history and contract details.
More basic parameters that affect valuation include payment schedules and delivery of the asset. Does the instrument pay monthly or quarterly on the 20th of each month (as per latest standardisation)?
Is accrued interest included upon default? Are there any delays in recovery rate payments, for example 30 days, after default?
The selected trading/portfolio management system must factor in all these effects in pricing and cashflow analysis/projections.
The system should also scale or evolve with new trends. For example, the alternative credit events definitions has made it a requirement to be able to analyse and price the difference between two CDSs having the same contractual terms apart from one having restructuring as a credit event and the other without.
An open analytical framework that permits entry of complete deal data is also essential for sound risk management practice.
Users may wish to apply shocks on instruments or portfolio levels to observe effects on recovery rate, default probabilities, credit volatilities, asset correlations and market value sensitivities.
In the second graph to the left, one can see what the effects of shocking the default probabilities and recovery rates are on the CDS premium.
The digital CDS is much more sensitive to recovery rate shifts that the vanilla CDS. This is due to the close relationship between the credit curve spread and the CDS premium, and the independence of the digital CDS payout to recovery rates.
The higher the recovery rate, the higher the calibrated default probabilities, and hence the higher the value of the fixed digital payout.
For vanilla CDSs, the higher default probabilities are offset by the resultant lower CDS payout (100-Recovery).
As a financial instrument, CDSs are typically managed by derivatives or the fixed income desks and require cross-asset risk and operational processing.
Regarding risk management, these instruments must be analysed using the same core valuation and market environment to ensure that model noise ' differences in valuation from different models ' is mitigated from the portfolio and that all risks are incorporated and managed, that is, interest rate risk.
Moving towards more complicated structures, it becomes increasingly important to know and manage all risks, with existing and potential deals. The two graphs fourth and fifth from the top, provide examples of correlation sensitivity. The fourth from top shows the sensitivity to the basket credit default swap premium of a first, second, third and fourth to-default 5 reference asset basket CDSs through changes in the correlation.
Correlation within these basket structures is critical in valuation and risk attributes, as is sensitivity to changes in credits of underlying reference assets. This is particularly important as basket CDSs cannot be replicated in the cash market, so require dynamic hedging in either the cash or vanilla credit instruments.
One can see that as correlation approaches 100% of all the basket CDS premium approach the same level, which is the most expensive single name premium in the basket. This level also provides a lower bound on the first-to-default basket premium.
This effect is illustrated differently in the bottom graph, where for 100% correlations, the basket premium for a first-to-default basket remains unchanged however many reference assets in the basket portfolio.
The bottom graph also shows the effect of correlation on first-to-default baskets as the number is increased of underlying reference assets.
New instruments ' new entities
Competitive pressures and challenging economic conditions have driven hedge funds to adopt strategies to aggressively pursue market arbitrage and more effective hedge opportunities, resulting in new credit hedge funds trading credit spreads and credit derivatives.
These products enable investors to take on the unique risk/reward exposure of liquid and illiquid fixed income assets without tacking on the assets to the balance sheet.
Through synthetic CDOs, credit derivatives have, in part, driven the growth in the securitization market.
Structured investment vehicles (SIVs) are also looking to incorporate trading of credit derivatives into their operations manual.
SIVs are bankruptcy-remote entities set up to issue rated notes and commercial paper backed by high quality assets.
Through active portfolio management, diversification and over-collateralisation, SIV issuances receive strong ratings from the agencies.
These vehicles, which generate returns by managing the asset/liability gaps between long-dated assets purchased and the short-term instruments they issue, see credit derivatives as a potential unfunded asset that can produce greater spreads than the funded underlying referenced assets.
Credit hedge funds are also are also borrowing from many of the SIV's technology and risk management capabilities without leverage constraints.
The applications of credit derivatives are broad, as is the list of participants in the arena which include all major financial institutions including insurers, reinsurers, CDOs, hedge funds, banks, broker-dealers, corporates and boutiques specializing in the credit trade.
Understanding how to price and utilise the credit default swaps is fundamental to implementing more advanced strategies that can produce greater yields or protection.
Credit derivatives consumption will continue to evolve and fundamentally change the way financial institutions manage risk.
For single name credit derivatives, the market has been converging towards an intensity-based model, at least for pricing credit derivatives. With this approach, one models the hazard rate of default, which is effectively the instantaneous forward default probability. These models are perametised by a term structure of default probabilities ' hazard rates ' and recovery rate assumptions.
European options on CDSs are also priced using the analogous Black's model for interest rate swaptions, in that the par credit default swap premium is assumed to be lognormally distributed.
For basket credit derivatives this approach is extended to model the correlated multiple defaults of the underlying reference assets. This is generally achieved using a multi-factor Monte Carlo simulation of the default time, using the Normal Copula function model for correlation.
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