Do Credit Rating Agencies Add Value? Evidence from the Sovereign Rating Business Institutions

Transcription

1 Iner-American Developmen Bank Banco Ineramericano de Desarrollo (BID) Research Deparmen Deparameno de Invesigación Working Paper #647 Do Credi Raing Agencies Add Value? Evidence from he Sovereign Raing Business Insiuions by Eduardo A. Cavallo* Andrew Powell* Robero Rigobón** **Iner-American Developmen Bank **Massachuses Insiue of Technology November 2008

2 Caaloging-in-Publicaion daa provided by he Iner-American Developmen Bank Felipe Herrera Library Cavallo, Eduardo A. Do credi raing agencies add value? : evidence from he sovereign raing business insiuions / by Eduardo A. Cavallo, Andrew Powell, Robero Rigobón. p. cm. (Research Deparmen Working Papers ; 647) Includes bibliographical references. 1. Credi bureaus. 2. Credi conrol. I. Powell, Andrew (Andrew Philip). II. Rigobón, Robero. III. Iner-American Developmen Bank. Research Dep. IV. Tile. V. Series. HG C C dc Iner-American Developmen Bank 1300 New York Avenue, N.W. Washingon, DC The views and inerpreaions in his documen are hose of he auhors and should no be aribued o he Iner-American Developmen Bank, or o any individual acing on is behalf. This paper may be freely reproduced provided credi is given o he Research Deparmen, Iner- American Developmen Bank. The Research Deparmen (RES) produces a quarerly newsleer, IDEA (Ideas for Developmen in he Americas), as well as working papers and books on diverse economic issues. To obain a complee lis of RES publicaions, and read or download hem please visi our web sie a: hp:// 2

3 Absrac 1 If raing agencies add no new informaion o markes, heir acions are no a public policy concern. Bu as raing changes may be anicipaed, esing wheher raings add value is no sraighforward. This paper argues ha raings and spreads are boh noisy signals of fundamenals and sugges raings add value if, conrolling for spreads, hey help explain oher variables. The paper addiionally analyzes he differen acions (raings and oulooks) of he hree leading agencies for sovereign deb, also considering he differing effecs of more or less anicipaed evens. The resuls are consisen across a wide range of ess. Raings do maer and hence how he marke for raings funcions may be a public policy concern. JEL Codes: F37, G14, G15, C23 Keywords: Raings, Spreads, Informaion Economics, Even Sudies. 1 This paper represens he views of he auhors and do no necessarily reflec he views of any insiuion including he IDB, is Execuive Direcors or he counries hey represen. We hank Jeromin Zeelmeyer, John Chambers, Eduardo Fernández-Arias and seminar paricipans a he XXVII Meeing of he Lain American Nework of Cenral Banks and Finance Minisries for very useful commens and Francisco Arizala for superb research assisance. All remaining errors are our own. 3

4 1. Inroducion Recenly, raing agencies have come under fire for heir role in assessing risks of srucured producs. Here we focus on somehing hey have been doing for a longer period of ime: raing sovereign deb. Deb insrumens are acively raded in secondary markes, hereby providing up-o-dae informaion on prices, yields and spreads over riskless deb. Credi raings are ofen mapped ino defaul probabiliies, bu bond spreads may also be mapped ono he same scale. Credi raings and spreads appear, hen, o be capuring he same hing; he quesion we consider in his paper is wha can raing agencies ell us ha we canno learn simply from looking a he price of deb? One poenial answer is ha raing agencies and invesors may have differen informaion ses. Take he case of a small invesor who wishes o have a diversified porfolio. I would generally no be in he ineress of each such invesor o have a large research deparmen focusing on he fundamenals of each counry. Insead, he invesor will rely on a cenral informaion source such as a raing agency. Some larger invesors may of course have research deparmens, and brokers ha serve many invesors are also a source of a grea deal of analysis. A second answer is ha invesors and raing agencies wih he same informaion may have differen opinions. Indeed, as we deail below, differen raing agencies frequenly have differen views on sovereigns. Roughly, and using sandard mappings beween he agencies, hey disagree abou as much as hey agree on raings. Anoher way o sae he poenial role of raing agencies is hen o sugges ha raings and spreads are boh noisy signals of rue and perhaps unknowable deep economic fundamenals. The quesion we address in his sudy is, hen, given he signals provided by markes, do raing agencies add informaion? This is an imporan opic no only as an academic issue o undersand how markes funcion, including he marke for informaion on how o value asses, bu also an imporan policy issue. If raing agencies do no add informaion, hen heir opinions do no maer and i is difficul o argue ha here is any policy concern regarding heir aciviies. On he oher hand, if i is found ha hey do add informaion, heir opinions maer and i is imporan o know ha he credi raing marke is working well. Several recen papers have considered he role of raing agencies in he sovereign deb marke. Canor and Packard (1996) and Afonso, Gomes and Roher (2007) show ha raings can be modeled fairly successfully by economic fundamenals. Several papers show ha raings 4

5 affec spreads, bu he real quesion is wheher raings affec spreads conrolling for fundamenals. Eichengreen and Mody (1998) and Dell Ariccia, Schnabel and Zeelmeyer (2006) regress raings on fundamenals and inerpre he error as he raing agencies opinion. They hen show ha his residual is highly significan in explaining spreads. Powell and Marínez (2007) replicae hese analyses; hey also employ a sysem of equaion approach and furher argue ha he raing agencies differences in opinion are informaive. In oher words, when one agency changes a raing and he ohers do no, hen his is associaed wih a change in spreads. Despie hese effors, i is no ye possible o argue ha he case is closed. Each mehodology employed o dae has is paricular drawbacks. In he case of he echnique used by Eichengreen and Mody (1998) and Dell Ariccia e al. (2006), i is a heroic assumpion ha he error of he raings equaion represens he raing agencies opinion and no ha his equaion is simply mis-specified. In he sysem approach favored by Powell and Marinez (2007) a differen bu also heroic assumpion is needed o idenify he sysem. In he approach employing raing agencies differences of opinions, one raing agency may follow a spread change raher han acually affec he spread. Moreover, here may be more informaion in markes han is capured in hese models, and he above approaches do no conrol for he curren informaion in markes, bu only curren fundamenals and/or raings. This paper raises he bar wih respec o he papers cied by esing if credi raings influence spreads over and above he informaion ha is already aggregaed in marke variables. Anoher ack would be o aemp an even sudy as in he corporae finance lieraure see Campbell, Lo and Mckinlay (1997) for a discussion. However, raing agencies appear o ry o signal when raing changes may occur. Sovereign deb is eiher given a posiive or negaive oulook (suggesing an upgrade or a downgrade may be he nex change respecively) and addiionally may be placed on a raing wach (indicaing ha a decision may be abou o be made). Moreover, agencies publish wha a paricular sovereign would have o do o improve is raing, and while arges may no be precise, he informaion required o make a judgmen is generally public and indeed may become a focus of marke research and analysis. All his implies ha he classic even sudy mehodology may no be appropriae as raing changes may be anicipaed. This means ha i is a real challenge o answer he quesion as o wheher raings agencies add value. If raing agency acions are fully anicipaed hen we would see no effec on 5

6 spreads. Bu seeing no effec on spreads of a raing change would no mean ha raing agencies do no add value. In our view oulined above, ha boh raings and spreads are noisy signals of fundamenals, i jus implies ha whaever effec raings had on spreads may have already been incorporaed ino spreads. We herefore sugges ha we need o seek oher mehods o ackle his quesion. For his purpose, we firs devise a simple specificaion es o evaluae wheher or no raings are informaive. We conclude ha hey are. Nex, we consider a ype of horse race beween raings and spreads as o how well hey are correlaed o oher macroeconomic variables using high-frequency daa. We sugges ha, given he possibiliy of full anicipaion, his is a beer mehod o evaluae wheher raing agencies add value. However, we also argue ha oulook changes give ineresing informaion on how anicipaed raing changes have been. If he oulook is changed jus a few days before he raing, hen i seems reasonable o sugges ha he raing change is largely unanicipaed before ha dae. We exploi his and oher furher deails of he process in our analysis below. We also conduc ess on wheher cerain raing changes are more imporan ha ohers. In paricular, if a deb issue obains an invesmen grade raing his may allow differen classes of invesors o purchase hose issues and hence he insrumen may be said o have changed asse class. We es below wheher raing changes in and ou of invesmen grade are more imporan han oher changes. Our resuls across several mehods and for he hree main credi raing agencies are srong and highly consisen. We find ha we canno rejec he view ha raing agencies add value. We find ha his is rue for boh changes in asse classes and oher raing changes, and we find ha less anicipaed raing changes have even more significan effecs. We conclude ha raing agencies do maer, and hence ha here is a public policy concern regarding wheher hese agencies are doing a good job. 6

7 2. Organizing Framework The quesion we are ineresed in answering is o evaluae he informaional conen ha he raing has in addiion o he observed spread on markeable sovereign deb (henceforh spread). 2 In oher words, he null hypohesis ha all he informaion in he raing is already refleced in he spread is equivalen o saying ha he spread is a sufficien saisic. The alernaive hypohesis, on he oher hand, implies ha spreads and raings are imperfec measures of he unobservable fundamenals of he economy, and herefore raings provide informaion above and beyond wha spreads reflec. In his secion we organize our houghs regarding raings and spreads in a simple errorin-variables framework. The goal is o devise a simple specificaion es o evaluae wheher or no raings are informaive. 2.1 Preliminary Consideraions Some consideraions are necessary o clarify before devising an empirical sraegy. Firs, his paper is sudying sovereign raings, and in his conex raing agencies are concerned wih evaluaing counries probabiliy of defaul, or counry risk. This is imporan because in his environmen, if he spread of he sovereign deb is observed, hen i is reasonable o assume ha he spread and he raing are supposedly capuring he same aspec. I is impossible o evaluae he informaional conen in he raing only using raings and spreads. We need oher variables. Forunaely, counry risk no only affecs he spread, bu also impacs oher macroeconomic variables. For insance, an increase in he probabiliy of defaul of a counry should have a negaive impac on all asse prices, paricularly sock prices. Therefore, if we observe a downgrade we should expec a drop in he sock marke index. If he spread is a sufficien saisic for he raing, hen if we were o run a regression where he spread and he raing are included on he RHS, he raing should be insignifican afer conrolling for he spread. In fac, we sudy hree macro variables: he spread one period ahead, sock marke prices, and he nominal exchange rae. 2 We focus he analysis on sovereign bonds spreads, which are compued as he difference of he yield-o-mauriy of a bond, minus he yield-o-mauriy of a comparable riskless bond (i.e., US Treasuries). These are he mos widely used proxies of risk by marke observers. 7

8 The second consideraion is ha we concenrae on high-frequency (i.e., daily) daa. This explains our choice of macro variables. The main reason why we look a daily daa is ha, if raings have any informaional conen beyond he spread, we expec his informaion o be incorporaed ino macro variables wihin days; and herefore, monhly daa will be unable o disenangle he spread and he raings informaional componens. Third, if raings and spreads are imperfecly measuring he fundamenal defaul probabiliy hen we can inerpre hem as noisy versions of an unobservable fundamenal. However, he raing, because of is discree naure, is hen a version of he fundamenal whose noise is no of classical form. In oher words, he raing can be inerpreed as a discreizaion of he fundamenal, and he noise implied in his measure is serially correlaed, and correlaed wih he fundamenal hence making i a non-classical error-in-variables problem. Our mehodology esing for he informaional conen has o be robus o his propery of he daa. Furhermore, raings are very sicky, in he sense hey change very infrequenly when observed daily. This means ha he error-in-variables (EIV) problem in he raing is probably more severe han in he spread esimaion. Therefore, in horse race esimaions beween he spread and he raing we have o be careful o ake ino accoun he possibiliy ha he EIV biases are differen across he wo variables. Fourh, exchange raes, spreads, sock prices, and raings are all endogenous. The mehodology we devise has o ake ino consideraion ha linear regressions migh be misspecified. The es has o be meaningful even in he presence of oher forms of mis-specificaion (no jus he error-in-variable inerpreaion). More imporanly, a crucial form of endogeneiy is he fac ha credi raing changes are indeed anicipaed by marke paricipans. This no only affecs he inerpreaion bu also how o implemen he esimaion. We reurn o he poin of anicipaion laer in he resuls secion. 2.2 Specificaion Tes Wih hese four consideraions a hand, we now proceed o explain our empirical sraegy. We assume ha spreads ( i ) and raings ( r ) are noisy versions of an unobserved fundamenal, i = i 0 x r = r +θ + ε + f (, η ) 0 x 8

9 where he idea is ha x is he unobserved fundamenal ha no only affecs he probabiliy of defaul of he counry (and is spread) bu also affecs he exchange rae, sock markes, and fuure spreads. We assume ha he raing is a non-linear funcion of he fundamenal rying rying o emphasize he discreeness of he variable. We assume a simple linear funcion for he spread, alhough ha is no resricive. Assume ha anoher macroeconomic variable y (which for exposiional simpliciy le s assume i is he sock marke) is affeced by he same fundamenal, y = y 0 x + β + μ The null hypohesis is ha he spread is a sufficien saisic i.e., ha he raing does no add informaion beyond wha he spread already capures. In a well-specified regression we could es for his by jus running a horse race beween spreads and raing. However, if he variables are endogenous or hey are measured wih error, hen his simple procedure migh no produce he correc inference. To resolve his problem we ake several seps in he esimaion procedure. Firs, we concenrae on he relaionship beween macro variables, spreads and raings around he periods in which he raing changes. Our preferred specificaion looks a he window 10 days before and afer a credi raing is modified. Second, we compue he cumulaive reurn on all he variables over he evens windows. This means ha if he movemen in he raing is anicipaed, spreads and macro variables will adjus before he raing acually changes. Hence, all will be endogenously deermined. Third, in his environmen, we regress he cumulaive change in he macro variables on he spread and compare he esimaes when he spread is insrumened by he raing. If he spread is a sufficien saisic for he raing, he wo coefficiens should be similar. If he spread and he raing summarize differen ses of informaion i.e. boh are imperfec measures of he fundamenals hen he wo coefficiens will be saisically differen. This procedure is robus o misspecificaion of he macro variable on he spread regression. In oher words, when we say ha he spread is a sufficien saisic for he raing, echnically wha we are saying is ha he change in he raing is capured by he movemen of he spread, and everyhing else in he raing is jus noise. Insrumening he spread wih he raing around he window in which he raing is changing herefore implies ha boh capure he same change in fundamenals. By concenraing on he window around he raing change we are 9

10 minimizing he EIV in he raing measure and providing he bes chance o he raing o provide addiional informaion. 3 Wha means ha he spread is a sufficien saisic for he raing? The simple model below highlighs a case in which he spread is indeed a sufficien saisic. i r = r = i 0 + θx + f (, η ) 0 x y = y 0 x + β + μ Uncorrelaed Error-in-Variables and Exogenous Fundamenals Le us sar by sudying he case when all he residuals are uncorrelaed. Because he spread capures he informaion in he fundamenal perfecly, when we esimae he regression: y = c 0 + bi he OLS esimae is consisen. Because he raing is a noisy version of he same fundamenal, and is noise is uncorrelaed wih he residual in he sock marke equaion, hen if we insrumen he spread wih he raing we also esimae a consisen coefficien. Imporanly, he insrumenal variable esimae is inefficien under he null hypohesis, and OLS is efficien. Under he alernaive hypohesis he spread is a noisy version of he fundamenal. This means ha he OLS esimae is inconsisen and biased; he bias comes exacly from he noise. This esimae can be improved, however, and he raing is a perfec insrumen for doing so. Firs, i is correlaed wih he spread because boh are measures of he same fundamenal. Second, heir noises are differen, and such noises are uncorrelaed wih he fundamenals. This means ha he raing is uncorrelaed wih he residual in he sock marke regression. In oher words, he raing is a valid insrumen for he spread and he IV esimaes are going o be a consisen esimae of he rue parameer. This is a sandard specificaion es. Under he null hypohesis, OLS is consisen and efficien, while IV is consisen bu inefficien. On he oher hand, under he alernaive hypohesis, OLS is inconsisen, bu IV coninues o be consisen (see Hausman, 1978). + ϕ 3 In oher words, when he raing is no changing i is possible o argue ha he defaul probabiliy is changing lile as well, and herefore no change in raings is imperfecly measuring small changes in fundamenals. However, when he raing is indeed changing, we expec in hose windows for he fundamenal o cross some hreshold, and herefore he increase in he raing indeed reflecs an improvemen in he fundamenal. 10

11 2.2.2 Uncorrelaed error-in-variables, and endogenous fundamenals The mos imporan source of possible misspecificaion in his model is when he fundamenals are no exogenous in oher words, when cov( x, μ ) 0. The mehodology we have described has no problems dealings wih his form of misspecificaion. Le us assume ha he measured fundamenal and he residual in he sock marke equaion are correlaed. The implicaion of his assumpion is ha OLS is biased, bu because in our window he raing is proporional o he fundamenal x, hen he IV will be equally biased if and only if he spread is a sufficien saisic. In oher words, if he spread is a sufficien saisic bu he fundamenals are correlaed wih he residual in he macro equaion, OLS and IV are equally biased. In he alernaive hypohesis, when he spread is no a sufficien saisic, hen boh coefficiens are biased, bu hey are biased differenly. The simples way o undersand he inuiion behind his es is o assume ha boh he spread and he raing are linear funcions of he fundamenal: r y i = i 0 + θx = r 0 x + α + η = y 0 x + β + μ The OLS esimae of he sock marke on he spread is equal o bˆ OLS cov( i, y ) = var( i ) βθ var( x ) + θ cov( x, μ ) = 2 θ var( x ) β cov( x, μ ) = + θ θ var( x ) where, jus for clarificaion, he bias arises from he correlaion beween he fundamenal and he residual in he sock marke regression. I is needless o say ha he OLS esimae when consisen is an esimae of he raio beween θ β. In his environmen, he IV esimae is (using he raing as he insrumen) bˆ IV cov( r, y ) = var( i ) βα var( x ) + α cov( x, μ ) = θα var( x ) β cov( x, μ ) = + θ θ var( x ) where he source of he misspecificaion cov( x, μ ) 0, is exacly he same in boh regressions. Noice ha boh esimaes are numerically he same. 11

12 Under he alernaive hypohesis he wo esimaors are going o differ from each oher. The OLS esimaor has wo forms of bias: one from mis-specificaion, and one from he error-invariables. On he oher hand, he IV esimae will have only bias from mis-specificaion. In he end, he es is roughly he same: he coefficiens should be he same under he null hypohesis bu differen in he alernaive hypohesis. The main difference is he inerpreaion of he coefficiens, bu no he validiy of he es. This is an imporan characerisic of our design because, cerainly, changes in raings, spreads, and financial variables are endogenous, hey are driven by common shocks ha are unobservable, and raing changes migh be anicipaed. 4 Our es will be able o deal wih hese aspecs. This example highlighs he form of specificaion ha we can solve analyically. I is he one in which he fundamenal and he residual of he economy are correlaed bu he error-invariables are sill orhogonal o everyhing else. In oher words, his solves he mos basic (and possibly imporan) form of misspecificaion: he fac ha he fundamenals and he residuals in he sock marke are correlaed. For insance, his covers omied variable biases and endogeneiy. In paricular, his includes he anicipaion of raing changes Correlaed Error-in-Variables Assume ha he errors in he raing equaion are also correlaed wih he fundamenal (nonclassical) hen he esimae of he IV is slighly differen from he OLS: bˆ IV cov( r, y ) = cov( r, i ) β = { α var( x ) + cov( x, η )} + α cov( x, μ ) β α cov( x, μ ) = + θ{ α var( x ) + cov( x, η )} θ θ{ α var( x ) + cov( x, η )} In his case, he esimaes (IV and OLS) will be differen, because he noise of he raing is correlaed wih he fundamenal. Ineresingly, in his case, he raing is indeed providing informaion above and beyond ha conained in he spread, and herefore a rejecion should be found. However, in his case he informaion is no necessarily conained in he acual change in he raing bu in is noise. This is imporan because we will be able o conclude wih our mehod 4 In fac, anicipaion of improvemens in fundamenals implies hacov( x, μ ) 0. 12

13 wheher or no he raing conains informaion, alhough we do no know or will no be able o disenangle is source. In summary, if he spread is a sufficien saisic, hen i capures all he relevan flucuaion of ha is conained in he raing. Because he sock marke (or exchange rae) equaion is likely o be mis-specified, he es can be performed, bu he coefficiens canno be inerpreed srucurally speaking. If he spread is a noisy measure of he raing (add noise o he firs equaion of our model) or he noise of he raing is correlaed wih he fundamenal or he residual, hen he raing is indeed providing informaion beyond he one conained in he spread, and we have shown ha he esimaion of he OLS and IV coefficiens will differ from each oher Error-in-Variables Finally, before discussing he esimaion and resuls we devoe our aenion o he error-invariables inerpreaion we are providing o he spread and he raing. In Figure 1 we have depiced he fundamenal, he spread, and he raing. In general we assume ha he spread differs from he fundamenal, and ha hose differences can be capured wih a sandard classical errorin-variables. A priori, here is no reason o have a differen view on he discrepancy beween he fundamenal and he spread. In fac, mos will argue ha here is no difference and ha he spread indeed capures he fundamenal. The difference beween he fundamenal and he raing is wha we inerpre as he errorin-variables. The idea is ha he raing is rying o capure he fundamenal, bu i is a discreized version of i. If he fundamenal increases, he raing increases, bu i does so in a sicky way. This implies ha he error-in-variables in he raing clearly is non-classical. 5 The es described here has discussed mosly he linear case, bu he non-linear case is exacly he same. For insance, ake a non-linear model and linearize i. The residuals in ha model will be correlaed wih he unobservable fundamenal exacly in he way we discussed cases 2 and 3. 13

14 Figure 1. Errors-in-Variables In oher words, he error-in-variables are serially correlaed. When he raing is below he fundamenal, i is very likely o coninue o be below he fundamenal in he following period. A classical error is serially uncorrelaed. Second, and probably more imporanly, when he raing remains he same and he fundamenal increases, he error-in-variable increases, which means ha he error-in-variables is correlaed wih he fundamenal. Finally, around he credi raing changes, he error-in-variables are serially negaively correlaed. The reason is ha if here is a rend in he fundamenal, and he raing moves up, hen he errors prior o he change in he raing were negaive, and hey are likely o be posiive aferwards. When he spread is a sufficien saisic, we are assuming ha he spread measures he fundamenal wihou error, and herefore he spread capures x perfecly, while he raing does no. When he spread is no a sufficien saisic, we assume ha he error-in-variables for he spread is classical, while he one for IV is no. One quesion ha should arise immediaely is wha assumpions are needed for he IV sraegy o be valid. This is very simple: we jus need he error-in-variables of he raing o be uncorrelaed wih he error-in-variables of he spread which we assume is rivially saisfied under he null hypohesis (given ha he error is exacly zero for he spread under he null). 14

15 3. Daa 3.1 Daase and Mehodology The raw daa for his sudy comes from Bloomberg daabase and from raing indusry sources. From Bloomberg, we colleced daily informaion available for 32 emerging marke economies beween January 1, 1998 and April 25, In paricular, we colleced daa on he following macroeconomic variables: sovereign spreads, nominal bilaeral exchange raes (domesic currency unis vis-à-vis he US$), and local sock marke indices. 7 We also colleced daa on he so-called volailiy index (VIX), a widely used measure of marke risk. 8 From he hree main raing agencies (Fich, Moody s and Sandard & Poor s), we colleced daa on raings and oulooks for he same daes and we abulaed he days of raing and oulook changes. 9 The resuling daase is an unbalanced panel wih 77,760 observaions. The raings from he hree agencies are ransformed ino a numeral scale (beween 1 lowes and 21 highes using he scale proposed by Afonso e al. (2007). Table 1. Raing Scale Fich Raing Number Moodys Raing Number S&P Raing Number AAA 21 Aaa 21 AAA 21 AA+ 20 Aa1 20 AA+ 20 AA 19 Aa2 19 AA 19 AA- 18 Aa3 18 AA- 18 A+ 17 A1 17 A+ 17 A 16 A2 16 A 16 A- 15 A3 15 A- 15 BBB+ 14 Baa1 14 BBB+ 14 BBB 13 Baa2 13 BBB 13 BBB- 12 Baa3 12 BBB- 12 BB+ 11 Ba1 11 BB+ 11 BB 10 Ba2 10 BB 10 BB- 9 Ba3 9 BB- 9 B+ 8 B1 8 B+ 8 B 7 B2 7 B 7 B- 6 B3 6 B- 6 CCC+ 5 Caa1 5 CCC+ 5 CCC 4 Caa2 4 CCC 4 CCC- 3 Caa3 3 CCC- 3 CC 2 Ca 2 CC 2 C 2 C 1 SD 1 DDD 1 D 1 DD 1 D 1 Source: Afonso e al. (2007) Invesmen Grade Speculaive Grade 6 The lis of counries is in he Appendix. 7 Some counries have muliple sock marke indices. The lis of indices used in his sudy is in he Appendix. 8 The VIX is consruced using he implied volailiies of a wide range of S&P 500 index opions. This volailiy is mean o be forward-looking and is calculaed from boh calls and pus. 9 One conribuion of his paper is o assemble a consisen daase wih precise daes for raing and oulook changes ha have been cross-checked wih indusry sources. 15

16 The nex sep consised of rearranging he maser daase o make i amenable o he analysis. For his purpose, firs we defined evens as changes in he raings for each of he hree raing agencies. Raing changes are eiher upgrades or downgrades of one noch or more. Table 2 summarizes he resuling evens per raing agency Table 2. Number of Evens by Raing Agency Number of evens Downgrades Upgrades Sandard & Poor's Fich Moody's For each of hese evens we defined a 21-day window 10 cenered on he day of even. Thus, he raing becomes a sep variable wihin each window: i has a saring value for he firs 10 days, hen jumps on day 11 (eiher upgrade or downgrade), and hen remains a he new value for he subsequen 10 days. 11 Nex, in order o make he res of he daa comparable across counries and evens, we normalized he variables so ha he saring poin for every series in each even window is he same. The normalizaion consiss of aking, for every day in he window, he following ransformaion: y = log( X ) log( X 0 ) where X is, alernaively: he sovereign spread, he sock marke index, he nominal exchange rae, and he VIX; X 0 is he value of he corresponding variable on he firs day of he window; and y is he ransformed variable, which is simply he cumulaive reurn. Thus, he iniial value for hese variables in each even window ( y o ), is normalized a zero. Table 3 below repors he summary saisics for he normalized variables grouped by raing agencies. 10 Alernaively, for robusness checks purposes, we defined 41-day and 11-day windows around he even. 11 In he cases where here are muliple raing changes wihin he same even window, we rea each raing change as an independen even. We alernaively drop hese evens from he sample for robusness checks purposes, bu he resuls remain unchanged. 16

17 Table 3. Summary Saisics Sandard & Poor's Variable Obs Mean Sd. Dev. Raing Spread Sock Marke Exchange Rae VIX Fich Variable Obs Mean Sd. Dev. Raing Spread Sock Marke Exchange Rae VIX Moody's Variable Obs Mean Sd. Dev. Raing Spread Sock Marke Exchange Rae VIX The firs panel shows ha for he case of S&P raings, where we have 145 evens, we end up wih,3045 observaions for he raing (i.e., 145 evens x 21 days per even). We repor he mean and he sandard deviaion of he raing for all he evens. In he rows below, we repor he summary saisics for he oher variables of ineres, where, for example, a value of 0.01 for he mean indicaes ha he average value of he corresponding variable for all he available days, across all evens, is 1 percen higher han he average value on he firs day of he window. The oher wo panels replicae he same exercise bu for evens based on he daa from he oher wo raing agencies. 3.2 Relaionship beween Spreads and Raings As discussed in he inroducion, several recen papers consider he relaionship beween spreads and raings. Eichengreen and Mody (1998) argue ha raings are imporan in explaining spreads. They regress raings on fundamenals and hen inroduce he residual of ha regression 17

18 ogeher wih fundamenals in a regression o explain spreads. They argue he residual reflecs he raing agency opinion and find ha i is highly significan. González Rozada and Levy Yeyai (2007) sugges ha a large componen of individual counry spreads is driven by global facors such as he overall EMBI spread or he US high-yield spread. In one specificaion hey include he raing as a conrol for counry fundamenals and find i o be significan wih he expeced sign. Powell and Marínez (2006) sar wih a simple regression of spreads agains raings and sugges ha a simple log-log relaionship works reasonably well o capure how an improvemen in he raing may lead o a reducion in spreads. They sugges, hough, ha he reducion in spreads o June 2007 levels is only parially explained by he improvemen in raings. They replicae he resuls of Eichengreen and Mody (1998) and also sugges a sysem of equaions wih similar resuls, suggesing ha raings may maer. They also exploi he differences beween raing agencies opinion and show ha hose differences may be informaive in explaining spreads. The differences in opinions beween raing agencies can be represened in various ways. In his paper we focus on raing changes as evens. Below, we presen a Venn diagram ha summarizes he disribuion of evens across he hree raing agencies, and heir overlap. As explained, in he baseline each even has a 21-day window. Thus, an overlap (or a poenial agreemen) occurs when raing changes for he differen agencies happen wihin he same window. For example, ou of 141 evens for S&P, overlap wih evens of Fich, 12 wih evens of Moody s, and 15 wih he wo raing agencies concurrenly. The general message ha emerges from Figure 2 is ha he overlap is relaively small across he hree raing agencies. This suggess ha he raing agencies do no always ac concurrenly, and hence ha disagreemens beween agencies persis. In urn, his suggess ha he informaional conen of he evens across he agencies migh be differen. In paricular, if he credi raings are no perfecly correlaed hen hey all hree canno be fully explained by he exac same saisic (in his case, he spread). In oher words, given how uncorrelaed he acions of he raings agencies are, i should be a priori clear ha hey provide differen informaion among hemselves. And if one of hese raings is perfecly explained by he spread, hen he oher wo can no. Therefore, in he 12 We use 141 evens, raher han he oal of 145 evens in able 2, because here are 4 evens ha happen wihin he window of a previous even. Thus, we drop hese o avoid double couning when comparing wih he oher raing agencies. We do he same for Moody s and Fich, where we drop 4 and 5 evens respecively. 18

19 analysis ha follows, we consider hese differences and es he validiy of our resuls using he daa from he hree raing agencies. Figure 2. Venn Diagram Resuls 4.1 Specificaion Tes We apply a sandard Hausman specificaion es. This is performed in wo seps. Firs we esimae he following models: OLS Model y i, α OLS ii, + θ VIX i, + κ i + ε i, = ; i = evens, and =days where y i, is, alernaively: i, + 1 i (i.e., he spread one day forward); s i, (i.e., he sock marke index); and ner i, (i.e., he nominal exchange rae); κ i is an even-fixed effec, and ε i, is he error erm. The VIX is included o conrol for he effec of global facors. We also run insrumenal-variables version of his regressions, where he only varian is ha we insrumen spreads wih raings: 19

20 IV-Model y i, = α IV ii, + θ VIX i, + κ i + ε i, j i i, = ri, where j is, alernaively: S&P, Moody s or Fich raings. For robusness checks purposes, we also run an error-correcion model for he case when he dependen variable is he spread. In his case, he esimaed equaion is as follows: Error-Correcion Model Δ i = α ii, + θ VIX i, + φ ΔVIX + κ i + ε i, where raing. Δ i = i i, + 1 ii,, and Δ VIX = VIX i, + 1 VIX i, In he IV-varian of he error correcion model, we simply insrumen he spread wih he The second sep consiss of applying a specificaion es using he esimaes from hese models. Hausman (1978) proposes a es where a quadraic form in he differences beween wo vecors of coefficiens, scaled by he marix of he difference in he variances of hese vecors, gives rise o a es saisic (chi-squared). Under he null hypohesis, OLS is consisen and efficien, while IV is consisen bu inefficien. On he oher hand, under he alernaive hypohesis OLS is inconsisen, bu IV coninues o be consisen. Table 4 summarizes he resuls we obain when we apply his es o our baseline specificaion (i.e., using all he evens upgrades and downgrades from S&P, and a window of 21-days per even). 13 Every column in he able is a differen dependen variable, and he las column is he error-correcion model. In he firs wo rows, we repor α OLS and α IV, respecively. 14 Thus, he coefficien repored in he firs row under he firs column is he OLS esimae for he effec of he curren spread on he spread one day forward. The OLS resuls suggess ha increases in he spread have a posiive effec on he spread forward (firs and fourh columns), are relaed o decreases in he sock marke index (second 13 For his purpose, we sack all he evens (i.e., boh upgrades and downgrades) ogeher and run he regressions for he full sample of S&P evens. 14 We omi o repor he coefficien for he VIX in he sandard OLS and IV regressions, and he res of he coefficiens in he error correcion model, as hey are no essenial for explaining he es we perform in his secion. 20

21 column) and are also relaed o depreciaions of he nominal exchange rae vis-à-vis he US dollar (hird column). The IV resuls (i.e., insrumening spreads wih raings) are qualiaively similar. Wha he Hausman specificaion es reveals is, in essence, if hese coefficiens are also quaniaively he same. 15 If hey are saisically differen, he null hypohesis is rejeced i.e., OLS is inconsisen. Quie imporanly for our purposes, he rejecion of he null hypohesis is evidence ha he spread is no a sufficien saisic. In he nex wo rows of Table 4, we repor he Hausman saisic (chi-squared) and he corresponding p-value. The resuls are ha he null hypohesis is rejeced a sandard confidence levels (10 percen or less) in hree ou of four cases. This suggess ha, for hese seleced macro variables, he spread is no a sufficien saisic. In oher words, no all he informaion in he raing is refleced in he spread, and hus he raing explains some of he variaion in hese macro variables. I is worh re-emphasizing here ha he es is valid even if he OLS regression is misspecified, a leas for he mos common forms of misspecificaion. The nex sep consiss of checking he robusness of hese resuls: we wan o evaluae if we ge a high number of rejecions for he specificaion es across differen possible varians. In Table 5 we summarize he resuls of a series of robusness checks. The firs se of checks consiss of spliing he sample of evens ino upgrades and downgrades and running he regressions separaely. Nex, we repea he same exercise for boh he full and he spli samples, using he evens of Fich and Moody s. Then, we go back o he S&P daa and change he even window o 11 days per even (i.e., 5 days around he raing change), and also o 41 days per even (i.e., 20 days around each raing change). Finally, we drop he few evens ha occur wihin he same 21-day window (conemporaneous evens). 16 For each of hese alernaive specificaions we run he OLS, IV and error correcion models, and perform he corresponding Hausman es. In able 5 we repor he p-values. For comparabiliy purposes, in he firs row we repor he p-values from he previous regressions (Table 4). The las row and he las column in he able are he rejecion raes, i.e., he percenage of rejecions of he null hypohesis for each row or column. 15 This is no echnically correc, as he Hausman procedure uses all he esimaed coefficiens, and heir variance marix, o perform he es. 16 In he case of S&P, hese are four evens ha happen wihin he window of a previous even. 21

22 The resuls are very elling: he rejecion rae varies beween 56 and 75 percen in every column, which means ha we rejec a lo across many possible permuaions of he dependen variable and also he esimaion model. In he case of he rows, he rejecion rae is below 50 percen only once: i.e., Fich upgrades and downgrades. The high rejecion raes across he board reinforce he conclusion ha he spreads are no a sufficien saisic. In oher words, here seems o be some informaional conen in raings ha is no capured by he spreads. 17 A his poin we can also evaluae he robusness of he es o he misspecificaions ha we are no fully able o solve analyically. In paricular, recall from he mehodological secion ha if he errors in he raing equaion are also correlaed wih he fundamenal (non-classical), hen he esimae of he IV is slighly differen from he OLS. In his case, he esimaes (IV and OLS) will be differen under he null hypohesis. If his form of misspecificaion is significan, we expec more rejecions he bigger he windows are. The reason is ha he error in variables implied in he raing grows wih he window in which he raing is no changing. We do no find his in our ess. On he conrary, if anyhing, focusing on he case of he full sample (upgrades and downgrades for S&P) we find ha he rejecion rae is smaller when he widh of he even window is increased o 20 days around he even. 18 Despie his, and even if we his paricular form of misspecificaion is significan and we do no find more rejecions when we expand he window simply because widening he window weakens he power of he es (because he insrumen becomes noisier, and hence weaker), he reader should res assured ha he validiy of he es is no invalidaed because, as explained in Secion 2, we sill expec o find rejecions if he raing is providing informaion above and beyond he one conained in he spread. The only difference is ha we can no disenangle wheher his informaion comes from he raing change iself or from he noise. 17 We also run he same ess including a ime rend in he regressions for each even window. We find somewha lower rejecion raes, alhough in mos cases hey remain over 50%. I is hardly surprising ha he rejecion raes fall when we include a ime rend, as many evens are anicipaed (more on his below) and he effec of he anicipaion may be precisely a rend over he even window for he macro variables. Thus, we find i reassuring ha we sill find a high number of rejecions even when we include a rend. 18 We rejec 3 ou of 4 imes when he window is 10 days around he even, and only 2 imes when he window is expanded. 22

23 A he same ime, if he raing is a non-linear funcion of he fundamenal, hen he nonlineariy can be inerpreed as a non-classical EIV, which means ha he larger he window, he more severe he non-lineariy and he higher he chance of rejecing he specificaion es. Thus, some of he rejecions ha we obain wih larger windows migh be spurious. Despie his possibiliy, our resuls are also robus o defining he windows very ighly. The rejecion rae for he cases when he widh of he even window is only five days around he even is 75 percen he same as he baseline hence, indicaing ha he EIV inroduced by he non-lineariy is no significanly large. Having esablished ha spreads and raings are differen, in he nex secion we run a horse race beween hese variables. If raings have informaional conen as we sugges, hen we expec ha when we run a regression where boh variables are included on he RHS, he raing should be significan afer conrolling for he spread. 23

24 Table 4. OLS vs. IV Spread +1 Sock Marke Exchange Rae Spread +1 (Error Correcion) OLS 0.906*** *** 0.100*** *** [0.010] [0.009] [0.007] [0.010] IV 1.008*** *** 0.109*** [0.025] [0.024] [0.017] [0.025] Hausman Tes (Ch^2) P-value S&P raings are used for hese regressions. To perform hese esimaions he daa is arranged o allow a 21-day window around he day of he change in he raing. The OLS coefficien is he esimaed effec of he change in spread on he corresponding dependen variable. The IV is he coefficien obained when he spread is insrumened by he raing. All hese regressions include even fixed effecs and he Volailiy Index (VIX) as conrols. The null hypohesis in he Hausman es is ha he OLS esimaor is more efficien. 24

25 Table 5. Hausman Tes, P-values Spread+1 Sock Marke Exchange Rae Spread +1 (Error Correcion) Rejecion rae 1 Sandard & Poor's (downgrades + upgrades) % Sandard & Poor's (downgrades) % Sandard & Poor's (upgrades) % Fich (downgrades + upgrades) % Fich (downgrades) % Fich (upgrades) % Moodys (downgrades + upgrades) % Moodys (downgrades) % Moodys (upgrades) % Sandard & Poor's - 5 day window (all) % Sandard & Poor's - 5 day window (downgrades) % Sandard & Poor's - 5 day window (upgrades) % Sandard & Poor's - 20 day window (all) % Sandard & Poor's - 20 day window (downgrades) % Sandard & Poor's - 20 day window (upgrades) % Sandard & Poor's - Wihou conemporanous change in raing % Rejecion rae 2 75% 69% 63% 56% 1 Corresponds o number of rejecions of he null hypohesis in he Hausman es over he oal regressions run per dependen variable 2 Corresponds o number of rejecions of he null hypohesis in he Hausman es over he oal regressions run per specificaion Every cell is he P-value of he Hausman es in he corresponden OLS versus IV regressions. 25

26 4.2 Horse Race Having esablished ha spreads are no a sufficien saisic, we urn now o esimaing a new model in which we exploi he informaional conen of raings in order o explain he variaion in hree macro variables using high frequency daa. We esimae he following OLS model: y j i, α ii, + β r + θ VIX i, i, + κ i + ε i, = ; i = evens, and =days where y i, is, alernaively: i, + 1 i ; s i, ; ner i, ; κ i is an even-fixed effec, and ε i, is he error erm. The VIX is included o conrol for he effec of global facors. For robusness checks purposes, we also run an error-correcion model for he case when he dependen variable is i. In his case, he esimaed equaion is as follows: Error-Correcion Model Δ i = α i + j i, + β r + θ VIX i VIX i,, + φ Δ + κ i ε i, where Δ i = i i, + 1 ii,, and Δ VIX = VIX i, + 1 VIX i, I is clear from he previous discussion on mis-specificaion ha we canno inerpre he magniude of hese coefficiens in a srucural way. Therefore, in wha follows we jus focus on he signs and heir saisical significance. We wan o es if raings explain par of he variaion in he cumulaive reurns of he macro variables over he seleced even windows afer we conrol for spreads, and also if raing and spreads are correlaed o hese macro variables in ways ha make inuiive sense. The resuls are repored in Table 6. The able is organized slighly differen han he previous ones. The panel on he upper LHS has he resuls for he baseline regressions: S&P, all evens, and a 21-day window for each even. Every row is a differen regression: eiher a differen dependen variable or he error correcion model. Every column is he esimaed coefficien for he corresponding RHS variable. The sandard errors are repored in parenhesis below every poin esimae. In order o make he inerpreaion easier, we pu aserisks nex o he coefficiens ha are saisically significan. 19 Thus, he firs row shows he resuls of esimaing he model by OLS for he case in which he dependen variable is he spread one day 19 *: significan a 10 percen, **: significan a five percen, and ***: significan a one percen. 26

27 forward. We find ha, as expeced, α is posiive and saisically significan, meaning ha increases in he spread oday (i.e., a higher perceived probabiliy of defaul) are correlaed wih increases in he spread omorrow. Ineresingly, β eners wih a negaive sign and is also saisically significan, meaning ha an increase in he raing (i.e., an upgrade) is correlaed wih a decrease in spreads one day forward. The fac ha he raing is significan afer conrolling for he spread is addiional evidence in favor of he hypohesis ha spreads are no a sufficien saisic. The hird RHS variable included in he regression, he VIX, is posiive bu no saisically significan. Nex, we change he LHS variable o he sock marke index. In his case we find ha increases in he spread are associaed wih decreases in he sock marke indices, while an increase in he raing, conrolling for he spread, is correlaed o a saisically significan increase in he sock marke. Finally, in his case, he coefficien esimae for he VIX is negaive and saisically significan. The nex row presens he resuls for he case in which he LHS variable is he nominal exchange rae vis-à-vis he US dollar. The resuls are ha increases in he spread are associaed o nominal exchange rae depreciaions, a resul ha we find consisen wih wha we would expec for emerging marke economies: higher probabiliy of defaul is ofenimes associaed wihcapial fligh and a weakening of he domesic currency. A he same ime, he esimaed effec for changes in he raing, in his case, is no saisically significan. Noe, incidenally, ha his is he one case for which we did no rejec he Hausman es for he baseline specificaion in Table 4. This is addiional evidence in favor of he power of he es: in he case where we do no rejec he specificaion es, we find ha he raing is insignifican afer conrolling for he spread (i.e., he raing provides no addiional informaion). Finally, we find ha he coefficien esimae for he VIX is also posiive and saisically significan. In he las row, we repor he resuls of he error correcion model. 20 The resuls are reassuringly similar o hose in he firs row, which are based on he same dependen variable. The only difference is ha he coefficien esimae for he VIX, while sill posiive, is now saisically significan a he 10 percen level. 20 We omi he coefficien esimaes forφ, as hey are no essenial. 27

28 Nex, we rerun he baseline specificaion spliing he sample beween upgrades and downgrades. The resuls for he case of downgrades are repored in he upper-cener panel, while for he downgrades are repored in he upper-righ panel. The resuls are very similar o he previous ones, wih only a couple of differences. When we focus on downgrades, we find ha he esimaed effec of changes in he raing is no longer saisically significan when he LHS variable is he sock marke. In he case of upgrades, he coefficien esimae for he effec of changes in raing on he nominal exchange rae is now posiive and significan. The middle panels of Table 6 repor he resuls for he same exercise, bu for he case of he evens from he oher wo raing agencies. For concreeness, we concenrae only on he cases of he full sample (upgrades and downgrades sacked ogeher). We find ha in all cases, he coefficien esimae for α eners he regressions wih he expeced sign and is saisically significan: increases in he spread oday are associaed wih higher spreads omorrow, decreases in he sock marke, and nominal exchange rae depreciaions. In he case of β, he esimaed effec of changes in he raing, we find ha for Fich evens, hey ypically have no explanaory power, excep in he case when he macro variable is he exchange rae: in ha case, we find ha increases in he raings (i.e., upgrades) are associaed wih nominal appreciaions. This is ineresing because his is he one case where we find an insignifican esimae for S&P. Also, i is consisen wih he resuls of he specificaion es: in he case of Fich, full sample, we rejec he null hypohesis only when he dependen variable is he nominal exchange rae. Insead, in he case of Moody s, he raing always eners he regressions wih he expeced sign and is saisically significan: upgrades are associaed wih decreases in he spread forward, increases in he sock marke, and nominal appreciaions. In he case of he VIX, he coefficien esimaes are always significan in boh samples and have he same signs: increases in he VIX are associaed wih higher spreads forward, lower sock marke indices, and more depreciaed nominal exchange raes. Finally, in he lower panels of Table 6 we repor he resuls for he cases in which we narrow he widh of he even window o five days around he even and, alernaively, expand i o 20 days. We repor he resuls based on he S&P sample only (upgrades and downgrades). The resuls are reassuringly similar o hose of he baseline specificaion, wih one excepion. For he case of he expanded window, he effec of raing changes on he sock marke is no saisically significan. 28