Determinants of forex market movements during the Europe ...532464/FULLTEXT01.pdf · negative...

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.


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


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).


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


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.


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).


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.


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.


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)


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).


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-


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.


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.


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


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).


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.


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


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.


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


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.


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


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)).


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


22

5.3 Reactions to Fitch Rating’s credit rating announcements


paper, a compressed version of the estimated equation takes the following form:

( )



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.


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.


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



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




the Appendix under a corresponding section. A normal probability plot is listed below.


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


paper, a compact version of the estimated equation takes the following form:

( )



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.


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


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




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.


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


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-


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




the Appendix under a corresponding section. A normal probability plot is listed below.


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


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




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.


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-


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.


30

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34

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.


35

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


2009 2010 2011 2012

VIX

-.05

-.04

-.03

-.02

-.01

.00

.01

.02

.03


2009 2010 2011 2012

LN_EX


36

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.


37

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



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


38

Mean dependent var -1.86E-05 Schwarz criterion -6.509470

S.D. dependent var 0.010388 Akaike info criterion -6.617757



Diagnostic tests


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


0

10

20

30

40

50

60

70

80

90

-4 -3 -2 -1 0 1 2 3


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




39

5.3 Reactions to Fitch’s announcements


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



Dependent Variable: LN_EX_DIFF


Sample (adjusted): 1/07/2009 4/20/2012





*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


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




40


Diagnostic checks





Test Equation:



Sample (adjusted): 1/08/2009 4/20/2012









-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3

Quantiles of EGARCH_RESID

Qu

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tile

s o

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41

5.4 Reactions to Moody’s announcements


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



Dependent Variable: EX_CHG_DIFF


Sample (adjusted): 1/07/2009 4/20/2012





*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


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



42



Diagnostic checks





Test Equation:



Sample (adjusted): 1/08/2009 4/20/2012









-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3

Quantiles of EGARCH_INITIAL_RESID

Qu

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