Do Rating Announcements convey new Information?

An Event Study on Credit Default Swap Spreads

Diploma Thesis, 2010

54 Pages, Grade: 1,3


Table of Contents

List of Tables

List of Figures

1 Introduction

2 Rating Agencies and the Market for Credit Risk

3 Previous Literature and Hypotheses

4 Data
4.1 Ratings
4.2 Credit Default Swap Spreads

5 Methodology
5.1 Framework
5.2 Hypothesis Testing

6 Empirical Results
6.1 Event Studies
6.2 Regressions
6.3 Discussion

7 Conclusion

8 References

9 Appendix: Robustness Test

List of Tables

1 Classification of rating classes from Standard & Poor’s

2 Default frequencies from S&P

3 Distribution of ratings

4 Descriptive statistics

5 Boehmer test statistic for downgrades

6 Sign test for downgrades

7 Boehmer test statistic for upgrades

8 Sign test for upgrades

9 T-test for asymmetric reaction

10 Regression results for downgrades and upgrades

11 Regression results for absolute spread changes

12 Results for downgrades with interpolated data

13 Results for upgrades with interpolated data

14 Regression results with interpolated data

List of Figures

1 Volume outstanding in the global CDS market

2 Variation in CDS spreads

3 CDS Index

4 CASC downgrades

5 CASC upgrades

1 Introduction

Since the beginning of the last century, investors in capital markets have strongly relied on rating agencies’ assessments of credit quality to decide on investments. Due to their important role in debt markets, they are supposed to provide ac- curate ratings without delay. However, cases like the defaults of WorldCom or Enron have damaged their reputation. In particular, credit rating agencies have been heavily criticized for their role during the financial crisis of 2007-2009. Many economists blame the rating agencies for having played a major part in the se- curitization process of mortgage loans by providing too high rating grades; and thus sowing the seeds of the crisis. Having rated credit derivatives like collateral- ized debt obligations with best grades, the rating agencies encouraged banks and other financial institutions to keep these assets in their portfolios. As a result, it caused severe problems for the banking sector when these products heavily lost in value. Along with imprecise assessments of creditworthiness, the slow reaction of rating agencies has been critizised over the last few years.

Therefore, the question of how well the agencies assess credit quality arises. This question is of great importance because of their dominant role on capital markets and the fact that decisions are made upon their ratings. To put it more precisely, this study asks whether the agencies process and convey new information to the market. On the other hand, it might be the case that market participants anticipate any change in the credit quality of a company before these institutions publish their assessments. Answering this question is of particular importance: if the rating announcements convey unknown information and the market reacts, then rating agencies are a systemic part of capital markets and policy should consider stricter regulation to prevent manipulation and failures like those described above. Conversely, if their announcements do not contain any new information - or to put it differently, if markets react faster - then we could think about using market based indicators instead in order to assess credit risk. In this case, the economic task of signaling creditworthiness could be handed over, among others, to Credit Default Swaps (see Chapter 2), which is also suggested by Hart & Zingales (2009).

This thesis contributes to the field of rating agencies’ performance measure- ment. Evaluating their announcements with the help of the Credit Default Swap (CDS) market, I examine the information content of their ratings. At the same time, this study can be seen as part of the market efficiency research on the CDS market.

This empirical analysis shows that there are only weak effects of rating an- nouncements, which might indicate that rating changes is no news to the markets. By observing the CDS market, the empirical evidence displays some anticipation of rating changes, especially for downgrades, such that markets seem to react faster. Moreover, there seems to be some counter-movement to the prior adjust- ment after the rating was announced. Finally, I can detect both an asymmetric reaction between upgrades and downgrades, which means that latter ones have a stronger impact on the market, and a higher sensitivity in poorer rating classes.

In order to give an answer to the question in the title, the thesis proceeds in the following way: The next section provides background information on rating agencies and CDS to build up the theoretical foundation of this study. The previous research and the working hypotheses are presented thereafter in section 3. The subsequent section introduces the data and its transformations. In order to perform the analyses, section 5 addresses the methodology of the event study. The empirical results are then stated in section 6 and finally, section 7 concludes.

2 Rating Agencies and the Market for Credit Risk

The U.S. Securities and Exchange Commission (SEC) defines a credit rating agency as “a firm that provides its opinion on the creditworthiness of an entity and the financial obligations (such as, bonds, preferred stock, and commercial paper) issued by an entity.”1 These agencies play an important economic role for capital markets as they assess the credit risk of companies or states such that private and institutional investors can use these assessments as basis for their in- vestment decisions. In this context, credit risk is regarded as an exposure to the losses arising from the borrower’s default. Collecting data about these entities, the agencies publish ratings which help to reduce the information asymmetry be- tween potential borrowers and potential lenders. This is for the benefit of both parties: Potential borrowers, for instance, gain additional information about the creditworthiness of the lender which allows better decision making with respect to credit risk and avoid monitoring costs. At the same time, lenders profit from the positive signaling effect of a rating which can reduce financing costs. As a result, the majority of institutions that borrow from the capital markets are willing to pay for this service.

Credit ratings also play an important role in financial regulation. For ex- ample, money market funds in the United States are restricted to invest in high quality short term instruments only. The criteria set by the Investment Com- pany Act to decide on the quality are rating agencies’ assessments of credit risk. In banking regulation, which was designed by the Basel Committee on Banking Supervision, the ratings are used for the calculation of regulatory capital.2

The credit rating industry arose in the early 1900’s in the U.S. and by the year 2000, the Bank for International Settlements (BIS) counted around 150 agencies worldwide (BIS 2000, p.14). Currently, there are three big global rating agencies, which are situated in the U.S.: Standard & Poor’s Credit Market Services (S&P), Moody’s Investors Service and Fitch, Inc. According to the SEC, they control 98 percent of the market for debt ratings in the U.S. and dominate the global credit risk assessment.3 Looking at the revenues or the number of employees in the year 2008, S&P is the largest one.4 The agencies’ role in global capital markets has expanded in the last couple of years due to the growing number of companies issuing securities. In order to successfully raise funds they are required to be rated by one of the major rating agencies. The reason is that institutional investors, for instance, are restricted to hold highly rated assets. Usually, the issuing company itself pays for this rating service.

In general, credit ratings are considered as “forward-looking opinions about the creditworthiness of issuers and obligations”.5 They represent a relative rank- ing of creditworthiness rather than the issuer’s absolute probability of default. Nevertheless, as Standard & Poor’s claims, the “likelihood of default - encom- passing both capacity and willingness to pay - is the single most important factor in our assessment of the creditworthiness of an issuer or an obligation”. Other such factors that might enter the rating are, for example, payment priority of the debt instrument, recovery in case of default, and credit stability. Generally, there are two ways to determine the rating: First, there are qualitative methods, in which experienced analysts assess the creditworthiness of an entity. Alterna- tively, there is the quantitative approach with the help of statistical models. The three principal rating agencies include a combination of qualitative and quanti- tative judgements.

Normally, rating agencies do not fully publish key bases and assumptions underlying their ratings as the models they use are confidential. Furthermore, on the basis of the SEC’s Regulation Fair Disclosure (Reg FD), which prohibits selective disclosure of non-public information, they exclusively gain access to con- fidential information from the issuer. On the basis of this unique access they have a better insight of the company which they rate. This might give clues related to the information content of ratings from rating agencies depending on whether

illustration not visible in this excerpt

Table 1: Classification of rating classes from Standard & Poor’s they use this private information efficiently.

The three principal rating agencies use an alphabetical scale to rank the en- tities, which reaches from “AAA” to “D” for S&P (see Table 1). The intention is to stress the ordinality and avoid the connotations of cardinality that might be immediately associated with a numerical rating scale (BIS 2000, p.15). Addition- ally, they use “+” and “-” signs to show relative standing within the major rating categories from “AA” to “CCC”. As such, the distance for example between “AA” and “AA-” is called one “notch”.

Once an initial rating is attached to an entity, the rating agencies normally continue monitoring the issuer and might announce a rating change, i.e. an upgrade or a downgrade, if a change in credit quality has occurred. The rating itself consists of the actual grade on the alphabetical scale, which is frequently accompanied by an outlook that is either positive, negative or stable. These outlooks represent the potential for a rating change and its direction over the intermediate term (typically six months to two years). In addition, Standard & Poor’s place ratings on the watch list, which they call “Credit Watch”, as reaction to events that affect the credit risk of the rated entity but whose extent cannot be assessed yet. Thereby, they gain additional time to collect information and evaluate the effect on the credit quality. However, Standard & Poor’s state that they complete their analysis of the magnitude of the rating impact normally within 90 days. Thus, within this period of time, the Credit Watch listing might lead to an actual upgrade or downgrade.

Efficient Markets Hypothesis

For the following analyses, the concept of efficient markets is fundamental. Al- though the theoretical foundations of information-efficient markets can be traced back to the beginning of the twentieth century, the Efficient Markets Hypothesis (EMH) was developed by Eugene Fama in the early 1960s. In his study concern- ing efficient capital markets from 1970 he states: “A market in which prices always ‘fully reflect’ available information is called ‘efficient’.” Thus, the simple definition of an information-efficient market is that all known information is contained in the prices. This implies that prices in efficient markets should instantaneously react to the release of previously unknown and relevant information. For this relation to be valid, there have to be rational agents and a frictionless market without disturbing transaction costs.

In an efficient market the release of former publicly unknown information should lead to an immediate abnormal reaction in the prices. Consequently, the announcements of ratings, among others, should yield an abnormal reaction on an efficient market dealing with credit risk if they convey new information. This implies that if no reaction can be observed on the particular market, credit rating announcements cannot contain significant information that is new to the market. Therefore, this relation can be used to investigate the information content of ratings, i.e. whether there is an unexpected part which is new to the public. As such, this requires that there is no significant insider trading, for example from rating agencies that anticipate the announcements. In the next step, an appropriate market has to be determined, which trades credit risks and can be assumed to be information-efficient. As we will see in the following section, the Credit Default Swap market suits both conditions and is dominated by credit risk.

illustration not visible in this excerpt

Figure 1: Volume outstanding in the global CDS market Credit Default Swaps

Aside from the bond market, credit risk is traded with credit derivatives, especially with Credit Default Swaps (CDS). This is a type of insurance contract that is traded over-the-counter, which means that there is no formal exchange up until now.6 They were developed in the mid nineties, and turned into the most popular credit derivative with markedly growing volumes (see Figure 1). There are two parties who enter this contract. On the one hand, there is the protection buyer who wishes to buy insurance against the possible default of a reference entity. This entity might be a corporation or a sovereign which the protection buyer has financial claims against. The underlying that the buyer intends to insure with a CDS contract is called reference obligation (for example a bond). However, the contract is based on the reference entity itself. The protection seller represents the counterparty that acts as the insurer. Usually, these are financial institutions, such as banks and insurances or hedge funds, which take the risk that the underlying entity can default. If there is no default, then the contract lasts until maturity as the parties previously agreed upon.

This can be any time between one month and ten years; however, the most common length is five years. In case of default, the contract terminates earlier and the seller must meet the obligations fixed in the contract. This is called credit event and it generally includes bankruptcy, failure to pay or restructuring of the reference entity. Depending on the arrangements, there are two ways for the protection seller to pay out the buyer. The buyer might keep the reference obligation and receive the difference between the notational and the market value, which is called “cash settlement”. Alternatively, he can deliver the underlying to the seller who pays the full notational amount, which is a “physical settlement”. As compensation for the protection, the seller receives an insurance premium from the buyer which is called the credit default swap spread: it is the annualized quote stated in basis points of the notational amount of the contract. Often it is referred to as the price or premium of a CDS. Usually, the payments occur on a quarterly frequency with fixed payment days that have evolved as March, June, September and December 20th. For example a spread of 100 means that to insure bonds with a nominal value of $1000 against default, one has to pay one percent of the amount, which is $10 per year or $2.5 each quarter. In return, one obtains the right to sell the bonds at the face value of $1000 in case of a credit event. If default occurs between two payment dates, the buyer must pay the seller the part of the premium that has accrued since the most recent payment. At origination the annual premium of a standard CDS contract is determined such that its market value is zero.

The CDS contract originated as a legal and individual contract between two parties. As legal problems came up with the exact definition of credit events, the need for standardization became obvious. Especially the International Swaps and Derivatives Association (ISDA) responded with guidelines for CDS contracts in 1999 and a revised version in 2003. They are now included in most CDS contracts and clarify the credit event. The standardization enabled the rapid growth of this market and made CDS utilizable for research purposes.

So why are CDS adequate to examine the information content of ratings? Firstly, credit risk is the dominant factor that affects CDS spreads. Both CDS and ratings refer to the reference entity. Per construction these derivatives are strongly linked to the probability of default of the reference entity. The following simple, one-period and risk-neutral pricing model derived from an actuarially fair perspective illustrates this link.7 Starting from an actuarially fair insurance, which does not make any profit on average, the following condition holds:

illustration not visible in this excerpt

In a world of perfect competition the expected income is of same value as the expected losses due to competitors entering the insurance market. The income for the protection seller, i.e. the annual premium, equals the CDS spread observed in the market times the notational amount that is insured. His expected losses depend on the probability of default p. If no default occurs within that year, which happens with the probability of 1 − p, then there are no expenses for the seller. In the case of default, however, the loss equals the difference between the insured value and the amount that can be recovered. Assuming risk neutrality, the expected values can thus be written as:

illustration not visible in this excerpt

where S is the CDS spread, F the insured face value and R the recovery value. If we express it independently of the insured amount F , then we get

illustration not visible in this excerpt

where r is the recovery rate. As this rate stays fairly stable for one entity, credit risk is the single most important factor determining CDS spreads. Nevertheless, both illiquidity and a premium for the counterparty risk - especially after the collapse of Lehman Brothers - are two determinants that have an influence on the spreads as well. However, the latter one can be minimized with the help of a daily settlement arrangement. The relation above shows that CDS theoretically depend on the probability of the reference entity defaulting and the fraction of the promised payments that the lenders are able to recover. These are entity specific parameters which are unobservable and difficult to estimate.

Compared to the bond market where credit spreads can be calculated from

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Figure 2: Variation in CDS spreads

This boxplot illustrates the variation in CDS spread levels per rating class. The plotted values are the mean spreads over 50 days for the CDS entities in the particular classes. The horizontal line within the boxes represents the median value while the upper (lower) end of the box is the 75% (25%) quantile.

the bond yields, the CDS market is preferable for this study for mainly two reasons: The CDS market is considered more liquid than the bond market as this derivative represents a short position which cannot easily be obtained on the bond market. Furthermore, the credit spreads have to be calculated using assumptions about the risk free interest rate. As CDS separate credit risk from other risks concerning interest rates or exchange rates, the premium closely reflect the risk of a credit event. Hence, the CDS premiums are the cleanest available measure of the spread which investors require to bear credit risk.

The second advantage of the CDS market in this context is that there are good reasons to assume this market to be information-efficient. In particular, compared to other financial markets as stock or bond markets, there are mainly professional and well informed agents as banks and insurances trading CDS; therefore, Norden & Weber (2004, p.6) argue that there are less noise traders. Moreover, Zhu (2004) and Blanco et al. (2005) show that the CDS market leads other markets in terms of information discovery. In particular, the CDS market appears to move ahead of the bond market in price discovery with respect to changes in credit risk. Due to the growth in volumes and importance of the CDS market over the last few years (see Figure 1) the quality of the spread data improved. Using an arbitrage approach, Cserna et al. (2008) find that the CDS market became efficient around 2005. At the same time, CDS Index trading arose and lead to higher liquidity. Thus, the CDS market can be regarded as efficient in terms of public information, i.e. spreads reflect all publicly known information. As the graph above illustrates (see Figure 2), the CDS spread is negatively related to its credit rating. Therefore, the lower the credit rating the higher the observed CDS spread tends to be. This is reasonable as the credit rating reflects the probability of default, which is priced with these derivatives. However, there is still some variation for the spreads of entities within a given rating. This might be due to specific characteristics of the contract (for example liquidity) or the entity (for example the type of industry). Due to the variation between the classes the following analysis takes account of the different classes.


1 United States Securities and Exchange Commission (SEC), 2005: Credit Rating Agencies - NRSROs. Modified 09/25/2008. Accessed at in January, 2010.

2 Basel Committee on Banking Supervision, 1999: A New Capital Adequacy Framework.

3 Statement at SEC Open Meeting by Commissioner Kathleen L. Casey. U.S. Securities and Exchange Commission Washington, D.C. September 17, 2009.

4 revenues 2008: S&P $2654 million, Moody’s $1205 million, Fitch $727 million; employees 2008: S&P 8500, Moody’s 3000, Fitch 2300.

5 Standard & Poor’s, 2009: Understanding Standard & Poor’s Rating Definitions. Accessed at in January, 2010.

6 However, as reaction to the financial distress of AIG, a clearing house was established in the U.S. and in Europe during 2009.

7 In order to extend the model to account for more periods, the present values of the expected premium payments and expected losses have to be considered.

Excerpt out of 54 pages


Do Rating Announcements convey new Information?
An Event Study on Credit Default Swap Spreads
University of Tubingen
Catalog Number
ISBN (eBook)
ISBN (Book)
File size
639 KB
empirische Arbeit
Ratings, CDS, Event Study, Rating Agency, Credit Default Swap, Rating, Credit Rating, Kreditbewertung, Kreditrisiko, Creditrisk, credit risk, Ratingagentur, Event Studie, Kreditderivate
Quote paper
Jan Klobucnik (Author), 2010, Do Rating Announcements convey new Information?, Munich, GRIN Verlag,


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