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Nova School of Business and Economics Universidade Nova de Lisboa
Dissertation, presented as part of the requirement for the degree of Doctor of Philosophy in Economics
Essays on the Impacts of Credit Ratings
Mário Henrique Machado Meira, Student Number 676
A Dissertation carried out on the PhD in Economics, under the supervision of Professor Miguel Ferreira.
July 31, 2019
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Abstract
This thesis studies the impacts of credit ratings in several dimensions. The firs paper studies the impact of the sovereign ceiling policies in the Municipal Bond markets. Firms at the sovereign ceiling are relatively more affected following a sovereign downgrade.
The second paper quantifies the impact of stringent rating criteria from credit rating agencies in investment grade and speculative grade firms. The former is significantly more affected by changes in criteria. The third paper access the impact in firm’s leverage, debt issuance and cash holdings from past changes in credit ratings. Unpredicted changes in credit ratings drive most firm’s reactions.
Keywords: Rating Agency; Capital Structure; Bond Market; Event Study.
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Acknowledgments
The PhD program was one of the toughest challenges that I faced on my professional life. Through all the process I received lots of encouragement from my family and friends, and, without a doubt, it would be impossible for me to finish this challenge without them.
To my lovely wife Andreia I want to acknowledge the highest support, encouragement and help throughout this whole process. You were my main drive and without you I would not make it this far. From the bottom of my heart I thank you for everything.
To all my family I thank you for the unwavering support. Mainly to my father António Meira, my mother Paula Meira, my aunt Ana Meira and my uncle Pedro Barros I thank you for pushing me forward.
To my supervisor Miguel Ferreira and all the staff from Nova SBE I thank you for the opportunity to be part of this program and to give me the push forward that I needed to close this chapter of my life.
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Contents
1. The Financial Effects of the Sovereign Ceiling Rule………....6
1.1. Introduction.………...…6
1.2. Data and Sample Description……….………..13
1.2.1. Rating Methodology………..13
1.2.2. Secondary Market………...14
1.2.3. Primary Market………..…...16
1.2.4. Identification Strategy………..…….17
1.3. Evidence From the Secondary Market………...……..19
1.3.1. Ex – Post Returns………..………20
1.3.2. Cost of Debt………...…………22
1.3.3. Impact on Trade Volume………...………26
1.4. Evidence From New Issues………...………...28
1.4.1. Cost of Debt………..………29
1.4.2. Impact on the Amounts Issued……….……..………...…32
1.5. Conclusion………...34
1.6. References....………...………36
1.7. Appendix………...………..…39
1.8. Internet Appendix………65
2. Are Good Firms More Affected by the Tightening of Rating Criteria from Credit Rating Agencies?...85
2.1. Introduction………..…85
2.2. Data and Sample Description………...90
2.3. Rating Models………..93
2.4. Robustness Tests………..97
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2.5. Implications From Tighten Rating Criteria………..99
2.5.1. Implications for Debt Issuances and Leverage Levels……….101
2.5.2. Implications for Cash Holdings………...103
2.6. Conclusion………..104
2.7. References……….……….106
2.8. Appendix….………...108
3. Do Changes in Credit Rating Truly Impact Leverage Decisions?...135
3.1. Introduction………....135
3.2. Empirical Design………139
3.3. Data and Sample Description……….142
3.4. Base Model………144
3.5. Predicting Changes in Credit Ratings………148
3.6. Impact of Predicted and Unpredicted Changes in Credit Ratings…………..150
3.7. Robustness Tests………....153
3.7.1. Tests by Credit Rating Category……….153
3.7.2. Tests for Different Years……….155
3.7.3. Tests for Different Predicted Credit Ratings………...156
3.8. Conclusion……….157
3.9. References……….159
3.10. Appendix……...………...……161
3.11. Internet Appendix………...……...………..194
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Chapter 1
“The Financial Effects of the Sovereign Ceiling Rule”
Abstract
I study the financial effects of the 2011 U.S. sovereign downgrade in the municipal bond market due to the sovereign ceiling rule. I find an asymmetric impact between issuers that are able to issue bonds with same rating as the sovereign (treated issuers) and other issuers (control issuers). The asymmetric shock leads to a relative increase in cost of debt for treated issuers, both in the primary and secondary markets, after the sovereign downgrade. Consistent with the increase in the cost of debt there is a relative contraction in debt supply to treated issuers. This effect is likely to be driven by sovereign ceiling- related downgrades due to direct adjustments from credit rating agencies and from the investors’ expectations regarding the downgrade likelihood of treated issuers.
1.1. Introduction
The Municipal Bond markets are relevant and sizable markets reaching
$3.71 trillion in outstanding municipal securities, as reported by the Federal Reserve by the end of the 2012. These are relatively opaque markets on which Credit Rating Agencies (CRAs) represent an important role as screening agents, mainly due to the scarcity of historical data, a strong illiquidity factor and a small number of empirical studies. Problems may arise when investors rely too much on the credit ratings and do not access and analyze the issuers’ fundamentals.1 In August 5th 2011 Standard & Poor’s
1 Janney Capital Markets report “Municipal Bond Market Monthly” analyzes the historical overreliance of investors on credit ratings in the muni markets. Please see the full article at http://www.janney.com/File%20Library/Fixed%20Income/Janney-MBMM-July- 2014_final.pdf.
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(S&P) downgraded, for the first time in history, the sovereign credit rating for the United States of America from AAA to AA+. In the following days, August 11th 2011, S&P announced that it would downgrade more than 11,000 municipal bonds that had a credit rating equal to the sovereign in order to reflect the change in the federal debt rating. S&P do not strictly apply the sovereign ceiling rule since 1997, when it allowed for four Argentinian firms to be rated above the sovereign, and justified this adjustment with specific links between certain municipal bonds and the federal government.2
The goal of this paper is to study the impact of the 2011 United States sovereign downgrade in the Municipal Bonds markets by using the sovereign ceiling rule as the channel for the shock. The identification strategy is close to one proposed by Almeida et al. (2014) on which changes in issuers’ credit ratings due to sovereign ceiling policies generates an asymmetric effect on issuers with credit rating at the sovereign ceiling. The identification strategy focuses on the issuers’ ability to issue new bonds with certain ratings rather than changes in credit ratings of outstanding issues. I find that municipal issuers who were able to issue AAA bonds prior to the sovereign (treated issuers) are at least 1.4 times more likely to be downgraded after the sovereign downgrade than other municipal issuers (control issuers).3 The reason for the S&P’s downgrades of several outstanding issues was specific links between those bonds to the federal government and not a direct change in those issuers’ fundamentals. Therefore, there would be no reason for issuers that were able to issue AAA bonds without links to the federal government and without any type of insurance (the bond risk is the issuer’s default risk) would have more difficulty in making new issues with the previous credit rating after the sovereign downgrade. The more reasonable explanation is that CRAs still follow the sovereign
2The main links that justified the municipal bonds downgrades include: pre-refunded bonds backed by escrowed Treasury notes, public financing housing authority, and others. Please see the full article at:
www.wsj.com/articles/SB10001424053111904140604576498544285815056.
3 A given issuer is considered downgraded if the credit rating of the last issue made prior to the sovereign downgrade is higher than the credit rating of first issue made afterwards.
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ceiling rule, or at least investors’ expect them to follow and act according to such an expectation.4
I argue that changes in fundamentals are not able to explain the asymmetric shock between the treatment and control groups. First, a key advantage of this identification is the inherent higher credit quality of the treatment group; meaning that in the event of a systematic shock treated issuers should be relatively less affected. Second, by accounting for several issuer/issue specific characteristics spillover effects from relationships with the federal government are taken into account and thus would render no asymmetric shock due to such links. Third, I consider the possibility of a non-linear relationship between ratings and the issuers’ default probabilities to be the reason for an asymmetric shock on treated issuers. However, according to S&P credit rating’s methodology for municipalities this should not be the case. I quantify the impact from this event through a difference-in-differences setting, by estimating the change in the cost of debt, trade volumes and amounts issued of treated issuers relative to control issuers, while controlling for a host of issue and issuer specific characteristics.5
I find that ex-post returns of outstanding muni bonds of control issuers increase significantly more than treated bonds, thus penalizing treated issuers. The differential return between an equally weighted portfolio of treated issuers and an equally weighted portfolio of control issuers decreases between 93 basis points and 152 basis points for different time windows, up to 3-months around the sovereign downgrade. The results are statistically and economically significant for all the time windows considered. The results show, in an ex-post basis, that outstanding bonds from treated issuers are relatively more
4From S&P “Credit FAQ: Understanding Ratings Above The Sovereign (August 8th 2011)” it is mentioned that the sovereign ceiling is not a rule. However, entities with high exposure to sovereign securities, such as banks and insurance companies, are unlike to have a rating above the sovereign.
5For the bond characteristics are considered coupon, maturity, bond type and tax exemption regime. For the issuer characteristics are considered the issuer liquidity, size and state. The variables are summarized in Table A.1 from the Internet Appendix.
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affect with progressively smaller returns than control issuers. The impact becomes stronger as the time window widens around the event.
Regarding cost of debt in the secondary market, I find the yield and spread of muni bonds from treated issuers increase relatively to control issuers following the sovereign downgrade. This effect is more pronounced as the time window widens and when conditioning the sample to contain more liquid bonds. For example, 6-months after the sovereign downgrade the yield and spread of treated issuers increase by 10 basis points and 11 basis points more than those of control issuers respectively. With the exception of outstanding bonds from issuers in the “BBB” category, the yield of treated issuers’
bonds is relatively more affected than the bonds of issuers within any investment grade credit rating category. The evidence suggests that issuers with credit rating closer to the sovereign are relatively more affected. When I instrument the change in the issuer’s credit rating by the sovereign ceiling rule I find similar results. For example, for a 6-month period after the sovereign downgrade bonds from issuers that suffered a one-notch downgrade that spurs from ceiling policies present both yield and spread higher by 15 basis points.
The amount traded of outstanding bonds from treated issuers declines between 26%
and 30% relative to control issuers after of the sovereign downgrade. A key concern regarding this result is the possibility of trades between different types of investors. From 2010 to 2012 retail investors dominated these markets with holdings ranging from 49.7%
(2010) to 44.6% (2012), followed by mutual funds with holdings between 24.2% (2010) and 26.0% (2012), and insurance companies with a constant holding of 12.2% throughout these periods.6 To account for trades between different types of investors I employ the Dick-Nielsen et al. (2012) trade segmentation: small trades – trades smaller or equal to
6 Information available: (page 95) http://www.federalreserve.gov/releases/z1/20130606/z1.pdf.
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$100,000 (usually associated to retail investors); and large trade – trades larger than
$100,000 (usually associated to institutional investors). The amount traded among small trades for treated issuers increase by 11% while it declines by 66% for large trades, in the month after the sovereign downgrade. This trade segmentation shows that the impact on trade volume dissipates after the first month post sovereign downgrade. This behavior suggests that large investors (such as pension funds and other institutional investors) tend to target specific ratings, and thus react mechanically to changes in credit ratings (also seen in Cornaggia et. al (2015)).
Finally, I show that the offer yield and spread of treated issuers in the primary markets increase relative to control issuers in the year after the event. Following the sovereign downgrade, the offer yield (spread) increases by 9 basis points (11 basis points) more for treated than for control issuers. Regarding the amount issued, treated issuers make on average new issues 56% smaller and issue a total amount 47% smaller than control issuers, in the year after the sovereign downgrade. This evidence suggests a relative contraction on debt supply for treated issuers. The results that spur from the sovereign ceiling channel are consistent with the previous results. Issuers that suffer a one-notch downgrade from ceiling policies see their offer yield (spread) increase by 16 basis points (25 basis points), while the amounts issued decreases by 57% following the sovereign downgrade.
In order to validate these results I conduct several robustness tests. First, in order to account for the inherent illiquidity in the secondary market I exclude bonds with less than 3 trades in the period pre and post sovereign downgrade. Second, to account for different credit qualities, I compare directly bonds from treated issuers with issuers within specific rating categories in the secondary market. Third, to access the investors’
expectations regarding the risk of treated issuers I condition the secondary market sample
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to only contain issuers that maintain their credit rating. All these tests provide evidence of a asymmetric negative impact in bonds from treated issuers. Finally, to account for the timing of this shock I test if the asymmetric impact on treated issuers leads or lags the sovereign downgrade in the primary market tests. I find that the impact is contemporaneous with the sovereign downgrade.
Testing the impact of changes in credit ratings of bonds in the municipal bond markets is difficult. The inherit endogeneity between changes in fundamentals and changes in credit ratings makes it difficult to access if investors rely on ratings or observe the issuers’ fundamentals. Some exceptions are Kliger and Sarig (2000) and Cornaggia et al. (2015) that were able analyze events on which credit ratings were explicitly refined/adjusted without a change in fundamentals. These studies show that changes in credit ratings can contain relevant pricing information and thus generates real effects. In a broader sense, Kisgen (2012) and Alp (2013) show that firms penalized by different rating methodologies or stricter rating criteria see an increase in the cost of debt and change their financial and investment decisions accordingly. Credit ratings are then a significant concern for financial managers, providing to the market information regarding the firm’s credit quality. Firms tend to target specific ratings, behaving differently when they are close to the target rating or on the verge of a downgrade (Kisgen (2006, 2007, 2009)). Credit ratings are also major concern when firms decide upon its capital structure (Graham and Harvey (2001)) and can affect the cost of debt capital, as shown in the case of rating-based regulations (Kisgen and Strahan (2010)). Also, when adjusting the capital structure changes in debt tend to take longer to materialize due to adjustment cost (Leary and Roberts (2005) and Lemon, Roberts and Zender (2008)). However, not all changes in credit ratings from different CRAs should be analyzed in same way, being paid CRAs
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slower to adjust and react to changes in fundamentals than non-paid CRAs (Cornaggia and Cornaggia (2013)).
Several studies show that downgrades in bond’s ratings are usually shortly anticipated with little to no market reaction following the actual change is rating (Hettenhouse and Sartoris (1976), Weinstein (1977), Pinches and Singleton (1978), Hite and Warga (1997)). This effect is particularly strong in relatively liquid markets such as industrial bonds markets, implying that changes in credit ratings do not convey meaningful information. In the existence of an impact, it is usually presented in the announcement of downgrades and presents a small to no persistence in abnormal negative returns (Holthausen and Leftwich (1986), Hand, et al (1992), Ederington and Goh (1998) and Dichev and Piotroski (2001)). When the reason for a firm’s bond downgrade are the deterioration of financial prospectus there are usually negative spillover effects for stockholders, that are not present when the reason for the downgrade are changes in leverage policy (Goh and Ederington (1993)). In contrast, Ingram, et al (1983) show that in municipal bonds markets, changes in ratings are not fully anticipated and thus leads to a change in the municipality conditions. The main reasons are the small seasonally update of accounting information, the lack of official mandatory auditing processes and the disparity in each municipality report structure.
It is well documented that changes in the sovereign credit risk spills over to the real economy. Augustin et al. (2012) show that an unexpected sovereign negative shock leads to a higher cost of debt for financial and non-financial firms within the same country.
Sometimes these spillover effects can cross borders and thus raise the firms’ cost of debt not only within its country but also across countries (Gande and Parsley (2005)).
The results in this paper show the financial implications of CRAs policies.
Municipal bond issuers should be aware of the investors’ reliance on credit ratings in
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these markets. Governments should be aware of possible externalities that follow a sovereign downgrade, such as the relative increase in the cost of debt and decrease in debt supply for issuers with the highest credit quality. Even though CRAs are gradually moving away from the sovereign ceiling rule the results show that it is still applied and/or investors expect its application. This paper makes three important contributions. First, it verifies the robustness of the methodology proposed by Almeida, et al (2014) at a subnational level, instead of focusing in corporate bonds, on which the sovereign downgrade produces several financial effects through the sovereign ceiling rule. Second, show a different application of the sovereign ceiling rule: on which issuers do not necessarily present changes in credit ratings of outstanding bonds (can be higher than the sovereign) but are no longer able to make new issues with credit ratings above the sovereign. Finally, it shows the problems that arise from investors’ overreliance on credit ratings in opaque markets and the impacts that follow on the different issuers. Such problems could be avoided with mandatory and periodically disclaimers regarding the issuers financing and operating positions.
1.2. Data and Sample Description
1.2.1. Rating Methodology
The main results from this work spur from changes in credit ratings of municipal bonds issuers around the 2011 U.S. sovereign downgrade. According to S&P, the main factors that determine the credit rating of a municipality are: government framework, financial management, economy, budgetary performance, debt and liability profile.7 S&P classifies each one of these factors in a scale from 1 (strongest) to 4 (weakest). Next, all individual scores are combined to generate an overall score on which it is assign a
7Information available: http://www.nasra.org/Files/Topical%20Reports/Credit%20Effects/StateRatingsMethodology.pdf.
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preliminary rating. The relationship between the overall score and the preliminary rating is presented in Table A.2 in the Internet Appendix. Finally, S&P considers several overriding factors (non-quantitative criteria) that can alter the initial credit rating and thus the final rating is created. Among the overriding factors are considered: access to capital markets, financial management, and willingness to support debt, among others.
Following the U.S. sovereign downgrade in August 5th 2011, S&P announced that it would downgrade more than 11,000 outstanding AAA bonds due to specific links between those bonds and the federal government. According to S&P’s explanation the issuers’ credit worthiness should remain unchanged, meaning that their fundamentals remain unaltered, but the adjusted bonds’ support system and/or government framework could be different. By conditioning the samples to include only uninsured issues and controlling for links with the sovereign I am able to capture the issuers’ credit worthiness before and after the sovereign downgrade. In the existence of a shock it should affect all issuers by changing the government framework.
1.2.2. Secondary Market
To create the sample for the secondary market tests I collected bond prices, yield and trade amounts from MSRB Electronic Municipal Market Access (EMMA) database, from the year prior and post August 5th 2011. From Bloomberg I extracted the bond type (segmented between General Obligation type, Revenue type or other type), the tax status (segmented between exempt state and federal levels), the issuer industry and the county identification. The data set extracted from Bloomberg was used to compute the number of outstanding bonds for each issuer.8 To avoid regulatory concerns I only consider
8I extracted from Bloomberg the maximum number of possible uninsured and investment grade bonds between August 5th 2010 and August 5th 2012 and required them to be traded at least once during this period (to avoid counting bonds that were called before maturity).
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issuers that are able to issue investment grade bonds: bonds with rating higher or equal than BBB-. The variables description, source and computation method is presented in Table A.1 in the Internet Appendix.
The sample was conditioned from 3-months prior up to 6-months after the event, and thus minimizes the impact of other possible contemporaneous shocks. In order to capture the issuers’ credit worthiness the sample excludes insured bonds and/or bonds with any kind of asset-backed security. The period pre-sovereign downgrade corresponds to the third month prior to the event (May 5th 2011 to June 5th 2011) and the periods post sovereign downgrade considered are: one month (August 5th 2011 to September 5th 2011), three months (October 5th 2011 to November 5th 2011) and six months (January 5th 2012 to February 5th 2012).
Table 1 shows the summary statistics of the data used in the multivariate regressions in section 3. Panel A of Table 1 splits the sample between the treatment and control groups, with the number of treated issuers ranging between 75 and 81 and the number of control issuers ranging between 501 and 526. As expected, bonds from the control group present average yield and spread between 0.96 percentage points and 1.07 percentage points higher than treated issuers.9 The natural logarithm of the average daily trade volume and the issuers’ number of bonds outstanding do not present significant differences between treated issuers and control issuers.
Panel B of Table 1 splits the sample between the periods pre and post sovereign downgrade. For both periods the average yield and spread decreases between 0.47 and 1.12 percentage points. On both periods the average daily volume traded and the issuers’ number of outstanding bonds do not present significant differences.
9The yield considered is the last daily yield-to-maturity and the Spread-to-treasury is computed by the difference between the yield and the treasury with the closest maturity.
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The sample for the new issues is from Ipreo i-Deal and includes and S&P’s long- term credit ratings. It includes new issues for the year prior and post August 5th 2011. I collected offer yield, par amounts, coupons and maturities. I completed the data set by collecting from Bloomberg several issuer/issue characteristics, which allowed the estimation of the number of each issuer’s outstanding bonds prior to each new issuance.
I discarded all bonds that have any kind of insurance (contractual or asset-backed security) in order to account for the issuers’ credit worthiness. To avoid regulatory concerns are only considered investment grade bonds. With these criteria I generate a sample with 69,764 new issues and 2,419 different issuers. I apply a numerical transformation to the rating scale that is increasing in the credit quality (AAA=22, AA+=21, AA=20,…, C=1). Table 2 presents the sample summary statics. Panel A of Table 2 splits the sample between new issues from treated and control issuers, while Panel B splits the sample between the pre and post sovereign downgrade periods.
Panel A of Table 2 shows that new issues from treated issuers present offer yield (spread) 42 basis points (37 basis points) lower than control issuers, which is consistent with the inherent higher credit quality of treated issuers. Most new issues can be classified as General Obligation (GO) or as Revenue (R) types. Bonds of “GO” type are backed by the “full faith and credit” of the issuer, which has the power to tax residents to pay bondholders. Bonds of “R” type are secured from revenues generated by specific project or revenue source. The percentages of these types are similar between treated issuers (50% GO and 48% R) and control issuers (54% GO and 39% R). On average, treated issuers make new issues that are considerable larger than control issuers, with the average natural logarithm of the amount issued being 25.25%
higher than control issuers, and have almost 2.8 times more bonds outstanding. Most
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bonds have some kind of tax exemption, being the proportions similar between treated (78% State-tax exemption and 7% Federal-tax exemption) and control issuers (72%
State-tax exemption and 10% Federal-tax exemption).
Panel B shows a decrease in the cost of debt through a decrease in the offer yield (spread) of 77 basis points (18 basis points) from pre to the post-sovereign downgrade periods. The average amount issued shows a slight increase, being consistent with the lower cost of financing. For both periods, the proportions of GO bonds, R bonds, Federal tax-exempt bonds, State tax-exempt bonds and the average number of bonds outstanding do not present significant differences.
1.2.4. Identification Strategy
Figure 1 shows the distribution of downgraded municipal bonds issuers, conditional on their rating prior to the sovereign downgrade. The figure shows that AAA issuers (treatment group) have a considerably higher probability of being downgraded in the first issue made after the sovereign downgrade. For example, previous AAA issuers have a probability of being downgraded 1.4 times higher than AA+ issuers. Overall, the figure shows that the closer the issuer’s rating is to the sovereign (AAA) the higher is the probability of being downgraded following the sovereign downgrade.
Panel A of Table 3 shows that from the entire issuers in the sample 3.72% were downgraded, 92.93% maintained the credit rating and 3.35% were upgraded, following the sovereign downgrade. Panel B of Table 3 shows the distribution of downgraded issuers, measured in notches, conditional on the issuer credit rating prior to the sovereign downgrade. In the full sample, 70% of all downgraded issuers suffered a downgrade with the same magnitude as the sovereign. Overall, previous AAA issuers have a significant
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higher probability of being downgraded and the adjustment is usually in line with the magnitude of the sovereign downgrade.
I argue that the adjustments made to the issuers’ credit ratings are mainly driven sovereign ceiling policies. The adjustments are not only made to the credit ratings of existing outstanding bonds but mainly to the issuers ability of issuing new debt with the same credit rating as before. Standard explanations like the deterioration of macroeconomic fundamentals are insufficient to explain this effect. The fact that treatment group had higher credit quality than the control group means that it should be less affected. A possible explanation is that in the event of a macroeconomic shock the rating adjustment is a non-linear function of the new default probability. For example, a systematic shock that increases the all the default probabilities by 0.5% could be enough to move a issuer from AAA to AA+ but insufficient to move a issuer from AA+ to AA.
However, based on the overall indicative score of a given state issuer in Table A.2 in the Internet Appendix this should not be the case. For example, considering an issuer in the median overall score for each rating category it is easier to move from the AA+ to AA then from AAA to AA+. Furthermore, In Section 3 I provide evidence regarding the investor’s expectation regarding sovereign ceiling policies, by conditioning the sample to only contain issuers that maintained their credit rating. The results still show a negative asymmetric shock on previously AAA issuers (treated issuers).
To further rule out other possible explanations I restrict the control group to contain specific credit rating categories. For example, this analysis allows to access the impact of the sovereign downgrade between AAA issuers and issuers in the “AA” category, i.e.
issuers that were able to issue AA+, AA or AA- bonds prior to the sovereign downgrade.
With the exception of the “BBB” category, I find a negative asymmetric shock in the
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treatment group in comparison to other credit rating categories that steams from the higher downgrade probability.
Overall, the evidence indicates that S&P continues to apply or is expected to apply the sovereign ceiling rule on new issues. These adjustments have strong implications in terms of the capacity of issuing new funds as well as the cost of debt. In the following sections I provide empirical evidence that shows a asymmetric shock in the treatment group as a consequence of the sovereign ceiling rule.
1.3. Evidence From the Secondary Market
For these tests I define the treatment and control groups at the issuer level. The treatment group contains outstanding issues from AAA issuers, i.e. issuers that attain at least one AAA credit rating in the last issue made in the year prior to the sovereign downgrade. The control group contains issues from non-AAA issuers, i.e. issuers that attain credit ratings below AAA in the last issue made in the year prior to the sovereign downgrade, but are still at the investment grade level (higher than BBB-).
In this section I provide empirical evidence that treated issuers are relatively more affected by the 2011 U.S. sovereign downgrade than control issuers, due to sovereign ceiling policies. To access this impact I segment the analysis in three parts: returns, yield- to-maturity (yield), spread-to-treasury (spread) and trade volume; while focusing on a narrow window around the event and thus minimizing the impact of other contemporaneous shocks. I test the investors’ ability to move past the credit ratings and access the issuer’s fundamentals, as well as the effects that steam from the sovereign ceiling policies. For completeness I also segment the analysis by trade size, using Dick- Nielsen et al. (2012) trade segmentation: small trades (usually associated with retail investors) present volumes traded smaller than $100,000, while large trades (usually
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associated with institutional investors) present volumes traded larger or equal than
$100,000. This segmentation allows to access differences between several types of investors that, due to regulatory or other reasons, might target specific credit ratings (e.g.
institutional investors).
1.3.1. Ex – Post Returns
I begin the analysis by comparing the returns of outstanding muni bonds for both treated and control issuers around the sovereign downgrade. The main difficulty of this analysis, as shown in Figure 2A (2B), is the inherent illiquidity of these markets. As a consequence, analysis of cumulative abnormal returns produces weak results. To avoid this problem I analyze bond’s returns up to a 3-month window around the event. For each interval returns were computed as the percentage price change for the last daily price for each interval. For each bond it was required at least one monthly trade the 3-month window around the event. Next, I created three equally weighted portfolios each composed by: bonds from treated issuers, control issuers and issuers that were able to issue bonds in the “AA” category (i.e. issuers able to issue at least one bond with credit rating AA+, AA or AA- in the last issue prior to the sovereign downgrade). Through the different time-windows each equally weighted portfolio presents the same composition of bonds, thus minimizing selection concerns. Finally, I use these portfolios in order to create the following spreads: “Treated – Control” and “AAA bonds – AA bonds”.
Figure 3 displays the different spreads across different time windows for both the pre and post sovereign downgrade periods. For example, for the 2-month period prior to the event one should compare the spread “Treated – Control” (T-2M; T+1M) with (T-1M;
T+2M).10 With this time window the spread “Treated – Control” decrease from -0.28%
10(T-2M; T+1M) stands for the return/spread computed based on the first price traded in the second month prior to the sovereign downgrade and the first price in the first month post sovereign downgrade. (T-1M; T+2M) stands for the return/spread computed
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to -1.80%. Figure 2 shows that for all time windows the spreads consistently decrease from the pre to the post event periods.
Table 4 presents the three portfolios’ returns that sustain Figure 3. The standard errors of each portfolio were estimated by bootstrap inference, due to the small number of observations. As shown in Figure 3, bonds from control issuers systematically present relatively higher returns after the event. The results are statistically significant and the spreads decrease as the window around the event widens. The event should affect all outstanding issues in the muni bond markets and the fact that the spreads decrease substantially provides empirical evidence of an asymmetric negative shock on outstanding bonds from treated relative to control issuers.
To ensure that the results are not driven by significant differences in credit quality between different issuers I compare the portfolio of bonds from treated issuers with issuers in the “AA” category, which have lower but close credit quality. Figure 3 shows that after the sovereign downgrade the evolution of the spread
“AAA bonds – AA bonds” decreases as the time window widens. Table 4 shows that the returns are statistically significant. For example, in a 2-month period around the event the spread “AAA bonds – AA bonds” decrease from -0.21% to -1.72%. This analysis provides evidence that the previous results are not driven by significant differences in credit quality.
For completeness, I replicate the analysis of Table 4 while discarding bonds from issuers that had a change in their rating around the sovereign downgrade. The results are reported in Table A.3 in the Internet Appendix and show that the previous results are still valid. For the same time-window pre and post sovereign downgrade the spreads “Treated – Control” and “AAA bonds – AA bonds” show a significant decrease. The results
based on the first price traded in the month prior to the sovereign downgrade and the first price in the second month post sovereign downgrade.
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suggest that investors penalized relatively more bonds from treated issuers due to the higher probability of being downgraded as a result from the lower sovereign ceiling.
1.3.2. Cost of Debt
In this section I access the impact on the yield-to-maturity and spread-to-treasury of outstanding bonds from treated and control issuers. Figure 4A shows the monthly evolution of muni bond yields in a 6-month window around the sovereign downgrade.
The figure shows that the differential between treated and control is steady declining after the sovereign downgrade, while satisfying the parallel trends necessary for a difference- in-differences approach. Figure 4B shows that the difference of market yields between treated and control issuers consistently increases after the sovereign downgrade.11
I quantify the impact of the sovereign downgrade in the market yield and spreads of outstanding bonds from treated and control issuers, through a difference-in-differences framework. I start by estimating through OLS the reduce form regressions (1) and (2) up to six months after the sovereign downgrade. ∆𝑦𝑖𝑒𝑙𝑑𝑖,𝑗, (∆𝑠𝑝𝑟𝑒𝑎𝑑𝑖,𝑗) represents the change in the yield-to-maturity (spread-to-treasury) of bond j from issuer i around the sovereign downgrade. The equations are described as follows:
∆𝑦𝑖𝑒𝑙𝑑𝑖,𝑗= 𝛽𝑡𝑟𝑒𝑎𝑡𝑒𝑑𝑖+ 𝛾∆𝑙𝑛(𝑡𝑟𝑎𝑑𝑒 𝑣𝑜𝑙𝑢𝑚𝑒)𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖 (1) ∆𝑠𝑝𝑟𝑒𝑎𝑑𝑖,𝑗 = 𝛽𝑡𝑟𝑒𝑎𝑡𝑒𝑑𝑖+ 𝛾∆𝑙𝑛(𝑡𝑟𝑎𝑑𝑒 𝑣𝑜𝑙𝑢𝑚𝑒)𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖 (2)
where the variable 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 that takes the value 1 if the issuer belongs to the treatment group and 0 otherwise. ∆𝑙𝑛(𝑡𝑟𝑎𝑑𝑒 𝑣𝑜𝑙𝑢𝑚𝑒) is the change in the natural logarithm of the
11The spread between treated and control issuers’ was computed by the difference between the average monthly yield of treated and control issuers. The methodology to compute the average monthly yields is described in Figure 3.
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average daily trade volume and αFE represents the set of fixed effects: state fixed effects, to control for state specific conditions and regulations and the type of government institution fixed effects.12 This analysis yields one observation per bond, while it allows for multiple bonds per issuer.
The results from estimation are presented in Panel A of Table 5. When considered a 6-month period after the sovereign downgrade bonds from treated issuers see their market yield increase by 10 basis points (significance at 1%) and the spread increase by 11 basis points (significance at 1%), in respect to bonds from control issuers. As the time window widens the effect becomes stronger and statistically more significant. In the estimation procedure were also considered standard errors clustered at the issuer and state levels that are presented in Panel A of Table A.4 in the Internet Appendix. The difference in standard errors suggests the inexistence of residual correlation at the issuer or state levels.
Next, I estimate the impact on the yield and spread that steams from the issuers’
change in credit rating around the sovereign downgrade.13 In order to access this impact I use instrument variables where the issuer’s change in credit rating is instrumented by the variable 𝑡𝑟𝑒𝑎𝑡𝑒𝑑, that captures if the issuer was on the sovereign ceiling. This analysis allows addressing endogeneity concerns regarding the relationship between changes in the issuer fundamentals and the changes in credit ratings. Since S&P do not strictly follow the sovereign ceiling rule a treated issuer can keep its credit rating after the sovereign downgrade. Around the sovereign downgrade and under ceiling policies, the variable 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 is expected to be correlated with issuer’s change in credit rating but uncorrelated with changes in fundamentals. Since is used one instrument for one
12 Cornaggia et. al (2015) proposes the division of municipal issuers into four groups: state, city, county and others. This division serves as a proxy of the issuer size and opacity.
13I compute the issuer’s change in credit rating by computing the difference in notches between the highest rating from last issue made prior to the sovereign downgrade and the highest credit rating from the first issue afterwards.
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endogenous variable (the system is exactly identified) it is not possible to test the validity of this instrument. The full set of equations was estimated through two stage least squares (2SLS) as follows:
𝛥𝑟𝑎𝑡𝑖𝑛𝑔𝑖 = 𝛽𝑡𝑟𝑒𝑎𝑡𝑒𝑑𝑖+ 𝛾𝑙𝑛(𝑡𝑟𝑎𝑑𝑒 𝑣𝑜𝑙𝑢𝑚𝑒)𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖 (3) ∆𝑦𝑖𝑒𝑙𝑑𝑗 = 𝛽𝛥𝑟𝑎𝑡𝑖𝑛𝑔̂ 𝑖+ 𝛾𝑙𝑛(𝑡𝑟𝑎𝑑𝑒 𝑣𝑜𝑙𝑢𝑚𝑒)𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖 (4)
∆𝑠𝑝𝑟𝑒𝑎𝑑𝑗= 𝛽𝛥𝑟𝑎𝑡𝑖𝑛𝑔̂ 𝑖+ 𝛾𝑙𝑛(𝑡𝑟𝑎𝑑𝑒 𝑣𝑜𝑙𝑢𝑚𝑒)𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖 (5)
where 𝛥𝑟𝑎𝑡𝑖𝑛𝑔 represents the issuer’s change in credit rating, measured in notches, around the sovereign downgrade. The covariates and fixed effects are the same used in equations (1) and (2). The analysis accounts for one observation per issue while allowing multiple observations per issuer.
The results from estimation are presented in Panel B of Table 5 and are consistent with the reduced forms in Panel A. When considered 6-months after the sovereign downgrade is shown that a one-notch downgrade that spurs from sovereign ceiling policies leads to an increase in 15 basis points (significance at 1%) in the yield and 15 basis points (significance at 1%) in the spread of bonds from treated relative to control issuers. As the time window widens the results become more significant and the effects stronger. The same standard errors extensions are described in Panel B of Table A.4 in the Internet Appendix and suggest the inexistence of residual correlation at the issuer and state levels.
Overall, the results from Table 5 provide empirical evidence of an asymmetric negative impact on treated issuers following the sovereign downgrade. The fact that the results only became statistically significant at 1% significance level with a wider time window could be attributed to the inherent illiquidity of these markets, as shown in
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Figure 2. In order to access the robustness of these results I replicate the analysis from Table 5 while excluding from the sample outstanding bonds with less than 3 trades in each period. Table A.5 in the Internet Appendix show that illiquid bonds are not driven the results presented in Table 5, rather the results become more pronounced and significant when considered more liquid bonds. Panel A of Table A.5 shows that when considered a 6-months period after the event treated issuers presents yield and spread 16 basis points (significance at 1%) and 17 basis points (significance at 1%) higher than control issuers, respectively. Panel B shows that when considered the sovereign ceiling rule treated issuers present yield and spread 18 basis points (significance at 1%) higher than control issuers 6-months after the event, respectively. In order to access the investors’ expectation I conditioned the sample to only contain issuers that did not suffer a change in credit rating. The results are presented in Table A.9 in the Internet Appendix and, with the exception of the 3-month period after the sovereign downgrade, show the existence of the asymmetric shock on treated issuers relative to control issuers.
To access the impact of differences in credit worthiness I exploit the impact of the sovereign downgrade by directly comparing bonds from treated issuers with bonds from specific categories: “AA”, “A” or “BBB”.14 Table A.6 in the Internet Appendix replicates the analysis from Panel A of Table 5 while conditioning the control group to contain only bonds from issuers that belong to a specific credit rating category. With the exception of
“BBB” category the results show an asymmetric negative impact on treated issuers.
When considered a 6-month period after the sovereign downgrade, treated issuers present yield (spreads) 11 basis points (11 basis points) higher than “AA” issuers and 16 basis
14An issuer is considered in the “AA” category if it was able to issue at least one bond with rating AA+, AA or AA- in the last issue prior to the sovereign downgrade. An issuer is considered in the “A” category if it was able to issue at least one bond with rating A+, A or A- in the last issue prior to the sovereign downgrade. An issuer is considered in the “BBB” category if it was able to issue at least one bond with rating BBB+, BBB or BBB- in the last issue prior to the sovereign downgrade.
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points (16 basis points) higher than “A” issuers. The results are significant at 1%
significance level.
In order to account for different types of investors I replicate the approach of Table 5 while conditioning the sample by different trade magnitudes (the procedure used is the same as in Dick-Nielson et al. (2012)): small trades - trades smaller than $100,000 (usually associated with retail investors); and large trades - trades higher or equal than
$100,000 (usually associated with institutional investors). The results from estimation are presented in Table A.7 in the Internet Appendix. For a 6-month period after the event, Panel A shows that the yield for treated issuers increase by 12 basis points (significance at 1%) for small trades while for large trades increase by 17 basis points (significance at 1%). Panel B provides the results when considering sovereign ceiling policies. For a 6-month period after the event, a one-notch downgrade that spurs from the sovereign ceiling policies increases the yield of treated issuers by 16 basis points (significance at 1%) for small trades and increases the yield by 28 basis points (significance at 1%) for large trades.
1.3.3. Impact on Trade Volume
Determining the impact of the sovereign downgrade in the trade volume of outstanding muni bonds presents extra challenges. As shown in Figure 2A (2B) the inherent illiquid of these markets makes it difficult to access the existence of an impact in the trade volume. Take for example one bond that in the pre-sovereign downgrade period was traded between a large or institutional investors (large trade) and in the post- sovereign downgrade period was traded between small or retail investor (small trade).
The change in the amount traded would be negative, even though the main cause for this change was the different type of investor/trade.
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I apply a difference-in-differences analysis to the natural logarithm of the trade volume. I estimate a reduced form expression where ∆𝑙𝑛(𝑡𝑟𝑎𝑑𝑒 𝑣𝑜𝑙𝑢𝑛𝑒)𝑗 represents the change in the natural logarithm of the trade volume of bond j around the sovereign downgrade. The results from the estimation are presented in Table 6.15 Panel A of Table 6 show the existence on a negative impact in the volume traded for treated issuers’ bonds.
For example, when analyze 6-months after the event the average volume traded of bonds from treated issuers decreases by 30% (significance at 5%) relative to control issuers.
Next, I estimate the impact on the trade volume from a downgrade that spurs from ceiling policies by instrumenting the issuers’ change in credit rating by the variable treated. The results are presented in Panel B of Table 6. For example, when considered a 6-month period after event issuers that suffered a one-notch downgrade that spurs from the sovereign ceiling policies leads to a reduction of the volume traded by 44%
(significance at 5%). In order to access the impact of illiquid bonds in this analysis I replicate the analysis from Table 6 while conditioning the sample to only contain bonds with at least three trades in each period. The results are presented in Table A.8 from the Internet Appendix and show that 6-months after the event treated issuers make on average issues 62% (significance at 5%) smaller than control issuers. Also, when applied the sovereign ceiling policy I show that a one-notch downgrade originated by ceiling policies decrease the amount issued of treated issuers by 67% (significance at 5%) relative to control issuers.
In order to minimize the effect on the trade volume caused by different types of investors I replicate the analysis of Table 6 while separating between small trades (< $100,000) and large trades (≥ $100,000), as suggested in Dick-Nielsen et al. (2012).
The results from estimation are presented in Table 7. Overall, the results show an
15Different specifications for the standard errors are presented in Table A.13 in the Internet Appendix.
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immediate response from investors that do not persist throughout time. One-month after the sovereign downgrade small trades, usually associated with retail investors, increase 11% (significance at 5%) for treated relative to control issuers, while it is not observed an effect from the sovereign ceiling policies. An opposite reaction occurs for large trades, usually associated to institutional investors, with the volume trading decreasing 66%
(significance at 1%) for treated relative to control issuers. For a one-notch downgrade that spurs from ceiling policies large trades decrease by 70% (significance at 1%). These results provide evidence that retail investors do not react negatively to this shock by the decreasing the amount traded of outstanding bonds from treated issuers. Instead, retail investors take advantage of the relatively higher yield and increased the volume traded after the event. On an opposite spectrum, the evidence suggests that institutional investors tend to target specific credit ratings thus decreasing the amount traded of treated issuers and thus reacting directly to changes in credit ratings.
1.4. Evidence From New Issues
For these tests the treatment and control groups are defined, as in section 3, at the issuer level. The treatment group contains new issues from issuers that were able to issue at least one AAA bond in the last issue prior to the sovereign downgrade. The control group contains new issues from issuers that attain credit ratings below AAA, but still investment grade credit ratings, in the last issue prior to the sovereign downgrade. This methodology considers one observation per each new issue while allowing several new issues per issuer.
Following the results presented in the previous sections, I access the impact of the sovereign downgrade into treated issuers’ ability of issuing new debt. In the presence of a negative asymmetric shock treated issuers are expected to present a relative higher cost
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of debt, translated into higher offer yields and spreads-to-treasury, and a relatively decrease in the amounts issued, in respect to control issuers. To further complement the analysis I test the lead/lag effect of the sovereign downgrade in the offer yield of treated and control issuers.
1.4.1. Cost of Debt
In this segment I access the extent on which the sovereign downgrade increases the issuers’ cost of debt, by estimating the increments on offer yield (yield) and spread-to- treasury (spread) on new issues from treated in respect to control issuers. Figure 5 presents the average quarterly yield of treated and control issuers (Figure 5A) and their respective difference (Figure 5B). Figure 5A shows that both groups satisfy the parallel trends for a difference-in-differences framework, while Figure 5B shows a decrease the in the spread “treated – Control” following the sovereign downgrade.
In order to quantify the impact shown in Figure 5 I estimate, through OLS, the reduce form expressions (6) and (7). The dependent variable 𝑦𝑖𝑒𝑙𝑑𝑖,𝑗 (𝑠𝑝𝑟𝑒𝑎𝑑𝑖,𝑗) represent the offer yield (spread-to-treasury) of bond j from issuer i. The equations are described as follows:
𝑦𝑖𝑒𝑙𝑑𝑖,𝑗 = 𝛽1𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 + 𝛽2𝑡𝑟𝑒𝑎𝑡𝑒𝑑 + 𝛽3𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 + 𝛾𝑋𝑖,𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖,𝑗 (6) 𝑠𝑝𝑟𝑒𝑎𝑑𝑖,𝑗 = 𝛽1𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 + 𝛽2𝑡𝑟𝑒𝑎𝑡𝑒𝑑 + 𝛽3𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 + 𝛾𝑋𝑖,𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖,𝑗(7)
where the variable 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 that takes the value 1 if the issuer belongs to the treatment group and 0 otherwise. The variable postdown takes the value of 1 if the bond was issued after the sovereign downgrade and 0 otherwise. 𝑋 represents the set of covariates that includes several issue characteristics (e.g., coupon rate, maturity, tax regime) and
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issuer characteristics (e.g., number of outstanding bonds prior to the new issue, state).
The set of covariates is fully described in Table A.1 of the Internet Appendix. As in section 3, 𝛼𝐹𝐸 represents the set of fixed effects, which includes issuer state and the type of government institution.16
The results from estimation are presented in Panel A of Table 8. The interaction term (𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × 𝑡𝑟𝑒𝑎𝑡𝑒𝑑) shows that, following the sovereign downgrade, the offer yield and the spread increase by 9 basis points (significance at 1%) and 11 basis points (significance at 1%) for treated relative to control issuers. The coefficient treated shows that issuers from the treatment group are on average less risky, with offer yield and spread 26 basis points (significance at 1%) and 27 basis points (significance at 1%) lower than the control group. When compared the pre and post sovereign downgrade periods the variable postdown shows a decline on both yield and spread of 62 basis points (significance at 1%) and 5 basis points (significance at 5%). The decline in the average yield is consistent with Quantitative Easing policies set in place by the Federal Reserve with the objective of stimulating the economy and has, as a spillover effect, a decline in average costs of funding. In the estimation procedure I also consider robust standard errors and standard errors clustered at the state level. These estimations are presented in Table A.12 in the Internet Appendix. Even though standard errors presented some differences most results remain economically and statistically significant.
Next, I estimate the impact in the yield and spread that steams from a change in the issuers’ credit rating. As in section 3, I instrument the change in credit rating (∆𝑟𝑎𝑡𝑖𝑛𝑔) around the event by the variable 𝑡𝑟𝑒𝑎𝑡𝑒𝑑. The system of equations estimated by 2SLS is described below:
16Cornaggia et. al (2015) proposed the division of municipal issuers into four groups: state, city, county and others. This division serves as a proxy of the issuer size and opacity.
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∆𝑟𝑎𝑡𝑖𝑛𝑔̂ 𝑖= 𝛽1𝑡𝑟𝑒𝑎𝑡𝑒𝑑𝑖+ 𝛾𝑋𝑖,𝑗+ 𝛼𝐹𝐸 (8)
𝑦𝑖𝑒𝑙𝑑𝑖,𝑗 = 𝛽1𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × ∆𝑟𝑎𝑡𝑖𝑛𝑔̂ 𝑖+ 𝛽2∆𝑟𝑎𝑡𝑖𝑛𝑔̂ 𝑖+ 𝛽3𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 + +𝛾𝑋𝑖,𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖,𝑗 (9)
𝑠𝑝𝑟𝑒𝑎𝑑𝑖,𝑗= 𝛽1𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × ∆𝑅𝑎𝑡𝑖𝑛𝑔̂ 𝑖+ 𝛽2∆𝑟𝑎𝑡𝑖𝑛𝑔̂ 𝑖+ 𝛽3𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 + 𝛾𝑋𝑖,𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖,𝑗(10)
where the covariates used are the same as in equations (6) and (7). Equation (8) represents the first stage that predicts the change in the issuers’ credit rating that spurs from the sovereign ceiling policies. Equations (9) and (10) correspond to the second stage for each of the two dependent variables. The set of fixed effects (𝛼𝐹𝐸) is the same used in equations (6) and (7).
Panel B of Table 8 presents the results from this estimation. The interaction term (𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × ∆𝑟𝑎𝑡𝑖𝑛𝑔̂ ) shows the impact of the sovereign ceiling policy in the issuer’s offer yield and spreads in the aftermath of the sovereign downgrade. For example, a one- notch downgrade that spurs from the sovereign ceiling policy increases the average yield and the spread by 16 basis points (significance at 1%) and 25 basis points (significance at 1%), respectively.
To further exploit the results I examine the extent on which the impact verified on the offer yield and spread-to-treasury leads or lags the sovereign downgrade. I replicate equations (6) and (7) while implementing a set of dummy variables that breaks the sample into several quarters before and after the event. The variable periodT±tQ takes the value of 1 if the bond was issued after the tth quarter and 0 otherwise. Hence, the interaction term periodT−2Q× treated captures the incremental yield or spread on treated in respect to control issuers after February 5th 2011. The set of covariates (𝑋) and fixed effects (𝛼𝐹𝐸) used are the same in Table 8. The results from estimations are presented in Table 9, with Panel A showing the impact on the offer yield while Panel B the impact on the spread-
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to-treasury. Table 9 shows that the negative impact on treated issuers in respect to control issuers is contemporaneous with the sovereign downgrade.
1.4.2. Impact on the Amounts Issued
The results presented in previous sections show the existence of an increase in the cost of debt for treated issuers in respect to control issuers following the sovereign downgrade. Given the increase in cost of debt it should be expected a relative contraction in debt supply for treated issuers. To access this impact I compute the difference-in- differences estimator using as the dependent variable the amount issued. 𝑙𝑛(𝑝𝑎𝑟)𝑖,𝑗 is the natural logarithm of the par amount issued in $ millions of issuer i for the new issue j. I start by estimating the following reduced form expression:
𝑙𝑛(𝑝𝑎𝑟)𝑖,𝑗= 𝛽1𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 + 𝛽2𝑡𝑟𝑒𝑎𝑡𝑒𝑑 + 𝛽3𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 + 𝛾𝑋𝑖,𝑗+ 𝛼𝐹𝐸+ 𝜀𝑖,𝑡 (11)
where the fixed effects (𝛼𝐹𝐸) are the same as in Table 8. The set set of covariates (𝑋) is fully described in Table A.1 in the Internet appendix.
The results from estimation are presented in Panel A of Table 10. The interaction term (𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × 𝑡𝑟𝑒𝑎𝑡𝑒𝑑) shows that in the in the year post sovereign downgrade treated issuers reduce the amount issued of each new issue by 56% (significance at 1%) relative to control issuers. Also, the year post sovereign downgrade shows an increase 54% (significance at 1%) on the average amount issued per issue, which is consistent with the decline of the cost of debt presented in the previous sub section. Treated issuers make on average new issues 43% (significance at 1%) higher than control issuers.
Next, I use instrumental variables by estimating through 2SLS the impact in the amount issued due to the issuers’ change in credit rating, which is instrumented by the
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variable treated. The results from estimation are reported in Panel B of Table 10. The interaction term (𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × ∆𝑟𝑎𝑡𝑖𝑛𝑔̂ ) shows that a one-notch downgrade that spurs from the sovereign ceiling policies reduces the average size of new issues by 57%
(significance at 1%). The results are in line with Panel A and corroborate the relative contraction of debt supply from treated in respect to control issuers.
A concern of the previous analysis is the fact that most issuers make several contemporaneous issues with different conditions, such as different coupon rates and maturities. The fact that each issuer makes new issues smaller in the aftermath of the sovereign downgrade does not necessarily translates into a contraction of debt supply if each issuer makes more issues. To account for this problem I analyze the impact in the natural logarithm of the total amount issued per issuer in each period, while considering the average values of each covariate. I start by estimating the following reduced form:
𝑙𝑛(𝑡𝑜𝑡𝑎𝑙 𝑝𝑎𝑟)𝑖 = 𝛽1𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × 𝑡𝑟𝑒𝑎𝑡𝑒𝑑 + 𝛽2𝑡𝑟𝑒𝑎𝑡𝑒𝑑 + 𝛽3𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 + +𝛾𝑎𝑣𝑒𝑟𝑎𝑔𝑒(𝑋𝑖,𝑗) + 𝛼𝐹𝐸+ 𝜀𝑖,𝑡 (12)
where the fixed effects are the same as in Table 9 and the set of covariates 𝑋 is averaged for each issuer in the periods pre and post sovereign downgrade.
The results from estimation are presented in Panel A of Table 11. The interaction term (𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 × 𝑡𝑟𝑒𝑎𝑡𝑒𝑑) shows that following the sovereign downgrade treated issuer reduced the total amount issued by 65% (significance at 1%) in respect to control issuers. In the post-downgrade period the total amount issued increases by 89%
(significance at 1%) while treated issuers make new issues 87% (significance at 1%) larger than control issuers. These results are consistent with the Table 9. Treated issuers are relatively more affected than control issuers making new issues relatively smaller and
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issuing relatively smaller amounts of new debt in the aftermath of the sovereign downgrade.
Panel B of Table 11 shows the impact in the total amount issued when instrumented the change in credit rating by the variable treated. The interaction term (𝑝𝑜𝑠𝑡𝑑𝑜𝑤𝑛 ×
∆𝑟𝑎𝑡𝑖𝑛𝑔̂ ) shows that a one-notch downgrade that spurs from the sovereign ceiling policy reduces the size of new issues from treated issuers by 68% (significance at 5%). The results are in line with Panel A and corroborate the relative contraction of debt supply of treated issuers in respect to control issuers.
1.5. Conclusion
I show that the 2011 U.S. sovereign downgrade had an asymmetric effect in the municipal bonds markets through the S&P sovereign ceiling rule policy. I access this asymmetric shock by comparing the cost of debt and amounts traded/issued of bonds from issuers that had credit quality equal to the sovereign prior to the downgrade (treatment group) and bonds from issuers that had investment grade rating smaller than AAA prior to the sovereign downgrade (control group). The treatment group presents yield-to-maturity and offer yield relatively higher than the control group in the aftermath of the sovereign downgrade. With the increase in the cost of debt the volume traded of bonds from treated issuers declines relative to control issuers. However, the reactions between large investors (trades ≥ $100 000) and small investors (trades < $100 000) the results are different: small investors increase the amount traded of bonds from treated issuers while large investors decrease the amount traded. The increase in the cost of debt of treated issuers is consistent with a contraction in debt supply through a relative reduction of the overall amount issued and amount per issue of treated in respect to control issuers.
