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Determinants of forex market movements during the Europe-
an sovereign debt crisis:
The role of credit rating agencies.
Master’s Thesis within Economics and Finance
Author: Marharyta Karpava
Professor: Andreas Stephan
Jönköping June 2012
Master’s Thesis in Economics and Finance
Title: Determinants of forex market movements during the European Sover-
eign debt crisis: The role of credit rating agencies.
Author: Marharyta Karpava
Tutor: Andreas Stephan
Date: 2012-06-11
Subject terms: Credit rating, sovereign debt crisis, Euro depreciation, event study.
Abstract
The purpose of this thesis is to identify key factors underlying exchange rate develop-
ments during the European sovereign debt crisis by examining the impact of credit rat-
ing news, published by the three leading credit rating agencies, on conditional returns
and volatility of EUR/USD (direct quotation) exchange rate. Empirical results highlight
the importance of interest rate differential and volatility index of options exchange in
explaining EUR/USD exchange rate volatilities. Downgrade announcements by Stand-
ard & Poor’s as well as watch revisions by Fitch Ratings had a detrimental impact on
the value of Euro, leading to a subsequent Euro depreciation over the period under con-
sideration (January 2009 – April 2012).
Table of Contents
1 Introduction ............................................................................................ 1
2 Background information and literature review .................................. 4
2.1 Credit ratings industry ........................................................................................ 4
2.2 Role of credit rating agencies in financial markets ............................................ 5
2.3 Accuracy of sovereign default assessment ......................................................... 6
2.4 Sovereign credit ratings and financial crisis ....................................................... 7
3 Methodology ........................................................................................... 9
3.1 Exchange rate determination .............................................................................. 9
3.2 Event study methodology ................................................................................. 11
3.3 Motivation for the application of EGARCH model ......................................... 12
3.4 Regression model specification ........................................................................ 14
3.5 Empirical framework ........................................................................................ 15
4 Data and preliminary analysis ............................................................ 16
5 Empirical results .................................................................................. 18
5.1 Benchmark regression ...................................................................................... 18
5.2 Reactions to Standard & Poor’s credit rating announcements ......................... 19
5.3 Reactions to Fitch Rating’s credit rating announcements ................................ 22
5.4 Reactions to Moody’s credit rating announcements ......................................... 24
5.5 Limitations and implications for further research ............................................ 27
6 Conclusion ............................................................................................ 28
References .................................................................................................. 30
Appendix .................................................................................................... 34
M. Karpava: Sovereign credit rating signals and forex markets
1
1 Introduction
By reading daily news since summer 2007, one might get an impression that many developed
economies across the world are suffering from one long-lasting financial crisis; however, in
reality that is not the case. The global financial turmoil started in the United States with the
real estate bubble in the summer of 2007, which affected numerous financial institutions that
invested substantial funds in mortgage-backed securities, thereby leaving banks with liquidity
and solvency problems, and eventually resulting in a banking crisis. As a result, stock markets
crashed in September 2008 (following the collapse of Lehman Brothers), leading to a
subsequent contraction of wealth, GDP and fiscal income, thus, forcing governments to
intervene and initiate safety plans to rescue financial institutions. For example, in the period
from October 2008 through May 2010, approximately 200 banks from 17 countries
contributed around 1 trillion Euros to government guarantee programs (Levy & Schich,
2010). The Eurozone, in its turn, contributed around 23% of its GDP to financial sector
bailouts (Attinasi, Checherita, & Nickel, 2009). Government stabilization programs and
domestic demand shrinkage caused governments to take on more debt, which increased
country risk and probability of default, leading to a sovereign debt crisis in the Eurozone. For
instance, Irish government bond spreads rose significantly after the announcement of a
government guarantee for bank bonds (Sgherri & Zoli, 2009).
The sequence of crisis linkages did not stop here, but rather transformed into a currency crisis
as spreads on government bonds of affected countries rose, thereby weakening balance sheets
of the central banks. As a matter of fact, these tensions led to domestic currency depreciation
in the troubled countries, US dollar and Euro, for example. In the view of high uncertainty
about future dynamics of the domestic currency, any unfavorable macroeconomic
announcements could lead to further currency depreciation. Therefore, financial market
speculators could take advantage of the unstable position of the Euro currency and exploit
possible arbitrage opportunities, leading to a self-fulfilling currency crisis (Candelon & Palm,
2010). Credit rating agencies, in their turn, might also contribute indirectly to further Euro
depreciation by publishing negative watch lists and outlooks for countries within the
European Monetary Union. Therefore, the role of the credit rating agencies in this vicious
cycle will be closely examined in this paper.
Sovereign credit ratings have a large economic impact as they tend to increase the magnitude
of business cycles, because ratings are upgraded during expansionary periods and
downgraded during contractionary periods. Thus, any negative credit rating signals have a
detrimental effect on a sovereign’s economy by limiting the number of credit sources
available, thereby making debt and interest payments more costly. Portfolio managers are
forced to get rid of the downgraded securities due to the legislative requirements (many
government-owned financial institutions are prohibited from investing funds into below
investment-grade assets), thereby worsening already existing imbalances in financial markets.
In addition to this, sovereign credit ratings often set a limit to corporate ratings assigned to
financial institutions, which are operating in that country. Thus, negative credit rating
announcements do not only weaken the financial position of a sovereign on the
macroeconomic level; the impact of negative sovereign news is also well observed on the
microeconomic level.
M. Karpava: Sovereign credit rating signals and forex markets
2
Problem discussion
By looking at the graph of daily spot exchange rate movements over 60 consecutive months,
it is fairly obvious that Euro was relatively stable until the middle of 2008, when the global
economy was hit by the US stock market crash following the collapse of Lehman Brothers in
September 2008. The volatility did calm down for a while until the beginning of 2009, owing
mostly to central banks’ rescue packages for financial sectors to prevent collapse of the
affected institutions, thereby contributing to stabilization of the global economic activity and
subsequent Euro appreciation. Euro began depreciating at the end of 2008 - beginning of
2009, when credit rating agencies started downgrading Greek and Irish sovereign bonds in the
view of high budget deficits. The Euro began falling again in the last quarter of 2009 up until
the beginning of June 2010. This period of extensive Euro depreciation was largely affected
by rising public debt levels in Greece, Ireland, Portugal and Spain, which were reflected in
negative sovereign rating events, published by each credit rating agency within this time
frame. There was a sudden slump in the value of the domestic currency (Euro) around the end
of 2010 until the beginning of 2011, when S&P and Fitch assigned junk bond rating to Greek
government bonds. The Euro began depreciating again in the second quarter of 2011 up until
the beginning of January 2012, when a series of negative watch and outlook revisions were
announced by S&P and Fitch, followed by massive downgrade announcements for Eurozone
countries in the early January 2012.
According to the statistics data obtained from the Federal Reserve Bank of New York1, the
Euro suffered from 20% drop in value over a 7 month-period in 2009-2010 (Euro-currency
decreased from 1.4999 USD on 9 November 2009 to 1.19976 USD on 4 June 2010, thereby
reaching its five-year absolute minimum). In addition to this, the Euro suffered from 13%
decline in value over the 8 month-period in 2011-2012 (Euro-currency declined from
1.462499 USD on 26 April 2011 to 1.2723 USD on 5 January 2012)..
Figure 1-1: Spot exchange rate movements, 20 April 2007 – 20 April 2012.
1 Federal Reserve Bank of New York official website: http://www.federalreserve.gov/default.htm.
0,6
0,65
0,7
0,75
0,8
0,85
20-apr-07 20-apr-08 20-apr-09 20-apr-10 20-apr-11 20-apr-12
EUR/USD
EUR/USD
M. Karpava: Sovereign credit rating signals and forex markets
3
Purpose
The purpose of this paper is to identify main factors, underlying euro-dollar exchange rate
fluctuations during the European sovereign debt crisis. One of the primary objectives is to de-
termine the impact of sovereign credit rating announcements, if any, published by the three
leading credit rating agencies (Standard & Poor’s, Moody’s and Fitch Ratings), on the condi-
tional mean and volatility of the spot exchange rate USD/EUR (indirect quotation). The im-
pact of credit rating announcements will be examined on an individual basis, thereby allowing
to determine the impact of each credit rating agency on the volatility of Euro during the sov-
ereign debt crisis.
To date, this is one of the first papers to study the impact of sovereign credit rating an-
nouncements on foreign exchange markets. This paper employs an event-study methodology,
which was used by many academic scholars in the past to examine the following: the impact
of credit rating news on bond and equity markets, as well as, the effect of macroeconomic
public announcements on foreign exchange markets.
Contributions
While the impact of credit rating announcements on stock and bond markets received a great
deal of attention among academic scholars in the past2, existing literature that examines the
relationship between sovereign credit ratings and foreign exchange markets remains scarce.
This work, thus, complements existing research on the role of credit rating agencies in
international financial markets and makes a valuable contribution to understanding the impact
of credit rating news on the volatility of foreign exchange rates. This paper extends existing
literature by examining the relationship of sovereign credit rating events and high frequency
forex markets by focusing on response reaction of the spot exchange rate USD/EUR during
periods of financial distress, thereby allowing to determine the crucial role of credit rating
agencies in international finance.
The remainder of this paper is organized as follows. Section 2 provides background
information and discusses prior research findings. Section 3 describes methodology and
provides an overview of the theoretical framework and empirical strategy. Section 4 describes
data. Section 5 discusses empirical findings, interprets the results, elaborates on the
limitations of the study and provides recommendations for further research. Section 6
concludes.
2 See (Cantor & Packer, 1996), (Brooks, et al., 2004), (Reisen & von Maltzan, 1999), (Elayan, Pukthuanthong-
Le, & Rose, 2007).
M. Karpava: Sovereign credit rating signals and forex markets
4
2 Background information and literature review
This section provides a thorough description of credit ratings industry discusses the impact of
credit rating announcements on financial markets and sheds a light on previous research find-
ings relevant to the objectives of this paper.
2.1 Credit rating industry
Credit ratings industry is dominated by the three leading credit rating agencies, which include
Standard and Poor’s (S&P) [you may want to change this to (S&P) for clarification of posses-
sive pronoun], Moody’s Investor Service and Fitch Ratings. Rating agencies assign a grade to
the bond issuer according to the relative probability of default, which is measured by the
country's political and economic fundamentals. Credit rating agencies distinguish between an
investment grade rating, which varies from “AAA” (Fitch’s and S&P's) or “Aaa” (Moody's)
to “BBB-“(Baa3), and a speculative grade rating, or “junk” bond rating, which consists of
high yield bonds, where higher interest rates on debt serve as a compensation for greater
amount of risk associated with lending to the sovereign with questionable creditworthiness.
Speculative grade conveys the scale range from “BB+” (Ba1) and below up until “D” (Fitch
and S&P's) or “C” (Moody's). Even though the three credit rating agencies use different rating
scales of measurement, there is a high degree of correspondence between them. A table with a
corresponding explanation of rating grades, assigned by each CRA, is listed in the Appendix.
Credit ratings tend to differ among CRAs, which can be mainly attributed to different estima-
tion methodologies and proxy variables, considered in the analysis. Background information
and discussion of major differences in default probability assessment will follow below.
Standard and Poor’s Ratings is the world’s largest credit rating agency, which dates back to as
early as 1860 when Henry V. Poor’s “History of railroads and canals of the United States”
book was released. Mr Poor is one of the early proponents of making financial information
publicly available to potential investors. In 1941 Poor’s publishing company merged with
Standard Statistics, forming Standard & Poor’s. Today S&P is a subsidiary of McGraw-Hill
Financial, which comprises of S&P Equity Research, S&P Valuation and Risk Strategies, and
S&P Indices. The company is headquartered in New York and reported a combined revenues
of 2.9 billion USD in 2010 in accordance with the S&P’s statistics data obtained from the of-
ficial website.
Moody’s Investor Service is another US-based credit rating agency, headquartered in New
York. It was founded in 1909 by John Moody with an objective to publish statistics manuals
of stock and bond ratings. Today it is a subsidiary of Moody’s Corporation, which also in-
cludes Moody’s Analytics. Combined revenues equalled to 2.03 billion USD in 2010 accord-
ing to the statistics data provided on the company’s website.
Fitch Ratings is the smallest of the big three credit rating agencies, which traces its history
back to 1913 when John K. Fitch founded the Fitch Publishing Company in New York City.
The company’s main objective was to provide financial statistics on stock and bond ratings. In
1924 the company first introduced the ordinal scale of “AAA” through “D” format, used up
until this day. Fitch Ratings was the first among the big three to get recognition from the Se-
curities and Exchange Commission as a nationally recognized rating organization (NRSRO)
in as early as 1975. Today Fitch Ratings is a part of the Fitch Group, which is dual-
headquartered in London, UK and New York, USA and jointly owned by FIMALAC and
M. Karpava: Sovereign credit rating signals and forex markets
5
Hearst Corporation. According to the statistics data3, provided on the official website, Fitch
Ratings reported revenues of 487.3 million Euros (656.9 million USD) in 2010.
Getting back to the differences in credit default assessment among the big three CRAs, S&P
focuses mostly on the forward-looking probability of default. Moody's bases its rating deci-
sions on the expected loss, which is a function of both the probability of default and the ex-
pected recovery rate. Finally, Fitch takes into consideration the probability of default and the
recovery rate, as well.
One more reason that explains differences in credit ratings across the CRAs is the lack of pub-
licly available information as to how CRAs assign weights to each variable they consider in
assessment of the credit risk. Generally, the following factors are taken into consideration by
the three CRAs when making credit risk assessment: economic and political factors, fiscal and
monetary indicators and debt burden.
2.2 Role of credit rating agencies in financial markets
Sovereign credit ratings are perceived by the market participants as important indicators of a
country risk, future economic development and financial stability. Hence, a rating downgrade
will generally affect the country's financial austerity policies by raising corporate taxes to be
able to afford borrowing at a higher cost, thereby reducing corporate cash flows and pushing
down stock prices that might shatter investor confidence and eventually lead to a massive sell-
off of the domestic currency, contributing to local currency depreciation. Therefore, the
market impact of negative credit rating events might have a detrimental effect on the
economic development of a sovereign and its financial market structure.
Previous studies on sovereign credit ratings find that rating events convey important
information to financial markets4. The asymmetric effects of credit ratings have been studied
explicitly by many researches ( (Brooks, Faff, Hillier, & Hillier, 2004), (Kim & Wu, 2011),
(Kräussl, 2005)), reporting that only rating downgrades and negative outlooks have
economically and statistically significant effects on debt and equity markets in contrast to
rating upgrades and positive outlooks, which have weak or insignificant impact on financial
markets. Several research papers also find evidence of strong contagion effects5 of watch and
outlook changes on stock, bond and CDS markets of nearby countries ( (Gande & Parsley,
2005), (Ferreira & Gama, 2007), (Ismailescu & Kazemi, 2010)). Cantor and Hamilton (2004)
and Alsati et al. (2005) find evidence that rating events are good predictors of future dynamics
of sovereign credit rating announcements, as they shed a light on which government bond
issuers are likely to default on their debt or to be downgraded in the foreseen future.
Spillover effects refer to short-term contagion across countries and financial markets. This
topic has been studied extensively by many researchers. Most studies report significant
spillover effects across sovereign ratings (see (Gande & Parsley, 2005), (Arezki, Candelon, &
Sy, 2011), (Ismailescu & Kazemi, 2010)). Duggar et al. (2009) find evidence that sovereign
3 Fitch Ratings fiscal revenues report: http://www.fimalac.com/regulated-information.html.
4 See (Afonso, Furceri, & Gomes, 2011), (Hooper, Hume, & Kim, 2008), (Hull, Predescu, & White, 2004).
5 Financial contagion effect refers to the situation when small shocks, which affected only a few institutions ini-
tially, or sovereigns in this instance, spill over to other countries, contaminating their financial sectors and
economies. It develops in a similar manner as transmission of a disease in medical terms.
M. Karpava: Sovereign credit rating signals and forex markets
6
defaults spread into other areas of corporate finance, leading to widespread corporate defaults.
Arezki et al. (2011) finds that rating downgrades of near speculative grade sovereigns are
especially contagious across countries and financial markets. Thus, a rating downgrade of
Greece from “A-“ to “BBB+” grade, as announced by Fitch on 8 December 2009, resulted in
substantial spillover effects across members of the EMU: 17 and 5 basis points growth in
Greek and Irish CDS spreads.
Afonso, Furceri and Gomes (2011) find evidence that rating downgrades of lower rated
countries have strong spill-over effects on higher-rated sovereigns in the region, which is
consistent with prior studies by Gande and Parsley (2005), and Ismailescu and Kazemi
(2010). Afonso, Furceri and Gomes (2011) also report statistically significant persistence
effects of rating announcements: recently downgraded (upgraded) countries (within a one
month period) tend to have at least 0.5% higher (lower) sovereign bond yields for the next six
months until the effect disappears. Surprisingly, rating announcements by Moody's tend to
have the strongest persistence effect of 1.5% higher bond yield spread for the next six months,
following a rating downgrade.
2.3 Accuracy of sovereign default assessment
Credit rating agencies were created with an objective to solve information asymmetry
problems in international financial markets as they provide an assessment of a government's
ability and willingness to repay its sovereign debt in a timely manner and fulfill its
obligations. The role of credit rating agencies in the global financial system as well as quality
of their credit risk assessment has been widely debated.
Credit rating agencies have often been criticized for violating their primary function of
minimizing information uncertainty in financial markets. In line with this conclusion, Carlson
and Hale (2005) found that the existence of credit rating agencies increases the incidence of
multiple equilibriums, which would have been unique otherwise. Bannier and Tyrell (2005)
report that a unique equilibrium can be restored by revealing more information to the public,
thereby making a credit rating process more transparent, and thus enabling market participants
to make independent assessment of quality and validity of credit ratings. The more accurate
credit rating announcements are, the greater efficiency of investor decisions, hence, the better
the market outcome is.
Credit rating agencies have also been criticized for lagging the market by publishing their
rating announcements ex post. Prior studies suggest that credit ratings are mostly determined
by country-specific economic fundamentals6. These studies report that sovereign credit ratings
are primarily affected by the following economic indicators: GDP per capita, GDP growth,
public debt as a percentage of GDP, budget deficit as a percentage of GDP and inflation level
within the country. A history of sovereign default, a level of economic development and
government effectiveness within the country have been identified as important in determining
sovereign credit ratings ( (Afonso, Gomes, & Rother, 2011), (Cantor & Packer, 1996)). A
recent study by Depken and Lafountain (2006) finds evidence that corruption has statistically
and economically significant impact on a sovereign's creditworthiness.
6 See (Afonso, 2003), (Afonso, Gomes, & Rother, 2011), (Cantor & Packer, 1996), (Bissoondoyal-Bheenick,
2005).
M. Karpava: Sovereign credit rating signals and forex markets
7
Numerous studies were undertaken to test whether credit ratings provide accurate and timely
assessment of a sovereign's credit risk and willingness to repay its debt. These studies
employed econometric models, based on credit rating determinants, discussed above, and
compared predicted ratings with actual credit ratings. Empirical evidence on whether the
ratings are sticky or procyclical has been mixed. Ferri, Lui and Stiglitz (1999) found evidence
supporting the hypothesis that credit ratings are indeed procyclical. Packer and Cantor (1996)
found that before the Asian financial crisis credit ratings were higher than those based on the
model of economic fundamentals, while ratings after the crisis were much worse than the
model predicted, thereby confirming the hypothesis that credit ratings are procyclical. Kräussl
(2005), however, found that the ratings agencies did not initiate boom or bust cycles in
developing countries, thereby rejecting the idea of procyclical nature of credit ratings. Mora
(2006) found evidence that ratings are rather sticky than procyclical, implying that credit
events did not exacerbate the Asian financial crisis as opposed to a widespread view.
Credit rating agencies have also been criticized for being unable to forecast financial crises.
Reinhart and Rogoff (2004) found that all three major CRAs consistently failed to predict
currency crises, whereas almost 50% of all defaults on public debt were linked with currency
crises. Bhatia (2002) claims that failed ratings stem from CRAs' inclination towards ratings
stability rather than accuracy of reported announcements, which implies that there is always a
trade-off between accuracy and stability. Rating failures were exceptionally apparent during
Russian and Argentinian crises. (Bhatia, 2002) If credit ratings were good predictors of future
movements in financial markets, expansionary periods would be characterized by foreign
investment, while during contractionary periods accurate rating assessments would help to
reduce capital outflows and finance local recession with lower interest rates (Elkhoury, 2008).
2.4 Sovereign credit ratings and financial crisis
Since the breakout of the real estate bubble in the United States in the summer of 2007,
European economies have been hurt badly by banking and sovereign debt crises. Most
European member states suffered from fiscal deficits, preventing them from meeting the
desired level of Maastricht criteria for fiscal and monetary stability: public debt as a
percentage of GDP below 60% and budget deficit as a percentage of GDP below 3%. For
example, according to the annual statistics data, provided by OECD7, public debt levels in
Italy of 106.8% in 2009 and 109.0% in 2010 and Greece of 127% in 2009 and 147.8% in
2010 far exceeded its GDP in the corresponding years. In addition to this, credit default swap
spreads experienced increased volatility during the financial crisis, resulting in a dramatic
CDS premia growth (in terms of basis points) from January 2008 to June 2010: 850% in
France, 614% in Germany, 3364% in Greece and 1394% in Spain (Deb, et al., 2011).
In addition to this, many European private banks invested massively in government bonds,
issued by the troubled countries, which now makes the return on these investments highly
uncertain. According to the data, published by the Bank of International Settlements8, at the
end of the third quarter in 2011 German banks held 473.91 billion USD, while French banks
held 617.2 billion USD in troubled foreign debt of affected countries within the Eurozone.
Therefore, even partial default of Italy or Spain (largest shares of German and French funds
7 OECD official website: http://stats.oecd.org/.
8 Bank of International Settlements website: http://www.bis.org/statistics/index.htm.
M. Karpava: Sovereign credit rating signals and forex markets
8
were allocated to these countries) would significantly hurt banking industry in the European
Union. As if situation was not bad enough, on January 13, 2012 S&P's announced credit
rating downgrades for nine EU-member states, including France, which lost its “AAA” rating
to “AA+”, while Portugal and Cyprus were assigned junk-bond ratings. In addition to this, 14
countries within the EU were given negative outlooks, whereas only Germany's premium
credit rating remained unaffected. On 27 February 2012 S&P's assigned “SD” grade to Greek
sovereign bonds, thereby increasing the magnitude of investor risk aversion towards European
financial markets as well as the domestic currency.
De Broeck and Guscina (2011) studied the impact of European sovereign debt crisis on
government debt issuance in the Euro-area. They found economically and statistically
important evidence, pointing out to a drastic shift from fixed-rate Euro-denominated bonds
with long-term maturities towards foreign currency denominated debt with shorter maturities
and floating interest rates. In the view of large fiscal deficits and massive credit rating
downgrades within the Euro-area, a shift from local-currency bond issuance towards foreign
currency denominated debt reflects another important aspect of the European sovereign debt
crisis, namely the loss of confidence in solvency of EU-member states, which has a direct
impact on value of the local currency.
Hooper et al. (2008) and Brooks et al. (2004) examined the impact of credit rating events on
stock markets and provided insights into responsiveness of the foreign exchange market to
sovereign credit signals. They found that the most apparent reactions occur during periods of
financial distress. A recent paper by Treepongkaruna and Wu (2008) tested empirically the
impact of sovereign credit rating news on volatility of stock returns and currency markets
during the Asian Financial Crisis. Both market measures were found to be strongly affected
by changes in sovereign credit ratings with currency markets being more responsive to credit
rating news, while changes in sovereign outlooks had much stronger impact on stock price
volatility rather than actual rating announcements. Research findings also indicate the
presence of statistically significant rating spillover effects from an event-country on markets
of nonevent-countries.
The first empirical study, however, to test the impact of sovereign credit ratings on foreign
exchange markets was completed by Alsakka and ap Gwilym (2012). The event study results
revealed that currency markets of higher-rated countries are more responsive to rating
downgrades during the crisis period, while lower-rated countries' exchange rates are mostly
affected in the pre-crisis period sovereign rating. Market reactions to credit rating changes and
contagion effects are particularly strong during the crisis period (2006-2010) compared to the
pre-crisis period (2000-2006) (Alsakka & ap Gwilym, 2012).
Previous research also suggests that equity markets are significantly affected by changes in
exchange rates (Phylaktis & Ravazzolo, 2005). Since stock markets are particularly
responsive to sovereign credit rating announcements and changes in stock market spreads are
triggered by exchange rate turbulence, then it would make sense to study directly the impact
of sovereign credit rating events on foreign exchange markets. Chung and Hui (2011) find
that increased default probability is positively correlated with exchange rate movements.
Therefore, in this paper the author expects to find statistically significant impact of sovereign
credit rating news on the mean and volatility of the spot exchange rate.
M. Karpava: Sovereign credit rating signals and forex markets
9
3 Methodology
This section provides a detailed description of the empirical strategy, underlying estimation
methods and regression model specification.
3.1 Exchange rate determination
Clearly, foreign exchange rate movements are dependent on many different factors ranging
from fiscal and monetary policies of a sovereign, economic fundamentals of a country (that
include but are not limited to budget and trade deficits, inflation rate and GDP growth), politi-
cal conditions, different macroeconomic shocks as well as psychological perceptions of cur-
rency traders. There are numerous theories on exchange rate determination that take into con-
sideration the impact of some of these variables on volatilities of foreign exchange rates. The
monetary model of exchange rate determination is a notable example, which will be closely
examined in this section.
The monetary model assumes that domestic and foreign bonds are perfect substitutes. It also
makes an assumption that exchange rate movements are determined by the changes in relative
demand and relative supply of money. In accordance with Copeland’s (2008, p. 205) notation
domestic money market equilibrium is given by the following equation:
, (1)
where is the natural logarithm of money stock, is the natural logarithm of price
level, is the natural logarithm of real output and is interest rate; and are positive con-
stants.
Money market equilibrium in the other country is shown in the linear equation below:
. (2)
The monetary model also assumes that purchasing power parity (PPP) holds, which is indicat-
ed by the following equation:
, (3)
where is the natural logarithm of the spot exchange rate (domestic currency per unit
of foreign currency). By combining equations (1), (2) and (3), the flexible-price monetary
model of exchange rate determination takes the following form:
( ) (
) ( ). (4)
Since nominal interest rate is composed of real interest rate and expected inflation rate:
, (5)
the expectations of future inflation rates can replace nominal interest rates by assum-
ing that real interest rates are equal in both countries:
. (6)
M. Karpava: Sovereign credit rating signals and forex markets
10
By plugging equation (6) in equation (4), flexible-price monetary model takes the following
form9:
( ) (
) (
) . (7)
Equation (7) implies that domestic money stock is positively related to the spot exchange rate:
an increase in the domestic money supply will result in domestic currency depreciation as
more units of the domestic currency will be required to purchase one unit of foreign currency.
An increase in domestic real income will cause the domestic currency to appreciate, given the
negative sign of the estimated coefficient. Lastly, nominal interest rates are said to reflect in-
flation expectations, therefore higher interest rates are associated with higher inflation level in
the future. As a matter of fact, as interest rate rises, the value of the domestic currency will
fall.
Since this paper is constrained to the application of daily data, nominal interest rate differen-
tial is the only variable that satisfies this condition. Real output, money supply and inflation
rates are only available on a yearly and quarterly basis. In fact, real interest differential has
proven itself to be a better proxy for general forex market movements; however, inflation sta-
tistics is not available on a daily basis, therefore, the possibility of using the Dornbusch-
Frankel real interest rate model as a benchmark must be foregone. Prior studies suggest that
evidence on the performance of the nominal interest differential is mixed. In the majority of
cases, however, an increase in interest rate differential led to a domestic currency deprecia-
tion, which is consistent with the assumptions of the flexible-price monetary model, discussed
above. Therefore, nominal interest rate differential (based on the fact that it reflects inflation
expectations) will be one of the control variables used in the regression analysis section of this
paper.
A recent monthly report by Deutsche Bundesbank (2010), which examined nominal exchange
rate movements during the financial crisis, used nominal interest rate differential in first dif-
ferences to control for the effect of general currency markets developments. The results are
statistically significant for USD/EUR exchange rate: changes in interest rate differential are
positively correlated with the spot exchange rate, implying that an increase in increase in in-
terest rate differential results in the domestic currency depreciation. In addition to this, the pa-
per also highlights statistical importance of another control variable, namely the Chicago
Board Options Exchange Volatility Index (VIX), to explain forex market movements during
the financial crisis. The volatility index is used as a proxy for global investor uncertainty lev-
el. The estimation method that allows to control for currency market developments and global
investor risk simultaneously is specified as follows:
(
) . (8)
This estimation method is particularly appropriate given the objectives and constraints of this
paper. As a matter of fact, the estimation equation (8) will be used as a benchmark regression
to explain nominal exchange rate movements during the European sovereign debt crisis by
controlling for general forex market developments and global uncertainty level among curren-
cy traders. Corresponding adjustments to equation (8) will be made.
9 See (Bilson, 1978), (Frenkel, 1976).
M. Karpava: Sovereign credit rating signals and forex markets
11
3.2 Event study methodology
The application of event study methodology has become nearly the benchmark research
method when studying responsiveness of financial markets to different macroeconomic an-
nouncements. This paper, however, uses a slightly different approach from the conventional
event study methodology. The empirical framework, used in this paper, offers more flexibility
and allows to gain valuable insights in the core problem, discussed in this paper.
Event study methodology is mostly applied in research papers, which study the impact of pub-
lic announcements on stock returns. This estimation method was initially introduced by Fama,
Fisher, Jensen and Roll (1969), however, over time event study methodology had been ex-
tended to examine the impact of macroeconomic announcements on other financial markets,
including bonds, options, credit default swaps, commodities and currency markets.
Numerous market efficiency studies were undertaken to determine whether foreign exchange
markets are efficient. For example, Frenkel (1981), Ito and Roley (1987) and Hardouvelis
(1988) attempted to study whether foreign exchange markets are semi-strong-form efficient
by utilizing event study methodology to identify the impact of public announcements on
changes in foreign exchange returns.
Two notable examples of the application of event study methodology in foreign exchange
markets in the 1980s are research papers by Sheffrin and Russell (1984) and Cosset and
Doutriaux de la Rianderie (1985). The first paper analyzed whether announcements of North
Sea oil discoveries had any impact on the appreciation of British pound sterling, however, no
evidence was found to support the hypothesis. The second paper focused on the research
question of whether announcements related to changes in the business environment had any
impact on currency markets. The results of their study found significant evidence of the rela-
tionship between the variables under consideration.
Generally, traditional event study methodology is set up in the following manner. After defin-
ing the event of interest, one is supposed to identify the event window, over which prices of
certain securities will be examined. The next step is to define the estimation window. When
working with daily data, at least 120 trading days prior to the event window are selected. In
order to evaluate the impact of the event under consideration, abnormal returns (AR) are cal-
culated with the help of the following equation:
[ ], (9)
where represents an abnormal return at time t, stands for the actual return at
time t, and [ ] is the expected return, given event occurs under normal conditions.
The conventional event study methodology, described above, is not particularly suitable in
this paper due to a relatively short period of time under consideration (from 1 January 2009
through 20 April 2012) as well as the problem associated with credit rating announcements’
clustering around certain dates included in the sample, which makes the identification of an
estimation window (that allows to estimate expected returns against which actual returns are
compared) highly problematic and inconsistent. The main obstacle is that credit rating events
are not spread out evenly over the sample period, ranging from 4 days to 6 months between
rating announcements. Taking into account the relatively short period of time included in the
sample, elimination of any credit rating event from consideration may result in the loss of
valuable information, leading to biasedness of the estimated coefficients and invalid infer-
ences from the regression analysis. Therefore, a certain adjustment is needed to the conven-
M. Karpava: Sovereign credit rating signals and forex markets
12
tional model specification. A detailed description of the regression model specification will be
discussed later on in the paper.
3.3 Motivation for the application of EGARCH model
To fulfill the objectives of this paper, volatility models are considered. Previous research on
currency market responses to public macroeconomic announcements suggests that forex mar-
ket reactions are reflected not only in the mean value fluctuations, but also in the conditional
variance of the spot exchange rate ((Bond & Najand, 2002), (Jansen & De Haan, 2005)).
ARCH-models, in their turn, allow to determine how a set of certain regressors affects condi-
tional mean and variance of the dependent variable. Since conventional ARCH-model is typi-
cally a subject to a number of different constraints, a GARCH (1,1) model is considered to be
a more plausible extension as it is more parsimonious and avoids overfitting10
.
GARCH(1,1) model is widely applied by many researchers when working with financial data.
The GARCH-model was initially developed by Bollerslev (1986). The model allows the vari-
ance of the regressand to be dependent upon its own lags. Under certain restrictions it is pos-
sible to prove that an infinite-order ARCH model is equal to a GARCH (1,1) model. Accord-
ing to Engle (2002), Bollerslev’s modification of the ARCH model is one of most robust ex-
tensions of volatility models.
One of the most useful characteristics of autoregressive conditional heteroscedasticity process
is that it allows to adjust for leptokurtosis, meaning that it takes into consideration the magni-
tude of extreme returns, which are rather common when working with financial data. Thus,
the GARCH model generates a greater number of extreme values than it is expected from a
constant volatility process as incidence of extreme returns is higher during periods of in-
creased volatility.
The estimated equations for the GARCH (1,1) process are specified as follows (Engle R. F.,
2002):
Mean equation: ; (10)
Variance equation: . (11)
The autoregressive conditional heteroscedasticity, , is a function of three terms that include:
• (constant), which is the long-term average conditional variance;
• , which is the ARCH term, measured as the lag of the squared residual from the
mean equation;
• , the GARCH term, which represents last period’s forecasted heteroscedasticity.
The GARCH (1,1) model, however, is a subject to the following non-negativity and non-
stationarity constraints for the variance equation:
>0, 0≤ <1, 0≤ <1, ( + ) <1,
where unconditional variance (UV) =
( ).
10
Overfitting refers to the practice of including more parameters in the model than it is necessary.
M. Karpava: Sovereign credit rating signals and forex markets
13
Given the wide application of GARCH models, there are a number of problems, associated
with this method. First, non-negativity constraints may still be violated. Second, GARCH
models do not allow to account for leverage effects. One of the possible solutions to these
problems, which are especially relevant in this paper, is the exponential GARCH (EGARCH)
model.
EGARCH was originally suggested by Nelson (1991). The variance equation is specified as
follows:
( ) ∑
(
) ∑ |
| ∑
. (12)
The log of the variance implies that leverage effect is exponential rather than quadratic. This
transformation means that values of the conditional variance will be non-negative. The pres-
ence of leverage effects, in turn, can be tested by looking at the sign of the estimated coeffi-
cient, γ. The conditional variance is negatively related to its mean when γ < 0. The impact is
asymmetric when γ ≠ 0.
According to Engle and Ng (1991), the estimated equation (11) can be represented in the fol-
lowing way:
( ) ( )
√ [
√ √
], (13)
where
√ and [
√ √
] terms are used to construct the news impact curve of the
EGARCH model.
Once again, leverage effects are reflected in the value of -coefficient: will be negative
when conditional heteroscedasticity responds asymmetrically to negative shocks. This implies
that the volatility will rise in response to a negative shock and decrease when a shock is posi-
tive. In situations when volatility is sensitive to large shocks, -coefficient will be greater
than zero and statistically significant. In addition to this, -coefficient is likely to exceed -
coefficient in absolute terms as it solely measures the incremental effect of large shocks on
the dependent variable.
Even though the EGARCH model is rather complicated in terms of parameter interpretation,
it has received wide recognition in the modern literature. The model has been applied exten-
sively by many scholars in their research projects. One of the notable examples is a research
paper by Jansen and Haan (2005), where they studied the impact of ECB statements on the
mean returns and volatility of the EUR/USD exchange rate. They applied EGARCH (1,1) es-
timation methodology and found evidence of statistically significant effects of ECB an-
nouncements on the conditional mean and volatility of the spot exchange rate. The research
by Jansen and Haan (2005) encouraged the author of this paper to consider the application of
EGARCH model, as well. The EGARCH (1,1) process has proven to address the objectives of
this paper reasonably well.
M. Karpava: Sovereign credit rating signals and forex markets
14
3.4 Regression model specification
Since this paper employs high-frequency data such as daily spot exchange rates and corre-
sponding 3-month money market rates, new information is absorbed rather quickly. There-
fore, short event windows (ranging from the same day change to 3 days following the an-
nouncement) allow to examine the impact of sovereign credit rating announcements on the
conditional mean and variance of the spot exchange rate USD/EUR over the time period un-
der consideration.
The main purpose of the empirical analysis is to investigate the impact of different credit rat-
ing announcements on the mean of spot exchange rate and volatility of exchange rate, thus,
three separate regressions will be estimated to capture the effects of rating events of each
credit rating agency on the mean returns and volatility of the USD/EUR exchange rate (indi-
rect quotation). Therefore, in order to determine the impact of credit rating announcements,
issued by each credit rating agency, the spot exchange rate (in first differences) is regressed
on a constant term and event variables, which enter the model with the help of binary dummy
variables (0 and 1). The value of “1” is assigned to event days, and “0” to non-event days,
which would be treated as a base group. Since only negative events were issued by the three
agencies (with just one exception of Fitch’s upgrade announcement on 13 March 2012) over
the study period, event dummies are divided into three categories: watch announcements, out-
look announcements and actual downgrades. Previous research suggests including lagged val-
ues of event dummies to examine the effect of public announcements on the conditional mean
and variance of the dependent variable the next day following the announcement. In line with
Nelson’s (1991) research paper, residuals are assumed to follow general error distribution
(GED); therefore the corresponding adjustments were made to incorporate this assumption.
Given the objectives of the paper, the following mean equation will be specified:
( ) , (14)
where represents a change in the natural logarithm in the spot exchange rate USD/EUR
(indirect quotation, that is EUR per 1 USD).
An interest rate differential, ( ) , represents the difference between 3-month money
market rates (EURIBOR and US LIBOR) on the day of the announcement, which is used here
as a proxy for general forex market developments. Theories on exchange rate determination
often include the real interest differential in the model, which is adjusted for inflation. When
working with daily data, however, inflation rates are not available on a daily basis. Therefore,
nominal interest rate differential enters the equation by making an explicit assumption that it
reflects inflation expectations, as it was discussed previously in exchange rate determination
section. Therefore, higher domestic interest rate implies higher expected inflation in the fu-
ture, which means that the value of the currency will go down, while the demand for foreign
currency will go up. Thus, interest rate differential and spot exchange rate should be positive-
ly correlated: an increase in the interest rate differential leads to a rise in the spot exchange
rate, implying domestic currency depreciation as more Euros will be needed to purchase 1 US
dollar.
Chicago Board Options Exchange Volatility Index, , is used here as a measure of global
investor risk and uncertainty during the period under consideration. This index is constructed
as the implied volatility of the S&P’s stock index over a 30-day period. There are no re-
strictions on the sign of the estimated coefficient as it is supposed to reflect how daily changes
in the volatility index affect both currencies. A positive sign would imply that an increase in
M. Karpava: Sovereign credit rating signals and forex markets
15
the volatility index leads to a US dollar appreciation, while the negative sign of the estimated
coefficient will put Euro at an advantage against US dollar.
and are bivariate event dummy variables at time t (event day) and at time t-k (a num-
ber of days following the event day). A value of “1” is assigned to an event day, while non-
event days represent a benchmark category and have a value of “0”. The number of lags will
be determined based on Akaike and Schwarz information criteria. Since three types of credit
rating events will be taken into consideration, the dummy variables will be divided into three
categories: watch revisions, outlook announcements and credit rating downgrades. The signs
of the estimated coefficients are expected to be positive: any negative event is anticipated to
have a detrimental impact on the value of the domestic currency, leading to a subsequent Euro
depreciation. To incorporate different types of dummy variables, the following regression
equation will be estimated for each credit rating agency:
( ) (15)
The variance equation will take the following form (eq(16)):
( ) ∑
(
) ∑ |
| ∑
( )
.
3.5 Empirical framework
To be able to draw meaningful conclusions from the regression analysis, it is essential to ana-
lyze the behavior of financial data over time. After formulating the testable hypothesis, a
number of different statistical tests must be performed to eliminate the possibility of spurious
regression relationships between dependent and independent variables.
With the methodology employed in this paper, the focus will be on problems associated with
the following:
Heteroscedasticity and autocorrelation in the residuals;
Non-stationarity of time series.
Spurious regressions, which lead to misleading inferences about causal relationship between
the regressor and regressand, are the result of using non-stationary time series in the regres-
sion analysis (Enders, 2010, p. 196). Thus, evaluating the data for possible stochastic and de-
terministic trend processes is an essential procedure in the empirical analysis. Formal unit root
tests will be conducted using Augmented Dickey-Fuller test statistic (Dickey & Fuller, 1981).
M. Karpava: Sovereign credit rating signals and forex markets
16
4 Data and preliminary analysis
The most affected economies in the EMU are referred to as PIIGS, which comprise of Ireland,
Spain and Portugal along with Italy and Greece. These countries are facing rising refinancing
rates in the view of their fiscal deficit problems. As a matter of fact, these tensions around
PIIGS countries put the entire European Monetary Union in danger. As a matter of fact, em-
pirical analysis consists of panel data for PIIGS countries included in the sample, over the pe-
riod ranging from 1 January 2009 through 20 April 2012.
Daily spot exchange rates as well as daily 3-month USD LIBOR series were obtained from
the Federal Reserve Bank of New York. The time series on 3-month EURIBOR rates were
obtained from the Euribor-EBF (European Banking Federation) official website11
. Euribor-
EBF is an independent non-profit organization, which was founded in 1999 with the launch of
Euro to fulfill the purpose of providing timely information on EURIBOR, EONIA and
EUREPO rates. Daily volatility index data was obtained from the CBOE (Chicago Board Op-
tions Exchange) official website12
. CBOE is the largest options exchange in the world and it
was included in the regression analysis as a proxy for global investor risk. Credit rating an-
nouncements, published by Standard and Poor’s, Fitch Ratings and Moody’s, were obtained
from the official websites of each credit rating agency.
Previous research suggests that credit rating agencies did not anticipate the global financial
crisis that started in the late summer of 2007. As a matter of fact, first negative credit rating
announcements were published only in 2009. S&P was the first one to publish negative watch
announcement in the view of Greece’s large budget deficit on January 9th
, 2009. The actual
rating downgrade from A to A- occurred shortly after, namely on January 15th
, 2009. Fitch re-
leased a negative outlook announcement on May 5th
, 2009, followed by an actual rating
downgrade from A to A- on October 22nd
, 2009. Moody’s was the last one to react to rising
budget deficit level in Greece by putting Greece on a negative watch list on 29 October 2009,
followed by a downgrade to A2 rating on 22 December 2009. Taking into account that Greece
was first downgraded at the beginning of 2009 since the outbreak of the financial crisis, the
sample data consists of all credit rating announcements, published by the major credit rating
agencies (S&P, Fitch and Moody’s), starting from January 2009 through April 2012.
The sample period consists of 831 trading days. There were a total of 109 rating announce-
ments, published by S&P, Moody’s and Fitch over the study period under consideration.
Standard and Poor’s published 41 rating announcements, which include 25 rating down-
grades, 16 negative watch lists and 21 negative outlook revisions. Fitch Ratings, in its turn,
published 34 rating announcements, which include 22 actual downgrades, 9 watch negative
reviews, 18 negative outlook revisions and 1 actual upgrade when Greece was assigned a B-
rating on 13 March 2012. Moody’s made 34 public announcements, which consist of 23 actu-
al downgrades, 13 negative watch reviews and 21 outlook revisions. Different number of
credit rating announcements published by each agency is not surprising since S&P’s is known
for its focus on the accuracy of rating announcements in the short-run, which results in a
higher frequency of public announcements over the time period under consideration. Fitch
Ratings and Moody’s, in their turn, assign more value to the stability of sovereign credit rat-
ings, therefore, the number of rating events is considerably lower than that of S&P.
11
Euribor-EBF official website: http://www.euribor-ebf.eu/euribor-org/euribor-rates.html.
12 CBOE official website: http://www.cboe.com/micro/vix/historical.aspx.
M. Karpava: Sovereign credit rating signals and forex markets
17
By looking at daily spot exchange rate movements (graph 1), the Euro depreciated significant-
ly against the US dollar at the beginning of 2009 when series of downgrades for Greek and
Irish government bonds were announced. The situation stabilized in the mid-spring 2009
when the Euro started appreciating as a result of government assistance to financial institu-
tions in the Eurozone. Nonetheless, the EUR/USD exchange rate started moving upwards in
the middle of November 2009 up until the beginning of June 2009, when the Euro reached its
lowest point within the period under consideration (0.8335 Euros per 1 USD according to the
close price on 4 June 2010). This period of extensive Euro depreciation was largely affected
by rising public debt levels in Greece, Ireland, Portugal and Spain, which were reflected in
negative sovereign rating events, published by each credit rating agency within this time
frame. The situation stabilized somewhat after Ireland and Greece were provided with a series
of bailout packages in late 2010 – first half of 2011. The stabilizing effect did not persist
though as more countries of the EMU were getting involved. As a matter of fact, rising fears
among investors of possible spillover effects to other countries within the Eurozone put a sub-
stantial strain on Euro. Looking back at the spot exchange rate movements over this period,
bailout packages did put, in fact, a downward pressure on the value of the domestic currency
as investors were searching for safer investments, thus, putting Euro at a substantial disad-
vantage against Japanese yens, Swiss francs and US dollars. There was a sudden slump in the
value of the domestic currency around the end of 2010 – beginning of 2011, when S&P and
Fitch assigned junk bond rating to Greek government bonds. Euro began depreciating again in
the second quarter of 2011 up until reaching its lowest value in the beginning of January
2012, when a series of negative watch and outlook revisions were announced by S&P and
Fitch, followed by massive downgrade announcements for Eurozone countries in early Janu-
ary 2012.
Figure 4-1: Spot exchange rate movements, January 2009 – April 2012.
0,65
0,67
0,69
0,71
0,73
0,75
0,77
0,79
0,81
0,83
0,85
02
-jan
-09
02
-mar
-09
02
-maj
-09
02
-ju
l-0
9
02
-sep
-09
02
-no
v-0
9
02
-jan
-10
02
-mar
-10
02
-maj
-10
02
-ju
l-1
0
02
-sep
-10
02
-no
v-1
0
02
-jan
-11
02
-mar
-11
02
-maj
-11
02
-ju
l-1
1
02
-sep
-11
02
-no
v-1
1
02
-jan
-12
02
-mar
-12
EUR/USD
EUR/USD
M. Karpava: Sovereign credit rating signals and forex markets
18
5 Empirical results
5.1 Benchmark regression
In this section, the benchmark regression for exchange rate determination, eq(14), is estimated
without dummy variables. Based on univariate tests, interest rate differential and volatility in-
dex time series appear to be non-stationary. The corresponding graphs are listed in the Ap-
pendix.
Both graphs exhibit a clear growth pattern (increasing in the case of changes in interest rate
differential and decreasing in the case of daily changes in the volatility index), therefore these
variables are taken in first differences to get rid of stochastic trends. After taking the first dif-
ferences, the processes become stationary in accordance with ADF test statistics. A detailed
description of ADF test results is listed in the Appendix under the corresponding section.
Thus, all variables are taken in first differences, or integrated of order 1, to avoid spurious re-
gression results. Thus, the benchmark regression takes the following form:
( ) .
The ordinary least-squares regression revealed autocorrelation in the residuals as well as
ARCH-effects. ARCH-effects in the residuals are a result of high volatility clustering, observ-
able on the graph for daily changes in the spot exchange rate. Previous research findings sug-
gest that ARCH effects are quite common when dealing with high-frequency data (Jansen &
De Haan, 2005). A detailed description of statistical tests and graphical representation of the
variables under consideration are listed in the Appendix under the corresponding section.
To account for ARCH effects in the residuals, a GARCH(1,1) model is estimated. An auto-
regressive term of order one, AR(1), is included in the mean equation to make sure there is no
autocorrelation in the residuals. The first order of AR-term was chosen based on Akaike and
Schwarz information criteria. The estimated output is provided in the table below.
Table 5-1-1: GARCH (1,1) estimated results.
Mean Equation
Variable Coefficient Standard Error Prob.
C -0.000021 0.000201 0.9184
( ) 0.033522 0.015559 0.0312
-0.000234 0.000120 0.0525
AR(1) -0.486844 0.029441 0.0000
Variance Equation
C 0.000001 0.000000 0.0017
RESID(-1)^2 0.019191 0.008616 0.0259
GARCH(-1) 0.959656 0.008538 0.0000
Source: Own calculations in Eviews.
In the table above all of the control variables are significant except for the constant term in the
mean equation. Akaike and Schwarz information criteria as well as the value of are rather
high for the model taken in first differences. A detailed description of the model statistics is
listed in the Appendix. In addition to this, diagnostic checks were performed, which proved
that the obtained results are robust. A normal probability plot is listed below.
M. Karpava: Sovereign credit rating signals and forex markets
19
Figure 5-1-1: Normal probability plot of standardized residuals, GARCH(1,1) process.
By looking at the normal probability plot statistics, it is fairly obvious that normality assump-
tions are satisfied. The estimated skewness indicator is insignificantly less than zero, while
kurtosis coefficient is insignificantly different from 3. The value of Jarque-Bera statistic is
equal to 0.7794 with a probability of 0.6773, which is well above the 5%-critical level. There-
fore, the residuals are said to be normally distributed.
Consequently, in the next section the benchmark model will be estimated in first differences
with the inclusion of event dummies. Dummy variables will enter the model in first differ-
ences so that all variables in the model are integrated of order 1. An exponential GARCH
model will be estimated to account for asymmetric responses of the spot exchange rate to
credit rating news.
5.2 Reactions to Standard & Poor’s credit rating announcements
In accordance with the equation(15), discussed previously in the methodology section of this
paper, a compressed version of the estimated equation takes the following form:
( )
,
where event dummies, , are divided into three categories to distinguish between
actual rating downgrades, outlook and watch revisions.
The correlation matrix of independent variables revealed high correlation coefficient between
rating downgrades and outlook revisions (see the corresponding section of the Appendix for a
detailed representation.) As a matter of fact, outlook dummies were excluded from the model
to avoid multicollinearity problems.
Based on Akaike and Schwarz information criteria, only 1-day lagged values of event dum-
mies are included in the model. Higher order lags of event dummies, as well as, forward-
looking values of dummy variables (which were used to test whether forex markets anticipat-
ed credit rating events), were found to be statistically insignificant. The estimated results are
provided in the tables below. Statistically significant explanatory variables are highlighted for
convenience of a reader. A more detailed description of the regression output is listed in the
Appendix under the corresponding section.
Table 5-2-1: Mean equation for exchange rate responses to S&P’s announcements.
Variable Coefficient Standard Error Probability
Constant -0.000147 0.000223 0.5102
( ) 0.032612 0.014243 0.0220
0
10
20
30
40
50
60
70
80
90
-4 -3 -2 -1 0 1 2 3
Series: Standardized Residuals
Sample 1/06/2009 4/20/2012
Observations 829
Mean 0.001096
Median 0.029302
Maximum 3.409698
Minimum -3.830734
Std. Dev. 1.001049
Skewness -0.041484
Kurtosis 3.125218
Jarque-Bera 0.779364
Probability 0.677272
M. Karpava: Sovereign credit rating signals and forex markets
20
-0.000229 0.000149 0.1238
0.004231 0.001876 0.0242
-0.001888 0.002546 0.4582
-0.001559 0.002582 0.5460
0.000215 0.003076 0.9444
Table 5-2-2: Variance equation for exchange rate responses to S&P’s announcements.
Variable Coefficient Standard Error Probability
ω (constant) -3.958062 1.702652 0.0201
α 0.189639 0.070680 0.0073
γ -0.059304 0.053327 0.2661
β 0.637537 0.159975 0.0001
( ) 0.041382 0.063306 0.5133
0.013157 0.006210 0.0341
0.107743 0.489824 0.8259
0.211099 0.516707 0.6829
-0.074169 0.508254 0.8840
-0.094016 0.507843 0.8531
Source: Own calculations in Eviews.
Interpretation
Mean equation
A change in interest differential, ( ) , is statistically significant at 95% confidence in-
terval. The sign of the estimated coefficient is greater than zero, which is in line with the
model assumptions, discussed earlier in the paper. A positive sign implies that 1% increase in
the daily change of interest differential is associated with 3.26% Euro depreciation. Higher
domestic interest rate (3-month EURIBOR) relative to the foreign interest rate (3-month US
LIBOR) reflects higher inflation expectations, therefore, exports are expected to rise and val-
ue of the domestic currency will fall, leading to a subsequent Euro depreciation. High-interest
currencies are believed to attract investors as there are potential gains from currency carry
trades13
. The behavior of investors that motivates their investment decisions, however, chang-
es drastically during periods of financial distress. Low-interest currencies, on the contrary, be-
come more attractive to investors in situations of high uncertainty. This phenomenon is often
referred to as “flight to quality”. As a matter of fact, demand for the US dollar was almost as
high as during the pre-crisis period, owing mostly to its low interest rates. (Deutsche
Bundesbank, 2010) Consequently, upward pressures on the US dollar drove the value of the
Euro down, resulting in a substantial Euro depreciation against the US dollar.
Daily changes in the volatility index, , are only marginally significant (p=0.12). Thus,
an increase in the volatility index, causes the Euro to appreciate (due to the negative coeffi-
13
Currency carry trades refer to the situation when investors tend to borrow funds in low-interest-rate currency
and invest them in higher interest-paying currency. Assuming that transaction costs are insignificant, thereby
making an assumption that uncovered interest rate parity holds, potential benefits from carry trades will be
equal to the interest rate differential ( (Brunnermeier, Nagel, & Pedersen, 2008), (Galati, Heath, & McGuire,
2007)).
M. Karpava: Sovereign credit rating signals and forex markets
21
cient) against the US dollar by 0.023%. A detailed interpretation of the following economic
relationship between daily changes in the spot exchange rate and changes in the volatility in-
dex will be discussed later on in the paper.
Of all event dummies, included in the mean equation, only downgrade announcements by
S&P’s are found to be significant. Since the spot exchange rate EUR/USD enters the model in
direct quotation (Euro per 1 USD), a positive sign of the corresponding coefficient implies
that a downgrade announcement is associated with 0.42% Euro depreciation on the day of the
announcement. This result is statistically significant at 95% confidence interval. This finding
is in line with the assumptions of the model: negative credit rating announcements are ex-
pected to drive down value of the domestic currency.
Variance equation
In the variance equation, β-coefficient is positive, which satisfies the model assumptions and
non-negativity constraints. The coefficients of interest, however, are α and γ. The latter, which
represents leverage effects between the mean and the variance of the spot exchange rate, is
negative but insignificant. The α-coefficient is above zero and statistically significant at 1%
significance level. With α being statistically significant while γ is not, the implication is as
follows: the asymmetric impact of credit rating signals in not important, while the magnitude
of credit rating announcements is.
The volatility index is, thus, the only regressor among explanatory variables, which is statisti-
cally significant at 95% confidence interval. The volatility of the spot exchange rate increases
by 1.32% when the volatility index, which represents an increase in global investor risk, rises
by 1%. This result implies that even small volatility shocks in forex options market cause the
variance of EUR/USD exchange rate to increase substantially.
Diagnostic tests
Figure 5-2-1: Q-Q plot of standardized residuals.
Diagnostic checks confirm that the estimated regression results are robust. There are no
ARCH effects in the residuals. Normality conditions of a classical regression model are satis-
fied. A visual representation of all diagnostic tests, performed on this model, is provided in
the Appendix under a corresponding section. Since all points in the Q-Q plot, listed above, lie
along the straight line, the residuals are said to be white noise.
-4
-3
-2
-1
0
1
2
3
4
-4 -3 -2 -1 0 1 2 3
Quantiles of RESID_GED_AR_1
Qu
an
tile
s o
f N
orm
al
M. Karpava: Sovereign credit rating signals and forex markets
22
5.3 Reactions to Fitch Rating’s credit rating announcements
In accordance with the equation(15), discussed previously in the methodology section of this
paper, a compressed version of the estimated equation takes the following form:
( )
where event dummies, , are divided into three categories to distinguish between
actual rating downgrades, outlook and watch revisions.
Since credit rating downgrades and outlook announcements are highly correlated as it is seen
in the correlation matrix, provided in the Appendix, outlook announcements are excluded
from the model.
Based on Akaike and Schwarz information criteria, only 1-day lagged values of event dum-
mies are included in the model. Higher order lags of event dummies, as well as, forward-
looking values of dummy variables (which were used to test whether forex markets anticipat-
ed credit rating events), were found to be statistically insignificant. The estimated results are
provided in the tables below. Statistically significant explanatory variables are highlighted for
convenience of a reader. A more detailed description of the regression output is listed in the
Appendix under the corresponding section.
Table 5-3-1: Mean equation for exchange rate responses to Fitch’s announcements.
Variable Coefficient Standard Error Probability
Constant -0.000144 0.000218 0.5091
-0.000218 0.000146 0.1342
( ) 0.032200 0.014349 0.0248
0.000564 0.001706 0.7410
0.001431 0.001469 0.3298
0.000584 0.002194 0.7901
0.003149 0.001576 0.0457
Table 5-3-2: Variance equation for exchange rate responses to Fitch’s announcements.
Variable Coefficient Standard Error Probability
ω (constant) -4.730256 1.444022 0.0011
α 0.184546 0.072248 0.0106
γ -0.032387 0.053286 0.5433
β 0.564955 0.135638 0.0000
0.016831 0.005784 0.0036
( ) 0.075496 0.070578 0.2848
0.418265 0.404004 0.3005
-0.532523 0.500829 0.2877
-1.092102 0.947344 0.2490
-1.270290 0.791432 0.1085
Source: Own calculations in Eviews.
M. Karpava: Sovereign credit rating signals and forex markets
23
Interpretation
Mean equation
The results for control variables in the mean equation are somewhat similar to the results ob-
tained for S&P’s rating announcements. The volatility index again is only marginally signifi-
cant, leading to 0.022% Euro appreciation when changes in global investor uncertainty rise by
1%.
Daily changes in interest differential are statistically significant at 5% level. The sign is posi-
tive, which means that 1% increase in interest differential leads to 3.22% Euro depreciation.
Therefore, despite being high-interest paying currency, the demand for Euro was falling dur-
ing the period under consideration, resulting in the drop-down in value of the domestic cur-
rency against the US dollar. Historically, US dollars and Swiss francs were favored by the
majority of investors in times of financial distress and currency crises due to their low-interest
rates and greater liquidity. As a matter of fact, these two currencies are often referred to as
“safe havens”.
Of all dummy variables in the mean equation, only watch announcements have a significant
impact on the conditional mean of the spot exchange rate, resulting in 0.32% Euro deprecia-
tion the next day following the announcement. This effect is statistically significant at 5%
level. Interestingly, exchange rate responds in the expected manner only next day after the
announcement. This might be an indication of the fact that foreign exchange markets do not
absorb this type of information immediately, but rather react with a delay to Fitch’s negative
watch revisions.
Variance equation
In the variance equation, β-coefficient is positive and statistically significant at 1% level,
which satisfies the model assumptions and non-negativity constraints. The coefficients of in-
terest, however, are α and γ. The situation with signs and significance of both coefficients is
very similar to what was obtained in the estimated output for S&P’s rating announcements.
Since α is statistically significant and γ is not, the implication is as follows: the asymmetric
impact of credit rating signals in not important, while the magnitude of rating signals, as well
as changes in the volatility index and interest differential, has the greatest impact on the vola-
tility of the spot exchange rate.
Of all control variables in the variance equation, only the volatility index, , is found to be
statistically significant at 1% significance level. An increase in the global investor uncertainty
(1%) leads to 1.68% increase in the conditional variance of the spot exchange rate. Compared
to the results obtained for S&P’s sovereign rating announcements, the effect of the volatility
index changes on the conditional variance of the spot exchange rate is even stronger.
Diagnostic tests
Diagnostic checks confirm that the estimated regression results are robust. There are no
ARCH effects in the residuals. Normality conditions of a classical regression model are satis-
fied. A visual representation of all diagnostic tests, performed on this model, is provided in
the Appendix under a corresponding section. A normal probability plot is listed below.
M. Karpava: Sovereign credit rating signals and forex markets
24
Figure 5-3-1: Normal probability plot of standardized residuals.
By looking at the statistics, estimated skewness is insignificantly less than zero, while the es-
timated kurtosis is insignificantly different from 3, which satisfies the normality assumptions.
In addition to this, Jarque-Bera statistic is equal to 1.9072 with a probability of 0.3854, which
is well above the 5%-critical value. Therefore, normality assumptions of the regression model
are not violated.
5.4 Reactions to Moody’s credit rating announcements
In accordance with the equation(15), discussed previously in the methodology section of this
paper, a compact version of the estimated equation takes the following form:
( )
where event dummies, , are divided into three categories to distinguish between
actual rating downgrades, outlook and watch revisions.
Actual rating downgrades and outlook announcements are highly correlated as it is shown in
the correlation matrix, provided in the Appendix. Encountering the same multicollinearity
problem for the third time is not surprising though. In the majority of cases, downgrade an-
nouncements are accompanied by negative outlook revisions, therefore high correlation coef-
ficient is well anticipated. As a matter of fact, dummy variables representing outlook an-
nouncements are excluded from the model to avoid multicollinearity problems.
As in the previous two regressions, only 1-day lagged values of event dummies are included
in the model. This decision is based on Akaike and Schwarz information criteria. Higher order
lags of event dummies, as well as, forward-looking values of dummy variables (which were
used to test whether forex markets anticipated credit rating events), were found to be statisti-
cally insignificant. The estimated results are provided in the tables below. Statistically signifi-
cant explanatory variables are highlighted for convenience of a reader. A more detailed de-
scription of the regression output is listed in the Appendix under the corresponding section.
Table 5-4-1: Mean equation for exchange rate responses to Moody’s announcements.
Variable Coefficient Standard Error Probability
C -0.000128 0.000218 0.5575
( ) 0.034288 0.014179 0.0156
-0.000254 0.000153 0.0969
0.001254 0.001698 0.4601
0
10
20
30
40
50
60
70
80
-3 -2 -1 0 1 2 3
Series: Standardized Residuals
Sample 1/07/2009 4/20/2012
Observations 828
Mean 0.016343
Median 0.044442
Maximum 2.823122
Minimum -3.677026
Std. Dev. 1.001297
Skewness -0.085106
Kurtosis 2.837803
Jarque-Bera 1.907165
Probability 0.385358
M. Karpava: Sovereign credit rating signals and forex markets
25
-0.000116 0.001544 0.9402
-0.000293 0.002692 0.9133
-0.000675 0.002012 0.7374
Table 5-4-2: Variance equation for exchange rate responses to Moody’s announcements.
Variable Coefficient Standard Error Probability
ω (constant) -6.082904 2.451447 0.0131
α 0.178461 0.081481 0.0285
γ -0.066823 0.054399 0.2193
β 0.438359 0.230453 0.0572
( ) 0.074510 0.091092 0.4134
0.022398 0.009409 0.0173
-0.098616 0.351093 0.7788
0.123010 0.361194 0.7334
0.423514 0.455933 0.3529
-0.272263 0.475645 0.5670
Source: Own calculations in Eviews.
Interpretation
Mean equation
Daily changes in the volatility index are found to be statistically significant at 10% level. The
sign of the estimated coefficient is negative, meaning that 1% increase in daily changes in the
volatility index results in 0.025% Euro appreciation. Similar results were obtained in the pre-
vious two cases for S&P’s and Fitch’s announcements with the only exception that those re-
sults were just marginally significant. There is a reasonable explanation, however, underlying
negative relationship between daily changes in the volatility index and exchange rate returns.
The Euro is the second most traded currency (after the US dollar), which proved to be one of
the most stable currencies before the outburst of the financial crisis in the summer of 2007.
Therefore, any economic shocks that put a downward pressure on the US dollar, will lead to a
subsequent Euro appreciation.
Changes in interest rate differential are statistically significant at 5% level. The sign of the es-
timated coefficient is greater than zero, which implies that 1% increase in daily changes of in-
terest differential leads to 1.56% Euro depreciation. Apart from “flight to quality” phenome-
non, discussed previously in the paper, investors’ perceptions and expectations affect the
market value of the currency to a substantial degree. Thus, rising budget deficit problems in
Greece, Italy, Portugal and Spain and fears of possible spillover effects to other EMU coun-
tries put a substantial strain on the value of Euro.
Variance equation
In the variance equation, β-coefficient is positive and statistically significant at 1% level,
which satisfies the model assumptions and non-negativity constraints. The situation with signs
and significance of α- and γ-coefficients is somewhat similar to what was obtained in the es-
timated output for S&P’s and Fitch’s rating announcements. With α being statistically signifi-
cant while γ is not, the implication is as follows: the asymmetric impact of credit rating sig-
M. Karpava: Sovereign credit rating signals and forex markets
26
nals in not important, while the magnitude of rating signals, as well as changes in the volatili-
ty index and interest differential, have the greatest impact on the volatility of the spot ex-
change rate.
Of all control variables in the variance equation, only the volatility index, , is found to be
statistically significant at 1% significance level. An increase in the global investor uncertainty
(1%) leads to 2.24% increase in the conditional variance of the spot exchange rate. Compared
to the results obtained for S&P’s and Fitch’s sovereign rating announcements, the effect of the
volatility index changes on the conditional variance of the spot exchange rate is the strongest
in this case.
Diagnostic tests
Diagnostic checks confirm that the estimated regression results are robust. There are no
ARCH effects in the residuals. Normality conditions of a classical regression model are satis-
fied. A visual representation of all diagnostic tests, performed on this model, is provided in
the Appendix under a corresponding section. A normal probability plot is listed below.
Figure 5-4-1: Normal probability plot of standardized residuals.
By looking at the statistics, estimated skewness is insignificantly less than zero, while the es-
timated kurtosis is nearly equal to 3, which satisfies the normality assumptions. In addition to
this, Jarque-Bera statistic is equal to 1.4022 with a probability of 0.4961, which is well above
the 5%-critical value. Therefore, normality assumptions of the regression model are not vio-
lated.
0
10
20
30
40
50
60
70
80
90
-3 -2 -1 0 1 2 3
Series: Standardized Residuals
Sample 1/07/2009 4/20/2012
Observations 828
Mean 0.011361
Median 0.049069
Maximum 2.853497
Minimum -3.555812
Std. Dev. 1.001206
Skewness -0.077178
Kurtosis 2.870321
Jarque-Bera 1.402161
Probability 0.496049
M. Karpava: Sovereign credit rating signals and forex markets
27
5.5 Limitations and implications for further research
Although the results of the empirical analysis are robust, the estimation method, employed in
this paper, is a subject to certain limitations. First of all, the application of exponential
GARCH model allows to account for asymmetric effects of credit rating news, although the
model itself has certain limitations. The application of other, possibly newer, extensions of
GARCH-models that take asymmetric effects into account, could have produced more plausi-
ble results, where a greater number of event dummy-variables is statistically significant.
Next, there are certain limitations associated with the application of event study methodology.
The major disadvantage of event study methodology, however, lies within oversimplified as-
sumptions accompanying this method, namely, it does not take into account other factors,
such as macroeconomic news, which could have occurred on the same day as credit rating an-
nouncements. Therefore, introduction of additional event-dummies to account for relevant
macroeconomic news could have produced different results.
Lastly, the magnitude of rating downgrades was not taken into consideration. Although out-
look and watch revisions are used by market participants to shape their expectations vis-à-vis
future developments in sovereign credit ratings industry, the cases of more than 2-notch
downgrades were rather common over the period under consideration. Bhatia (2002) intro-
duced a concept of rating failures by referring to the instances of three- or more-than-three-
notch downgrades within a 12-month period. Financial markets are expected to exhibit
stronger responses in such cases as unanticipated information, reflected in the magnitude of
rating downgrades, is announced. Hence, one of the implications for further research would
be to consider the impact of rating failures, as well.
M. Karpava: Sovereign credit rating signals and forex markets
28
6 Conclusion
Given the objectives of the paper, the results turned out to be mixed. This paper aimed to ex-
plain nominal exchange rate developments during the European sovereign debt crisis and de-
termine if credit rating agencies had any impact on the increased volatility of the Euro against
the US dollar over the period under consideration (2009 - 2012). In accordance with the re-
sults, an increased volatility of the Euro was strongly affected by nominal interest rate differ-
ential changes. The relationship between these variables is negative, implying that an increase
in nominal interest differential, leads to domestic currency depreciation. This result is con-
sistent with the assumptions of the flexible-price monetary model, where nominal interest rate
differential is said to reflect inflation expectations. Therefore, an increase in interest differen-
tial reflects higher inflation rates, which drive the value of the domestic currency down. An-
other sound explanation of the negative relationship between spot exchange rate develop-
ments and nominal interest differential is “safe haven” considerations. Under high uncertainty
investors show stronger preference to less volatile low-interest paying currencies such as US
dollar, in this case. Therefore, higher interest rates, which are believed to attract investors, are
driving the value of Euro down.
The volatility index turned out to be significant in explaining the returns and volatility of the
EUR/USD exchange rate (direct quotation). Interestingly, the relationship between these vari-
ables turned out to be positive: an increase in the volatility index leads to Euro appreciation,
although the magnitude of this effect on the returns is rather weak. Since the Euro is the
world’s second most traded currency, any changes in investors’ perceptions of risk that result
in US dollar depreciation, put the Euro at an advantage. This finding also owes to the fact that
the Euro was relatively stable before the outburst of the financial crisis in 2007 and managed
to sustain global pressures up until the end of 2008.
The impact of credit rating agencies’ announcements on the volatility of the spot exchange
rate received mixed evidence. The results suggest that downgrade announcements by S&P
have an immediate impact, while markets react with a delay to Fitch’s negative watch revi-
sions. Both events lead to domestic currency depreciation. Immediate impact of S&P’s down-
grade announcements can be attributed to the high reputation of this credit rating agency,
which has a strong impact on financial markets. To date, S&P is the largest and one of the
most reputable credit rating agencies in the world. Another reasonable explanation is a higher
frequency of rating announcements, published by S&P relative to Moody’s and Fitch. There-
fore it is not surprising that S&P is often the first agency to issue rating downgrades in the
sovereign bonds market. According to prior studies, S&P was the first CRA to assign rating
downgrades to sovereigns during sovereign/currency crises. Fitch Ratings, in its turn, is the
smallest among the three CRAs, although judging by the timing of watch revision announce-
ments, it appears to lead the industry in some cases. As a matter of fact, currency markets ex-
hibit a stronger reaction to watch revisions, published by Fitch relative to Moody’s and S&P’s
watch announcements. Actions by Moody’s do not have statistically and economically signif-
icant impact on exchange rate developments. This finding might be attributed to the fact that
Moody’s announcements are often lagging public announcements, made by the two other
CRAs.
Overall, the impact of the three leading CRAs is not as apparent as it was expected. It can be
attributed to certain limitations of event study methodology. Explicitly, event study method-
ology treats credit rating announcements as dominant events, neglecting any macroeconomic
news that could have happened on the same day. Public announcements about rising budget
deficits or inflation expectations tend to have a detrimental impact on the value of the domes-
M. Karpava: Sovereign credit rating signals and forex markets
29
tic currency, as well. Therefore there is a possibility that macroeconomic news cause the cor-
responding downgrade actions by credit rating agencies. Thus, when the actual downgrade
occurs, currency traders could have foreseen it happening and there is almost no immediate
impact. This subject, however, might be an interesting topic for further research.
Next, using a different set of control variables might reveal a completely different picture, de-
pending on the assumptions one makes. Since interest rate differential as well as the volatility
index are used in the regression analysis as control variables, the impact of credit rating an-
nouncements could have been at least partially incorporated in these variables, therefore, the
direct impact on the spot exchange rate might not be as palpable as it was expected. The com-
plexity of financial markets should not be underestimated.
In addition to this, high criticism could have put credibility and accuracy of credit default as-
sessment by the CRAs into doubt. Therefore, there is a possibility that market dependence on
credit rating agencies’ activity has diminished to some extent, favoring individual investor as-
sessments instead. Taking into account that credit rating agencies failed to predict currency
crises in the past, the feasibility of this hypothesis gets stronger. In fact, credit rating agencies
started lowering credit ratings of the affected countries only in the early 2009, although the fi-
nancial crisis unfolded in the summer of 2007.
Lastly, investors’ decisions are largely determined by their perceptions and expectations. Very
often currency traders base their decisions on prevailing sentiments in financial markets rather
than on economic statistics that is publicly available. Actions of market speculators have a
strong impact on market expectations, as well. Therefore, in order to get a thorough explana-
tion, underlying the essence of the relationship between credit rating announcements and de-
velopments in forex markets, one has to consider a great number of different factors, dis-
cussed in this paper. Without making any simplifying assumptions, however, the results of the
empirical study will be incomprehensible.
Acknowledgement
I would like to express sincere gratitude to Professor Andreas Stephan for his valuable sug-
gestions from the early stages of conceptual inception up to this day. I also wish to thank my
family and friends for motivating me throughout this process.
Any remaining shortcomings are my own responsibility.
M. Karpava: Sovereign credit rating signals and forex markets
30
References
Afonso, A. (2003). Understanding the determinants of sovereign debt ratings: Evidence for
the two leading agencies. Journal of Economics and Finance, 27(1), 56-74.
Afonso, A., Furceri, D., & Gomes, P. (2011). Sovereign credit ratings and financial market
linkages: Application to European data. Working Paper No. 1347/June 2011,
European Central Bank.
Afonso, A., Gomes, P., & Rother, P. (2011). Short and long-run determinants of sovereign
debt credit ratings. International Journal of Finance and Economics, 16(1), 1-15.
Alsakka, R., & ap Gwilym, O. (2010). Leads and lags in sovereign credit ratings. Journal of
Banking and Finance, 34, 2614–2626.
Alsakka, R., & ap Gwilym, O. (2012). Rating agencies' credit signals: an analysis of
sovereign watch and outlook. International Review of Financial Analysis, 21(2012),
45-55.
Alsati, M., Katz, M., Leung, E., & Vazza, D. (May 2005). Credit watch and ratings outlooks:
Valuable predictors of ratings behaviour. Standard and Poor's.
Arezki, R., Candelon, B., & Sy, A. (2011). Sovereign rating news and financial markets
spillovers: evidence from the European debt crisis. Working Paper No. 11/68,
International Monetary Fund.
Attinasi, M. G., Checherita, C., & Nickel, C. (2009). What explains the surge in euro area
sovereign spreads during the financial crisis of 2007-09? ECB working paper 1131.
Bannier, E., & Tyrell, M. (2005). Modelling the role of credit rating agencies – do they sprak
off a virtuous circle? Working Paper Series: Accounting and Finance No. 165, W.
Goethe-University, Frankfurt.
Bhatia, A. (2002). Sovereign Credit Ratings Methodology. Working Paper No. 02/17,
International Monetary Fund.
Bilson, J. (1978). The Monetary Approach to the Exchange Rate: Some Empirical Evidence.
IMF Working Paper, 25, 48-75.
Bissoondoyal-Bheenick, E. (2005). An analysis of the determinants of sovereign ratings.
Global Finance Journal, 15(3), 251-280.
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of
Econometrics, 31, 307-321.
Bond, C., & Najand, M. (2002). Volatility changes in European currency exchange rates due
to EMS announcements. Global Finance Journal, 13, 93–108.
Brooks, R., Faff, R., Hillier, D., & Hillier, J. (2004). The national market impact of sovereign
rating changes. Journal of Banking and Finance, 28, 233-250.
Brunnermeier, M., Nagel, S., & Pedersen, L. (2008). Carry trades and currency crashes.
NBER Macroeconomics Annual, 313-347.
Candelon, B., & Palm, F. (2010). Banking and debt crisis in Europe: the dangerous liaisons?
De Economist, 158(1), 81-99.
M. Karpava: Sovereign credit rating signals and forex markets
31
Cantor, R., & Hamilton, D. (2004). Rating transitions and default rates conditional on
outlook. Journal of Fixed Income, 14, 54-71.
Cantor, R., & Packer, F. (1996). Determinants and impact of sovereign credit ratings.
Economic Policy Review, 2, 37-53.
Carlson, M., & Hale, G. (2005). Courage to capital? A model of the effects of rating agencies
on sovereign debt roll-over. Cowles Foundation Discussion Paper No. 1506, Yale
University.
Chung, T.-K., & Hui, C.-H. (2011, November). Crash risk of the euro in the sovereign debt
crisis of 2009-2010. Journal of Banking and Finance, 35(1), 2945-2955.
Copeland, L. S. (2008). Exchange Rates and International Finance (5th ed.). Harlow, Essex,
England: Pearson Education.
Cosset, J. C., & Doutriaux De La Rianderie, B. (1985). Political Risk and Foreign Exchange
Rates: An Efficient-Market Approach. Journal of International Business Studies,
16(3), 21-55.
De Broeck, M., & Guscina, A. (2011). Government Debt Issuance in the Euro Area: The
Impact of the Financial Crisis. Working Paper 11/21, International Monetary Fund.
Deb, P., Manning, M., Murphy, G., Penalver, A., & Toth, A. (2011, March). Whither the
Credit Ratings Industry? Financial Stability Paper No. 9, Bank of England.
Depken, C. A., & Lafountain, C. L. (2006). Fiscal consequences of public corruption:
Empirical evidence from state bond ratings. Public Choice, 126(1-2), 75-85.
Deutsche Bundesbank. (2010, July). Nominal and real exchange rate movements during the
financial crisis. Deutsche Bundesbank Monthly Report, 62(7), 39-55.
Dickey, D., & Fuller, W. (1981). Likelihood Ratio Statistics for Autoregressive Time Series
with a Unit Root. Econometrica, 49, 1057-1072.
Duggar, E., Emery, K., Gates, D., Paulo, S., Lemay, Y., & & Cailleteau, P. (February 2009).
Emerging market corporate and sub-sovereign defaults and sovereign crises:
Perspective on country risk. Moody's Investors Service.
Elayan, F., Pukthuanthong-Le, K., & Rose, L. (2007). Equity and debt market responses to
sovereign credit rating announcements. Global Finance Journal, 18, 47-83.
Elkhoury, M. (2008). Credit rating agencies and their potential impact on developing
countries. United Nations Conference on Trade and Development. Discussion Paper
No. 186.
Enders, W. (2010). Applied Econometric Time Series (3rd ed.). Hoboken, NJ, USA: John
Wiley & Sons, Inc.
Engle, R., & Ng, V. (1993). Measuring and Testing the Impact of News on Volatility. Journal
of Finance, 48, 1749-78.
Fama, E., Fisher, L., Jensen, M., & Roll, R. (1969). The Adjustment of Stock Prices to New
Information. International Economic Review, 10(1), 1-21.
M. Karpava: Sovereign credit rating signals and forex markets
32
Ferreira, M., & Gama, P. (2007). Do sovereign debt ratings news spill over to international
stock markets? Journal of Banking and Finance, 31, 3162-3182.
Ferri, G., Lui, L.-G., & Stiglitz, J. E. (1999, November). The procyclical role of rating
agencies: Evidence from the East Asian crisis. Economic Notes, 28(3), 335-355.
Frenkel, J. A. (1976). Monetary Approach to the Exchange Rate: Doctrinal Aspects and
Empirical Evidence. Scandinavian Journal of Economics, 78, 200-224.
Frenkel, J. A. (1981). Flexible exchange rates, prices, and the role of news: Lessons from the
1970's. Journal of Political Economy, 89, 665-705.
Galati, G., Heath, A., & McGuire, P. (2007, September). Evidence of carry trade activity. BIS
Quarterly Review, 27-41.
Gande, A., & Parsley, D. (2005). News spillovers in the sovereign debt market. Journal of
Financial Economics, 75, 691-734.
Gerlach, S., Schulz, A., & Wollf, G. B. (2010). Banking and sovereign risk in the euro-area.
Discussion Paper 09/2010, Deutsche Bundesbank.
Hardouvelis, G. A. (1988). Economic news, exchange rates and interest rates. Journal of
International Money and Finance, 7, 23-35.
Hooper, V., Hume, T., & Kim, S. (2008). Sovereign rating changes - do they provide new
information for stock markets? Economic Systems, 32, 142-166.
Hull, J., Predescu, M., & White, A. (2004). The relationship between credit default swap
spreads, bond yields and credit rating announcements. Journal of Banking and
Finance, 28, 2789-2811.
Ismailescu, I., & Kazemi, H. (2010). The reaction of emerging market credit default swap
spreads to sovereign credit rating changes. Journal of Banking and Finance, 34, 2861-
2873.
Ito, T., & Roley, V. (1987). News from the U.S. and Japan: which moves the yen/dollar
exchange rate? Journal of Monetary Economics, 19, 255-277.
Jansen, D., & De Haan, J. (2005). Talking heads: the effects of ECB statements on the euro-
dollar exchange rate. Journal of International Money and Finance, 24, 343-361.
Kaminsky, G., & Schmukler, S. (2002). Emerging markets instability: Do sovereign ratings
affect country risk and stock returns? The World Bank Economic Review, 16(2), 171-
195.
Kim, S. J., & Wu, E. (2011). International bank flows to emerging markets: Influence of
sovereign credit ratings and their regional spillover effects. Journal of Financial
Research, 34, 331-364.
Kräussl, R. (2005). Do credit rating agencies add to the dynamics of emerging market crises?
Journal of Financial Stability, 1(3), 355-385.
Larrain, G., Reisen, H., & von Maltzan, J. (1997). Emerging market risk an sovereign credit
ratings. Technical Paper No. 124, OECD Development Centre.
M. Karpava: Sovereign credit rating signals and forex markets
33
Levy, A., & Schich, S. (2010). The design of government guarantees for bank bonds: Lessons
from the recent financial crisis. OECD Financial Market Trends Journal, 2010(1).
Lothian, J. R. (1985). Equilibrium Relationships between Money and Other Economic
Variables. American Economic Review(75), 828-835.
Lucas, R. E. (1980). Two Illustrations of the Quantity Theory of Money. American Economic
Review(70), 1005-1114.
Mishkin, F. S. (1992). Anatomy of a Financial Crisis. Journal of Evolutionary Economics,
2(2), 115-130.
Mora, N. (2006). Sovereign credit ratings: Guilty beyond reasonable doubt? Journal of
Banking and Finance, 30(7), 2041-2062.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns. Econometrica, 59, 347-
370.
Phylaktis, K., & Ravazzolo, F. (2005). Stock price and exchange rate dynamics. Journal of
International Money and Finance, 25, 1031-1053.
Reinhart, C. M., & Rogoff, K. S. (2004, February). The Modern History of Exchange Rate
Arrangements: A Reinterpretation. The Quarterly Journal of Economics, 119(1), 1-48.
Reisen, H., & von Maltzan, J. (1999). Boom and bust and sovereign ratings. International
Finance, 2, 273-293.
Sgherri, S., & Zoli, E. (2009). Euro area sovereign risk during the crisis. Working Paper
09/222, IMF.
Sheffrin, S. M., & Russell, T. (1984). Sterling and oil discoveries: The mystery of
nonappreciation. Journal of International Money and Finance, 3(3), 311-326.
Treepongkaruna, S., & Wu, E. (2008). Realizing the impact of sovereign credit ratings on
stock and currency markets. The European Financial Management Association.
M. Karpava: Sovereign credit rating signals and forex markets
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Appendix
2.1 Credit ratings industry
Table 2-1-1: Comparison of credit rating scales across the big three CRAs.
S&P's Moody's Fitch Description
INVESTMENT GRADE
AAA Aaa AAA Prime grade and the lowest credit risk .
AA+ Aa1 AA+
AA Aa2 AA High grade and very low credit risk.
AA- Aa3 AA-
A+ A1 A+
A A2 A Upper-medium grade and low credit risk.
A- A3 A-
BBB+ Baa1 BBB+
BBB Baa2 BBB Lower-medium grade and moderate credit risk.
BBB- Baa3 BBB-
SPECULATIVE GRADE
BB+ Ba1 BB+
BB Ba2 BB Speculative grade and significant credit risk.
BB- Ba3 BB-
B+ B1 B+
B B2 B Highly speculative grade and high credit risk.
B- B3 B-
CCC+ Caa1 CCC+
CCC Caa2 CCC Poor quality and very high credit risk.
CCC- Caa3 CCC-
CC Ca CC Near or in default, with a possibility of recovery.
SD-D C C-D Lowest quality, in default, low likelihood of recovery.
M. Karpava: Sovereign credit rating signals and forex markets
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5.1 Benchmark Regression
Figure 5-1-2: Interest rate differential time series (level).
Figure 5-1-3: Volatility Index (VIX) time series (level).
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
I II III IV I II III IV I II III IV I II
2009 2010 2011 2012
INT
10
20
30
40
50
60
I II III IV I II III IV I II III IV I II
2009 2010 2011 2012
VIX
-.05
-.04
-.03
-.02
-.01
.00
.01
.02
.03
I II III IV I II III IV I II III IV I II
2009 2010 2011 2012
LN_EX
M. Karpava: Sovereign credit rating signals and forex markets
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Figure 5-1-4: Logarithmic transformation of the spot exchange rate.
Table 5-1-2: Univariate test for interest rate differential (in first differences)
Null Hypothesis: D(INT) has a unit root
Exogenous: Constant
Lag Length: 20 (Automatic - based on AIC, maxlag=20) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.733527 0.0001
Test critical values: 1% level -3.438208
5% level -2.864898
10% level -2.568613 *MacKinnon (1996) one-sided p-values.
Table 5-1-3: Univariate test for volatility index (in first differences)
Table 5-1-4: Estimation output, GARCH (1,1), ARIMA (1,1,1)
Dependent Variable: EX_CHG_DIFF
Method: ML - ARCH
Sample (adjusted): 1/06/2009 4/20/2012
Included observations: 829 after adjustments
Convergence achieved after 37 iterations
Presample variance: backcast (parameter = 0.7)
GARCH = C(5) + C(6)*RESID(-1)^2 + C(7)*GARCH(-1) Variable Coefficient Std. Error z-Statistic Prob. C -2.06E-05 0.000201 -0.102450 0.9184
INT_DIFF 0.033522 0.015559 2.154453 0.0312
VIX_DIFF -0.000234 0.000120 -1.939315 0.0525
AR(1) -0.486844 0.029441 -16.53651 0.0000
Variance Equation C 1.43E-06 4.56E-07 3.142778 0.0017
RESID(-1)^2 0.019191 0.008616 2.227461 0.0259
GARCH(-1) 0.959656 0.008538 112.3979 0.0000
Mean dependent var -3.93E-05 Akaike info criterion -6.616331
S.D. dependent var 0.010399 Schwarz criterion -6.576474
Null Hypothesis: D(VIX) has a unit root
Exogenous: Constant
Lag Length: 9 (Automatic - based on AIC, maxlag=20) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -10.49805 0.0000
Test critical values: 1% level -3.438100
5% level -2.864850
10% level -2.568587
*MacKinnon (1996) one-sided p-values.
M. Karpava: Sovereign credit rating signals and forex markets
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Inverted AR Roots -.49
Source: Own calculations in Eviews
5.2 Reactions to S&P’s announcements
Table 5-2-3: Correlation matrix for independent variables, multicollinearity problem detected.
DOWN_SP OUTLOOK_SP WATCH_SP INT VIX
DOWN_SP 1.000000 0.626701 0.350427 0.063334 0.031146
OUTLOOK_SP 0.626701 1.000000 0.187294 0.036411 -0.014035
WATCH_SP 0.350427 0.187294 1.000000 0.067271 0.010918
INT 0.063334 0.036411 0.067271 1.000000 -0.160650
VIX 0.031146 -0.014035 0.010918 -0.160650 1.000000
Source: Own calculations in Eviews
Table 5-2-4: Estimation output, EGARCH (1,1), ARIMA (1,1).
Dependent Variable: LN_EX_DIFF
Method: ML - ARCH (Marquardt) - Generalized error distribution (GED)
Sample (adjusted): 1/07/2009 4/20/2012
Included observations: 828 after adjustments
Presample variance: backcast (parameter = 0.7)
LOG(GARCH) = C(9) + C(10)*ABS(RESID(-1)/@SQRT(GARCH(-1))) + C(11)
*RESID(-1)/@SQRT(GARCH(-1)) + C(12)*LOG(GARCH(-1)) + C(13)
*INT + C(14)*VIX + C(15)*DOWN_SP + C(16)*DOWN_SP_1 + C(17)
*WATCH_SP + C(18)*WATCH_SP_1 Variable Coefficient Std. Error z-Statistic Prob. C -0.000147 0.000223 -0.658511 0.5102
INT_DIFF 0.032612 0.014243 2.289720 0.0220
VIX_DIFF -0.000229 0.000149 -1.538892 0.1238
DOWN_SP_DIFF 0.004231 0.001876 2.254729 0.0242
DOWN_SP_DIFF_1 -0.001888 0.002546 -0.741785 0.4582
WATCH_SP_DIFF -0.001559 0.002582 -0.603704 0.5460
WATCH_SP_DIFF_1 0.000215 0.003076 0.069781 0.9444
AR(1) -0.470801 0.032552 -14.46285 0.0000 Variance Equation C(9) -3.958062 1.702652 -2.324645 0.0201
C(10) 0.189639 0.070680 2.683078 0.0073
C(11) -0.059304 0.053327 -1.112092 0.2661
C(12) 0.637537 0.159975 3.985233 0.0001
INT 0.041382 0.063306 0.653683 0.5133
VIX 0.013157 0.006210 2.118807 0.0341
DOWN_SP 0.107743 0.489824 0.219963 0.8259
DOWN_SP_1 0.211099 0.516707 0.408547 0.6829
WATCH_SP -0.074169 0.508254 -0.145930 0.8840
WATCH_SP_1 -0.094016 0.507843 -0.185128 0.8531 GED PARAMETER 2.155459 0.176461 12.21496 0.0000
M. Karpava: Sovereign credit rating signals and forex markets
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Mean dependent var -1.86E-05 Schwarz criterion -6.509470
S.D. dependent var 0.010388 Akaike info criterion -6.617757
Inverted AR Roots -.47
Source: Own calculations in Eviews.
Diagnostic tests
Figure 5-2-2: Normal probability plot of standardized residuals.
Table 5-2-5: Test for the presence of ARCH effects (none).
Heteroscedasticity Test: ARCH F-statistic 0.015926 Prob. F(1,825) 0.8996
Obs*R-squared 0.015964 Prob. Chi-Square(1) 0.8995
Test Equation:
Dependent Variable: WGT_RESID^2
Method: Least Squares
Sample (adjusted): 1/08/2009 4/20/2012
Included observations: 827 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 1.003417 0.059414 16.88867 0.0000
WGT_RESID^2(-1) -0.004387 0.034763 -0.126197 0.8996 Mean dependent var 0.999020
S.D. dependent var 1.383056
Akaike info criterion 3.490075
Schwarz criterion 3.501485
Hannan-Quinn criter. 3.494451
Durbin-Watson stat 1.997126
Source: Own calculations in Eviews.
0
10
20
30
40
50
60
70
80
90
-4 -3 -2 -1 0 1 2 3
Series: Standardized Residuals
Sample 1/07/2009 4/20/2012
Observations 828
Mean 0.013382
Median 0.052181
Maximum 2.802080
Minimum -3.803463
Std. Dev. 1.001379
Skewness -0.090410
Kurtosis 2.912492
Jarque-Bera 1.392198
Probability 0.498526
M. Karpava: Sovereign credit rating signals and forex markets
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5.3 Reactions to Fitch’s announcements
Table 5-3-3: Correlation matrix for independent variables, multicollinearity problem detected.
DOWN_F OUTLOOK_F WATCH_F INT VIX
DOWN_F 1.000000 0.627459 0.243267 0.022861 -0.050272
OUTLOOK_F 0.627459 1.000000 -0.012496 -0.010533 -0.025310
WATCH_F 0.243267 -0.012496 1.000000 0.045912 -0.010981
INT 0.022861 -0.010533 0.045912 1.000000 -0.160650
VIX -0.050272 -0.025310 -0.010981 -0.160650 1.000000
Source: Own calculations in Eviews.
Table 5-3-4: Estimation output, EGARCH (1,1), ARIMA (1,1).
Dependent Variable: LN_EX_DIFF
Method: ML - ARCH (Marquardt) - Generalized error distribution (GED)
Sample (adjusted): 1/07/2009 4/20/2012
Included observations: 828 after adjustments
Presample variance: backcast (parameter = 0.7)
LOG(GARCH) = C(9) + C(10)*ABS(RESID(-1)/@SQRT(GARCH(-1))) + C(11)
*RESID(-1)/@SQRT(GARCH(-1)) + C(12)*LOG(GARCH(-1)) + C(13)
*VIX + C(14)*INT + C(15)*DOWN_F + C(16)*DOWN_F_1 + C(17)
*WATCH_F + C(18)*WATCH_F_1 Variable Coefficient Std. Error z-Statistic Prob. C -0.000144 0.000218 -0.660289 0.5091
VIX_DIFF -0.000218 0.000146 -1.497552 0.1342
INT_DIFF 0.032200 0.014349 2.244042 0.0248
DOWN_F_DIFF 0.000564 0.001706 0.330547 0.7410
DOWN_F_DIFF_1 0.001431 0.001469 0.974487 0.3298
WATCH_F_DIFF 0.000584 0.002194 0.266118 0.7901
WATCH_F_DIFF_1 0.003149 0.001576 1.997921 0.0457
AR(1) -0.468061 0.033458 -13.98963 0.0000 Variance Equation C(9) -4.730256 1.444022 -3.275749 0.0011
C(10) 0.184546 0.072248 2.554347 0.0106
C(11) -0.032387 0.053286 -0.607783 0.5433
C(12) 0.564955 0.135638 4.165182 0.0000
VIX 0.016831 0.005784 2.910162 0.0036
INT 0.075496 0.070578 1.069684 0.2848
DOWN_F 0.418265 0.404004 1.035298 0.3005
DOWN_F_1 -0.532523 0.500829 -1.063284 0.2877
WATCH_F -1.092102 0.947344 -1.152804 0.2490
WATCH_F_1 -1.270290 0.791432 -1.605052 0.1085 GED PARAMETER 2.216842 0.186615 11.87920 0.0000
Mean dependent var -1.86E-05 Akaike info criterion -6.628856
S.D. dependent var 0.010388 Schwarz criterion -6.520570
Inverted AR Roots -.47
M. Karpava: Sovereign credit rating signals and forex markets
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Source: Own calculations in Eviews.
Diagnostic checks
Figure 5-3-2: Q-Q plot of standardized residuals.
Table 5-3-5: Test for the presence of ARCH effects (none).
Heteroscedasticity Test: ARCH F-statistic 0.135006 Prob. F(1,825) 0.7134
Obs*R-squared 0.135311 Prob. Chi-Square(1) 0.7130
Test Equation:
Dependent Variable: WGT_RESID^2
Method: Least Squares
Sample (adjusted): 1/08/2009 4/20/2012
Included observations: 827 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 1.011543 0.058612 17.25828 0.0000
WGT_RESID^2(-1) -0.012768 0.034750 -0.367432 0.7134 Mean dependent var 0.998745
S.D. dependent var 1.354931
Akaike info criterion 3.448841
Schwarz criterion 3.460250
Hannan-Quinn criter. 3.453217
Durbin-Watson stat 1.995888
Source: Own calculations in Eviews.
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M. Karpava: Sovereign credit rating signals and forex markets
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5.4 Reactions to Moody’s announcements
Table 5-4-3: Correlation matrix for independent variables, multicollinearity problem detected.
DOWN_M INT OUTLOOK_M WATCH_M VIX
DOWN_M 1.000000 0.072275 0.950018 0.103277 -0.064376
INT 0.072275 1.000000 0.074705 0.022907 -0.160650
OUTLOOK_M 0.950018 0.074705 1.000000 -0.019284 -0.051295
WATCH_M 0.103277 0.022907 -0.019284 1.000000 -0.063697
VIX -0.064376 -0.160650 -0.051295 -0.063697 1.000000
Source: Own calculations in Eviews.
Table 5-3-4: Estimation output, EGARCH (1,1), ARIMA (1,1).
Dependent Variable: EX_CHG_DIFF
Method: ML - ARCH (Marquardt) - Generalized error distribution (GED)
Sample (adjusted): 1/07/2009 4/20/2012
Included observations: 828 after adjustments
Presample variance: backcast (parameter = 0.7)
LOG(GARCH) = C(9) + C(10)*ABS(RESID(-1)/@SQRT(GARCH(-1))) + C(11)
*RESID(-1)/@SQRT(GARCH(-1)) + C(12)*LOG(GARCH(-1)) + C(13)
*INT + C(14)*VIX + C(15)*DOWN_M + C(16)*DOWN_M_1 + C(17)
*WATCH_M + C(18)*WATCH_M_1 Variable Coefficient Std. Error z-Statistic Prob. C -0.000128 0.000218 -0.586528 0.5575
INT_DIFF 0.034288 0.014179 2.418232 0.0156
VIX_DIFF -0.000254 0.000153 -1.659954 0.0969
DOWN_M_DIFF 0.001254 0.001698 0.738605 0.4601
DOWN_M_DIFF_1 -0.000116 0.001544 -0.075009 0.9402
WATCH_M_DIFF -0.000293 0.002692 -0.108876 0.9133
WATCH_M_DIFF_1 -0.000675 0.002012 -0.335283 0.7374
AR(1) -0.470436 0.033355 -14.10381 0.0000 Variance Equation C(9) -6.082904 2.451447 -2.481352 0.0131
C(10) 0.178461 0.081481 2.190208 0.0285
C(11) -0.066823 0.054399 -1.228374 0.2193
C(12) 0.438359 0.230453 1.902157 0.0572
INT 0.074510 0.091092 0.817961 0.4134
VIX 0.022398 0.009409 2.380515 0.0173
DOWN_M -0.098616 0.351093 -0.280883 0.7788
DOWN_M_1 0.123010 0.361194 0.340565 0.7334
WATCH_M 0.423514 0.455933 0.928896 0.3529
WATCH_M_1 -0.272263 0.475645 -0.572408 0.5670 GED PARAMETER 2.145566 0.184505 11.62879 0.0000 Mean dependent var -1.86E-05 Akaike info criterion -6.607340
S.D. dependent var 0.010388 Schwarz criterion -6.499053
M. Karpava: Sovereign credit rating signals and forex markets
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Inverted AR Roots -.47
Source: Own calculations in Eviews.
Diagnostic checks
Figure 5-4-2: Q-Q plot of standardized residuals.
Table 5-4-5: Test for the presence of ARCH effects (none).
Heteroscedasticity Test: ARCH F-statistic 0.151857 Prob. F(1,825) 0.6969
Obs*R-squared 0.152197 Prob. Chi-Square(1) 0.6964
Test Equation:
Dependent Variable: WGT_RESID^2
Method: Least Squares
Sample (adjusted): 1/08/2009 4/20/2012
Included observations: 827 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 1.011429 0.058905 17.17064 0.0000
WGT_RESID^2(-1) -0.013531 0.034724 -0.389689 0.6969 Mean dependent var 0.997870
S.D. dependent var 1.366173
Akaike info criterion 3.465346
Schwarz criterion 3.476755
Hannan-Quinn criter. 3.469722
Durbin-Watson stat 1.995370
Source: Own calculations in Eviews.
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Quantiles of EGARCH_INITIAL_RESID
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