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FORECASTING OF EXCHANGE RATE AND IT'S FUNDAMENTALS USING EURODOLLAR AND POUND
CHAPTER ONE
General Structure of the Dissertation
In this dissertation, chapter one deals with the background of the research, forecasting of
exchange rate and it's fundamentals using euro-dollar and pound. This is aimed at highlighting
the research topic and gives an introduction of the overall dissertation. In this chapter, research
objectives and aims of the research are be given, the research question as well as the limitations
of the research. In chapter two, it is the review of literature, use of secondary sources regarding
the previous researches that were previously conducted by other researchers regarding this
research topic. Chapter three is the methodology chapter. This gives different consideration on
the different methods that have been utilized in data collection, primary and secondary data.
Chapter four will be case study which on the other hand will lead to chapter five that presents the
data and analysis. Chapter six is the recommendation and the conclusion.
Background of the Study
The relevancy of studying the significant of forecasting the exchange rate using the eurodollar and pounds is evident in the current international financial markets. Currently, these
currency are the most important especially in the foreign exchange market, and oscillations of
the euro-dollar as well as pounds exchange rate are so vital not only due to the economic
transactions reasons among most of the major economic blocks in Europe, but also their effects
do still have an impact even to the rest of the world (Brzeszczynski and Melvin 2006). These
currencies are mostly used in among other countries as a medium of exchange as well as the
store of value internationally. Nevertheless, getting a full understanding together with the
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forecasting of the changeability of both the euro-dollar and pounds in the market may not be
regarded as a simple task.
Euro currency first came into the market as currency on January 1999. However, it was
used as a legal tender especially in the retail transactions among customers on January 2002.
This shows that it is only a decade and being specific it’s only fourteen years since the first
introduction while it had been eleven years since the currency started to be used as a medium of
exchange in the foreign markets. Therefore, both the economic agents and the European Central
Bank have been in a continuous learning process with an aim of identifying the mechanism in
which monetary policies are transmitted as well as their impacts on the economic activities.
Additionally, the impact of modification of the exchange rate among the European member’s
countries coupled with the role played by the euro currency in the international currency is
undeniable as indicated in the views conducted by (Posen 2008 and Chinn and Frankel 2008).
Furthermore, the moment in which the euro-dollar series have contributed in the economic
variables among the European Monetary Union, even though minimal, has lengthy impact and
thus posing more challenges to the traditional methods of econometrics which include the
cointegration techniques which demands for a variety of data collection and more studies in
order to unearth the stability and the correlations of these variables. While underpinning this
effects in the international market, it would be of great importance to understand the time-series
econometrics as identified by Mariono and Murasawa (2003) as well as the utilization of the
maximum likelihood analytical factors in the time series which were in the mixed frequencies
(Camacho and Perez-Quiros 2010), and treat them in a quarterly manner and segregate them
according to the monthly series. These mixtures of frequencies will assist in determining the
effects of the macroeconomic variables which are quoted in both the monthly and weekly
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exchange frequencies. Additionally, as the objective of the study is to explain or forecast weekly
or daily exchange rates while considering both the euro-dollar and pounds in the foreign markets,
there is much importance to consider these time series in the international markets combined
with the lengthy of currency fluctuations.
However, most of these studies are much more theoretical only and may not be of much
usefulness in the prediction of the future exchange rate in the international markets. The
variables used to deduce the conclusion in much of the studies have a constant equation with an
identified coefficient, thus lending the studies in most cases inapplicable. In this connection, as
Ehrmann et al. (2005) identified, it would be useful explore the topic while using a structural
model of transmission collected from the daily returns of bonds, stocks, exchange rates as well as
the interest rates in order to understand the actual transmission taking place in the market.
The History of Nigeria Foreign Currency Exchange Rate
Nigeria just like other low income economies in the world, have been struggling to
maintain the stability in the exchange rates. The country had adopted exchange rate strategies
aimed at gaining both the internal and external balance (Umar and Soliu 2013). The exchange
rate policy has been controlled by direct administrative measures which are used to manage the
foreign exchange in Nigeria since 1960. The country has played a very significant role so that the
economy can attain the ultimate productivity capacity (Ogiogio 1996). However, Nigeria since
then there has been regulation made in the market to maintain the stability and naira was much
pegged against the euro-dollar and pound, as a result of devaluation of these currencies
(Oyediron and Afieroho 2013). Foreign exchange rate is mainly referred to the relation that
different countries use different currencies. When there is any impact on the risk of rate in each
country, the monetary influence, the inflation, and the rate of return on investment influence
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foreign exchange rate movement. However, it has been so unclear on whether there is much
advantage in regard to the way the market in Nigeria is organized in respect to the buying and
selling of the foreign exchange because of the filthy float regime that operates within the country
(Umar and Soliu 2013).
The currency exchange rates in the country has been changing from time to time,
basically in a chaotic manner due to the potholed regulation and monitoring system by the
Central Bank of Nigeria (CBN). For instance, between July, 2002 and February, 2004, the naira
depreciated by 11.5 percent against the dollar (Sanusi, 2004). From the CBN statistical report,
the average AFEM intervention rate which closed at 82.33 to a dollar in 1995 appreciated to
81.48 per dollar in 1996. The rate depreciated continuously to 81.98, 84.84 and 91.83 in 1997,
1998 and 1999 respectively. From 1960 until 2011 the USD-NGN exchange averaged 47.25
reaching an historical high of 157.85 in September of 2011 and a record low of 0.53 in October
of 1980 with a depreciation rate of 4.78 percent against the US Dollar during the last 12 months
(Trading Economics, 2011).
Aims and objectives of the research
The main aim and objectives of this paper, the forecasting of exchange rate and it's
fundamentals using euro-dollar and pounds, that are aimed to be achieved by this research
include:

Understanding the concept used by the Nigerian financial institutions to determine the
euro-dollar and pounds exchange rates

To understand how exchange rates settings affect the value of the local currency
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
To understand what fundamentals must be considered before setting the exchange rates in
the country

To understand what effects the exchange rates has on the economy of the country

To understand whether the Nigerian exchange rates settings rhymes with other nations
Research Question (s)
This paper will be looking towards answering the following research questions,

What are the fundamentals of exchange rate using euro-dollar and pound?

How is the exchange rate focused and determined?

Are there any difference in forecasting exchange rates for euro-dollar and pounds and
other currencies?
Secondary Sources
This paper will give a great consideration to what other researchers and scholars in this
field have contributed to the topic through reviewing their literature regarding the research topic.
The research on the other hand has utilized the available publications, both online and offline to
contribute to the research topic and in answering the research question.
Limitations of the research
The limitations for this research include:

Understanding the concept behind the determination of exchange rates and their
fundamentals

Limited time for the research

Gathering information from both the primary and secondary sources
Summary of the chapter
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This chapter has introduced the background of the forecasting of exchange rate and it's
fundamentals using euro-dollar and pound. It has given a highlight on what the other parts of the
dissertation will be working towards fulfilling. This chapter has also explained on how the
information will be sourced despite the limitations involved.
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CHAPTER TWO
Literature Review
Introduction
Understanding and forecasting the exchange rate in the short run is a controversial and
still an evolving issue, which is far from being settled. There is an extremely vibrant debate
pertaining to the exchange rate expectedness especially while using the time series data
(Altavilla and De Grauwe 2010). Most of the empirical models of exchange rate which are in
existence have not been offering consistence outcomes as compared to the huge amount of
empirical papers (Grech 2004). Thus, forecasting of exchange rate, especially in the current
global market characterized by immense integration of the foreign economies across the globe
has been an extremely subtle borne of discussion in the international finance and banking, in
spite of the huge resources dedicated to the issue both academically and even in non-academic
sectors such as financial markets professionalism (Bianco, et al. 2012). It is so unfortunate that
there is no well outlined and anticipated success while predicting this theme. Greenspan (2002)
remarked that, "There may be more forecasting of exchange rates, with less success, than almost
any other economic variable." While the paradigm and the characterization of this concept may
be quite pertinent, it is not supposed to deter us from attempting to explore and identify the
empirical data determinants in the exchange rates, which is an enterprising critical issue of
forecasting the exchange rates (Chinn 2003).
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Forecasting of the exchange rate has raised challenge among the academic profession as
manifested in the work conducted by Meese and Rogoff (1983) in a seminal where they highlight
out the sample of forecasting the exchange rate performance which is poor and there are
structural models for identifying exchange rate with the like of portfolio balance model and
monetary model. Particularly, they show for the post-Bretton Woods floating period that
structural post-sample forecasts of foreign exchange rates among major countries are bettered,
especially in the short-run, by a simple drift less random walk model that does not use any
information on "fundamentals" and forecasts the exchange rate to remain unchanged. This occurs
even though these authors base the forecasts of the structural models of the realized value of the
fundamentals for the forecasting period, giving the structural models an important informational
advantage over the random-walk model (Faust et al, 2003).
An alternative consequent literature shows the strength of these results as seen and
identified in the post-Bretton Woods floating time and is through the use of non-linear
econometric techniques, data periodicity and samples, different currencies (Cheung, et al, 2005).
In accordance with that situation, the challenge of tackling these model exchange rates while
using the fundamental economic variables as well as ensuring that exchange rate are obtained at
a fix rate in both out-of-sample and in-sample so as to surmount the pessimistic and cynical
feeling instilled professionally in the work of Meese and Rogoff (1983) and that the exchange
rates and fundamentals are separated (Frankel and Rose, 1995, p. 1704). That is, solving the
"exchange-rate disconnect puzzle," (Obstfeld and Rogoff, 2000) which has emerged to be a
challenging point in the related literature.
Additionally, by the mid 1990s, some authors accounted that the empirical evidence
pertaining the monetary fundamentals may in the long-run have the predictive power in the
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exchange rate shifts and movements (e.g. MacDonald and Taylor, 1994; Mark, 1995; Chinn and
Meese, 1995; Kim and Mo, 1995). These studies apply long horizon regression approach to
model the relationship between the exchange rate and fundamentals, and although they do not
have short-run predictive power, since they use weekly, monthly, or quarterly data their shorterrun in their prediction. Furthermore these predictions do still have their way and evidence as per
the long-run exchange rate inevitability and predictability. More so these findings and results
were later confirmed with by other authors such as Mark and Sul (2001) even though they are
not seen to be robust (Cheung, et al., 2005) and still are not free or exempted from critics (Kilian,
1999; Berkowitz and Giorgianni, 2001; Boudoukh, et al., 2008). In this case, the forecasting of
the exchange rate has remained unsolved especially in the short-run horizons (Bianco, et al.
2012).
Even though the fundamentals of exchange rate do not seem to be useful in forecasting
short-run returns in the foreign exchange rate, the existence of a connection between
fundamentals and the exchange rates in the short-run is clearly outlined and identified on a
crucial work of Andersen, et al (2003). While using real-time data collected and available, they
found that macroeconomic reports surprises produce quick jumps in the conditional mean of five
US dollar exchange rates from January 1992 to December 1998. Andersen et al. (2007) and
Faust, et al. (2007) confirm this result for the euro-dollar exchange rates. A challenge with the
use of high-frequency data in checking the relationship between exchange rates, which are
quoted second-by-second, if necessary, as well as the economic fundamentals, which includes:
money stocks, prices, are that normally there are no readily available fundamentals in a highfrequency series (Bianco, et al. 2012). Hence, much of the work does utilize the monthly or
quarterly available data as the traditional econometric models do not offer for the empirical test
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of the already existing pertaining the relationship between these exchange rate and the basic
fundamentals using either daily or weekly data, that are usually used as the frequencies of
interest within the international markets among the foreign exchange market practitioners and the
policy makers. In addition, it may also imply that the empirical test in respect to high frequency
models are not possible and there is a need to propose for a stable and more accurate model that
will relate both the fundamentals and the exchange rate (Bianco, et al. 2012). These setbacks are
paramount because in the voluminous foreign exchange (FX) market, foreign currencies are used
to be traded continuously and constantly through the use of dealers who have a well connected
network and mostly located within big centres which do transact huge amount of money all over
the world, and new information in respect to the available and pertinent to economic variables in
most cases influences the rate of exchange irrespective of the frequency at which it is identified.
Thus, there is much more important to study and conduct research that will assist in
understanding the relevancy of economic fundamentals while predicting and explaining the
exchange rate and the use of mixed frequencies data. This is much more important in this study
as it will assist in achieving the objective of this paper.
Euro Exchange Rate and European Nations Debt Crisis
The debt crisis across the entire European nations and the exchange rated among many
countries has been constant as well as remaining extremely unpredictable. Many analysts have
attributed the uncertainty with the evolution observed in the foreign exchange rate, especially the
euro exchange rate, not only to the economic fundamentals but also to the public debate
witnessed within the policy makers on the issue of debt among the European sovereign as well as
identifying the possible remedies to this crisis (Ehrmann, et al 2013). There are huge numbers of
studies that assist in determining how the euro exchange rate is volatile and unstable in the
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international markets, thus causing witnessed a debt crisis in the European Union. The global
financial dispute coupled with a subsequent European debt crisis among countries had a
substantial impact on the foreign exchange rate configured (Fratzscher 2009). While comparing
to the years between 2007 and 2009, Ehrmann, et al noted, there is much more turbulent and
inconsistency in the international exchange rate that has receded globally. However, the
exchange rate of euro as compared to other countries within Europe has also remained volatile
during this period. While comparing with the volatility experienced during the fiscal year of
European Monetary Union (EMU) in 2010/ 2011, there is extreme fluctuations which even
destabilize the euro-dollar exchange rate as well as the British pound while still considering other
countries currencies, such as the yen (2013).
Andersen et al. (2003) show that exchange rates tend to react more eventually and
quickly in respect to the news, that U.S. macroeconomic reports and that timeliness in regard to
the news issues are more likely have more influence as compared to those experienced in their
German and European counterparts. In a similar vein, Faust et al. (2007) argue that the effect of
macro announcements on exchange rates and other asset prices depends on the source of the
shock and on the way it changes the public perception of the state of the economy. These
findings from studies with high-frequency data are broadly confirmed by studies using daily
data, such as Ehrmann and Fratzscher (2005), Johnson and Schneeweis (1994), Kim (1998) or
Kim (1999). Furthermore, Evans and Lyons (2005) emphasize the effect of news on order flow
and show how the response of currency markets to news takes days rather than minutes to fully
work itself out.
There is an also immense literature analysis in regard to the impacts of communication
among policy makers in respect to the exchange rate volatility and other returns. There is
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sufficient evidence that shows the communication from the central banks on the matters relating
to the monetary policy do have effects on the exchange rates: Sager and Taylor (2004) and
Conrad and Lamla (2010) identified this as the case among the ECB’s communication while
Melvin et al. (2009) for the Bank of England’s also illustrated so. Additionally, many of the
studies have documented that much of the oral exchange rate transactions and interventions
greatly affect the exchange rate volatility as well as the returns. Whereas Jansen and De Haan
(2005) only find effects of ECB interventions on the euro’s conditional volatility, Fratzscher
(2006) furthermore finds substantial effects of ECB communications on both the spot and
forward euro-dollar exchange rate returns.
Furthermore, there is additional literature which is of great importance and of high
expectation while studying these effects as there is linked with the current analyses and the
effects of announcements and statements in line with the political issue and the uttered by the
politicians mostly during the convention of the European sovereign debt crisis. Beetsma et al.
(2012) identified a new variable and model of using the Eurointelligence daily news flash as well
as the code of the content in a very fashionable manner that will fit us all. They find that the
amount of news matters as more news tend to augment government bonds distribution especially
in the peripheral and developing countries. Likewise, the content found or identified in that news
is found to be imperative, with irrelevant or bad news trying to explain why there is upward
pressure and moves on spreads and distributions. Correspondingly, Mohl and Sondermann
(2013) create variables related to the politicians’ statements based on the frequency of statements
reported by news agencies, without differentiating their content. Even though there is a much
more correlation between the increase spreads of news and the change or predictability of the
volatile foreign markets, there is still much more arguments on the news statements uttered in
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public in respect to the response of the exchange rate. Mink and de Haan (2012) also compile a
new variable about the European sovereign debt crisis, identified by looking up the news on days
that saw large changes in Greek government bond yields, but avoid the endogeneity problem of
analysing the impact of news on bank stock prices. Much more, Kilponen et al. (2012) make and
compiled more than 50 policy initiatives in connection to the resolution reached upon during the
European sovereign debt crisis, and indicated that many of these influenced and affected
government bond are distributed and spreads, for instance, the resolutions on supporting
packages as well as embracing the EFSF mechanism on average decreased spreads.
Baker, Bloom and Davis (2012) also conducted the related study and complied the
monthly index information available to predict the policy, decision-making issues is economics
which are having uncertainty through the use of inter alia the frequency of news media
references to economic policy uncertainty coupled with the measure of forecaster difference over
prospect government inflation and purchases. In accordance to this study, it has been identified
that their VAR estimates and predictions indicates that an increase or a raise in economic policyrelated ambiguity is mostly followed by significant and persistent declines in U.S. aggregate
dollar output, private investment, and employment. Additionally, while relating to the literature
of financial markets, which has a very crucial role in the forecasting of exchange rate, Afonso et
al. (2012) find a noteworthy response of bond yield spreads to the rating of the changes as in the
case of negative and bad announcements or news, while there is a sufficient evidence pertaining
to contagion and infection among lower-rated countries, and is mostly experienced while
approaching a non-investment grade in respect to a higher-rated countries. Arezki et al. (2011)
focus on the effects of sovereign rating announcements during the European sovereign debt
crisis. Using a VAR analysis, they also conclude that sovereign rating downgrades have
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significant spillover effects across countries, which are particularly strong when the downgrade
refers to countries with a lower investment-grade rating or below. Looking at changes in CDS
spreads, Kiff et al. (2012) find that rating changes and credit warnings do have an impact,
although most of the incremental information value is transmitted through negative credit
warning rather than actual rating changes.
Economic Theory in Forecasting of Exchange Rate
Monetary model
The basic economic theory that laid the ground in the forecasting of the exchange rate is
the monetary model (MM-model) of exchange rate determination (Grech 2004). The empirical
methods in this study have various forms defined by the vector-auto-regressions (VARs). The
empirical methods and the theoretical framework are extensively described extensively by van
Arle et al. (2000) and Cuaresma and Hlouskova (2004).Van Arle et al. (2000) predicts and
approximate a sticky-price monetary exchange rate model which were in a kind of a Vector Error
Correction Model (VEC) of the U.S. dollar against the Euro (EUR), pound (GBP), the Japanese
yen (JPY), as well as the Swiss franc (CHF). The variables which were used in this empirical
analyses is having a nominal money balances, nominal long-term interest rates, nominal shortterm interest rates real output, together with serving as a alternative to the inflation expectations.
These authors additionally provide out-of-sample of forecasting exchange rate while
investigating the duration ranging from Jan 1994-Feb 1999, with the horizons of forecasting are
vary from one to twelve months. The performance and output of these various forecasts is mainly
measured in the ratio of the Mean Absolute Error (MAE) and the Root Mean Squared Error
(RMSE) as the best model against all the alternative models incorporated by random walk (RW)
as a true benchmark. However, this model of forecasting model, somehow, has outshined the use
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of random walk model in the euro-dollar and pound exchange rate. In contrast, while comparing
euro-yen and euro-Swiss exchange rate, the best model to apply is the random walk (RWmodel).
Cuaresma and Hlouskova (2004), on the other hand, compares the accuracy and precision
vary from VAR-model which has five different forms of forecasting while considering five
Eastern and Central European currencies. These include: Polish zloty, Czech koruna, Slovenian
tolar , Hungarian forint, together with Slovakkoruna tolar against the U.S. dollar and euro.
Similarly, the study also forecast the horizons by still comparing the transactions within one to
twelve months. However, it is only the Slovenian tolar/euro rate that was capable to perform
extemporary while using the random walk model.
While concluding and summarizing from these two sources is that: The sticky-price monetary
model of exchange rate has more out-of-sample forecasting power, especially in the short run,
even though it is not considered a consistent way. This is because the findings do vary as from
one country to another coupled with different forecasting period which keeps on varying.
Although the ultimate results are disappointing, the following reasons might be having
ranging reasons. They include: first is the time horizon. It is really to short to forecasting the
exchange rates for a duration/period ranging from one to twelve months. This short period may
make the findings unreliable. Thus, the results may be insignificant while understanding that
MM-model of forecasting is a long-run model of exchange rate. Generally, most of the empirical
studies in respect to the exchange rate which do have been investigating the monetary models
that determines the exchange rate determination in relation to the floating exchange rate period is
having no supportive theoretical framework in the study of forecast. Contrary to what the longrun MM-models demand for, there had no sufficient evidence found that can show the co-
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integration and the relationship between exchange rate coupled with the various foreign
exchange fundamentals, and has not been established (Grech 2004). Furthermore, MM-model is
having very little support and assistance, even to the situation where the co-integration
relationship is in existence, less prediction accuracy is attained. Coefficients are sometime
insignificant as they are mostly having wrong signs. In this case, there should no much surprise
in this study, as in a long-run, purchasing power parity (PPP) is relevant in a situation where
countries are performing poorly among the selected blocks in a short-run horizons. In addition,
the use of uncovered interest rate parity (UIP), is still useful while sxtuudying the building
blocks. However, the uncovered interest rate parity is usually rejected in the study of empirical
evidence as it is considered to be having unbiased predictions while analyzing the future
exchange/spot rates while comparing the previous rates.
Additionally, MM-models might be having a deficit of omitting some variables. For
instance, the models might have been omitting and failing to recognize both home and foreign
nation wealth variables, which include, government debt compared to GDP ratio as well as the
ultimate foreign asset ratios. This promotes these variables do be studied and evaluated with the
use of a models of exchange such as the “behavioural equilibrium exchange rate” (BEER)
approach (Grech 2004). Both the BEER and the NATREX models have tremendously gained
more importance and they are of much more importance while studying a context pertaining
policy issues in line with an immediate and short-run horizons. While determining the forecast in
the long-run, BEER approach is of great importance in the determination of the value of euro.
Clostermann and Schnatz (2000) for example, identify four basic fundamental factors in the
drive of the real exchange rate of the euro-dollar exchange rate as they are considered to be the
real international exchange interest rate differential, the real oil price, relative prices in the
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trading and non-trading commodities sectors, and also the relative monetary position. They also
considered effective while forecasting the exchange rate as from done top eight quarters, thus
indicating each and every single computation error in the equation as well as making relevant
correction. This principle majorly over headed MM-models vis-à-vis the random walk model
benchmarked.
In contrast, the monetary exchange rate models may have been performing fairly poor
especially in the empirical literature. Grech (2004) identified some of these challenging reasons
which include:

Simultaneity problems. The question that rises from these models is whether all the
variables in the forecasting equation right hand side are exogenous. For instance, while
there is a change in the supply of money is assumed to have an impact and influence the
exchange rate. Nevertheless, when the central bank is that country intervenes to avert the
situation without further sterilization, the supply of money will change too. Likewise, the
effect will also be felt even in the real exchange income exchange rate.

Failure to consider nonlinearities. While accounting for this reason, the economic
fundamentals are considered to have an influence on the exchange rates, and especially
while in a nonlinear entity coupled with the formulation of a linear model, which in most
cases negatively have an impact on the empirical methods.

Instability of the parameters. This is because, most of the empirical studies were
supposed have been covered in the long-run and the models only do forecast for short-run
and with structural breaks.
Spinning of the econometric method is also applied. The VARs is having a distinction or
an advantage not only when comparing the long-run spot rates in MM-model, but the approach
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also focuses and capture data available in the dynamics of short-run approach. Generally, it is
well known and understood that the VARs approach have been performing extreme while
forecasting and analyzing and describing data as pointed out by Stock and Watson (2001).
Furthermore, VARs are credited to perform other tasks: they are of much importance in
both policy analysis and structural inference as they are much better of in fulfilling the
difficulties emerging. This is because the approach only requires one to identify the problem
under discussion or study. Being able to solve and handle the problem identified effectively,
always enables the research to differentiate between the causation and the correlation, and hence
being able to interpret and predict the correlation casually (Grech 2004).
Additionally, it is obvious understood that, for the researcher to offer and produce good
and favorable results, VARs forecasting mechanisms should be used to compared on one
side/hand at least three or four variables, while on the other hand , the time should be varying
effectively in a parameters that will assist to capture significant drifts within the coefficients.
This is because, while dealing with much more variables, there will be much more difficulties in
obtaining realiable forecasts against all coefficients and thus leading to much more restrictions
and assumptions. This problem should be handled urgently and prudently through the use of a
common structure in respect to coefficients while handling the empirical methods, and especially
in the forecasts of exchange rates in different analysis of countries.
Therefore, one of the prime alternative empirical model that could of much essence and
need to be followed while forecasting, especially when dealing with the euro-dollar exchange
rate in a short-run horizons, is through considering the possible nonlinearities majorly between
the exchange rates and the fundamentals, then examine the findings with the use of MM-model
in case the coefficients in the study are varying with dime. `
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In summing up MM-models, the models can be improved while forecasting the exchange
rate for example in the euro-dollar and pounds by considering the following factors: prior
suggestion concerning the data, and being known in the forecasting exercises. Several
forecasting of exchange rate experiments concerning the macroeconomic data as revised in the
empirical methods (Grech 2004). However, it is known clearly that data revisions have an effect
on the fundamentals of exchange rate, for instance, GDP figures. Nowadays and precisely in the
field of fiscal economics, there is an emergence of other current literature. It proposes that
analysis working with concurrent data habitually results in substantially different conclusions
than work based on revised data. Indeed, it is conceivable that we would possibly get a much
different picture of exchange rate movements, if information or data are used which were
actually available to agents at a particular point in time in the past. Faust et al. (2003), for
instance, examine the real-time forecasting power of standard exchange rate models of several
currencies (JPY, DEM, CAD and CHF) against the USD. The authors conclude that the
predictive power of the exchange rate models used is uniformly better using real time data than
using ex-post revised data.
The second and last suggestion refers to the estimation technique: As empirical exchange
rate models performed rather poorly in forecasting spot rates on a country-by-country basis, why
not turn to panel tests that pool data across countries? In this context, I would like to refer for
instance to a paper by Rapach and Wohar (2002). The authors show that in contrast to countryby-country analysis, there is substantial support for the monetary model using panel tests.
Moreover, comparing forecasts undertaken on a country-by-country basis versus panel forecasts,
they show that panel estimates generate superior out-of-sample exchange rate forecasts. This
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suggests that panel estimates of the monetary model are more reliable than country-by-country
analysis.
Fundamental determinants of exchange rates
There exist a well conveyed literature t put in to consideration when forecasting the
economic determinants that influence the exchange rate of euro-dollar and pound. These
determinants are considers to explain and predict the future exchange rate. While focusing on
forecasting and explain short-run changes in the euro-dollar and pound exchange rate while
using macroeconomic fundamentals and financial elements. While underscoring the parameters
of forecasting way the conventional set of fundamentals derived from the monetary model of
exchange rate determination, enlarged by a set of forward exchange rates.
While using the fundamentals from the monetary model of exchange rated because of its
usefulness in the study of international economics, where it is regarded as the "standard
workhorse" (Frankel and Rose, 1995, p. 1691), and since these fundamentals are just the same as
for those derived in the contemporary founded micro exchange rate models. Forward rates are
used as the basic parity situations propose that these parameters are much useful in the forecast
of exchange rate. Obviously, the most important questions to research on in the field of
international finance is the issue of whether or not the forward exchange rate do assists in
predicting the exchange rate of the market (Bianco, et al. 2012). As the ultimate answer is
usually "no", Clarida and Taylor (1997) illustrate that there is vital and imperative information in
the term structure of forward exchange rates in respect to the future movements and shifts of the
exchange rate.
Widely relying on these two forms of fundamentals in international finance as they are
much more widely used in most of the literature: although it was with limited success in
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forecasting short-run exchange rate changes, there is a probability of achieving positive results
available while applying and the enforcement of the econometric methodology.
Forward exchange rates
It denote that St as the logarithm of the spot exchange rate at time t defined as the
domestic price of foreign currency (hence raises in s imply domestic currency depreciation), and
Fkt as the log of the k-period forward exchange contracted at time t. forward exchange rates and
spot are mainly connected by two fundamental international parity conditions, the Uncovered
Interest Parity (UIP) and the Covered Interest Parity (CIP).
To work it out, let Ikt and i*kt be the data t nominal interest rated on similar domestic and
foreign securities with a maturity of k periods, respectively. If both deposits have the same risks
characteristics and only differ by the currency of denomination, CIP arbitrage condition states
that nominal risk-free returns from both deposits should be equal. Using a logarithmic
approximation, the CIP condition is expressed as:
Ikt ≈ i*kt+Fkt-St………………………………………………………….. (1)
In relation to this, it further implies that while in equilibrium, the expected forward
conjecture is further driven to nil because if (1) is violated, and a riskless arbitrage profit
prospect is also available in a zero-net investment strategy. The empirical verification, generally
are in the support of the validity of CIP (Taylor, 1989).
On the other hand, the Uncovered interest parity (UIP), and is called uncovered as
forward markets in the exchange rates are not mainly applied as a hedge, is grounded on the
suggestion that with risk-neutral agents, who care only about the mean value and not the variance
22
of asset returns, are expected to forward speculation profits should be driven to zero. Since FktSt+k is the profit from taking a position in forward foreign exchange, the k-period jforward
exchange in equilibrium must be equal to the market agent’s expected in the coming spot
exchange rated during time k. Hence:
Fkt=Et (St+k)-St…………………………………………………………(2)
In this case, Et (St+k) is the mathematical expectations of St+k conditioned on the date-t
available information set it. Several works have studied whether the forward exchange rate is a
forward exchange rate predictor of the future spot rate, but from the evidence obtained, there is
an indication that the current spot rate is a better predictor of the future spot rate that the current
forward exchange rate (Meese and Rogoff 1983). This must be adhered to in this study, the work
papers conducted by these authors is inseparable with the literature of forecasting of the
exchange rate. This is because, they demonstrates that there are structural models that are unable
to outperform the forecast in the random walk model, which in most case, is unchangeable.
While we substitute (2) into the CIP we end up getting the UIP arbitrage condition:
ikt≈i*kt + Et (St+k)- St……………………………………………………..(3)
UIP estimation is used to approximate the equilibrium of the market assets as well as
being the cornerstone parity condition in the test FX market efficiency. In case we violate
equation (3), a zero-net investment borrowing strategy is useful in determining the currency as
well as the simultaneously lend to discover the expected pay-offs in a positive manner.
In addition, under the rational expectations as well as risk neutrality, the estimate would be
viewed as unbiased. Therefore, if we plug in (1) and (3), the result will be:
23
Et (St+k) ≈ Fkt-St……………………………………………………….(4)
Thus, if (4) holds, the forward premium should be an optimal predictor and forecaster in
the future exchange rate increment as well as it depreciation. It would be noteworthy to
understand that UIP holds with the agents have risk neutral and be of rational expectations.
Constructing on this knowledge, the general and the most empirical strategy used while testing
the hypothesis of the risk-neutral efficient in the markets is founded in respect to the following
equation:
St+k – St =α+β(Fkt-St)+Ut+k…………………………………………………….(5)
This formula will assist in the prediction of the future exchange rate, the projection
understood in the forward premium. The level of risk-neutral efficient market is projected by the
hypothesis requirements of α=0, and β=1, thus the empirical evidence in the function will
generally not hold as show in a survey by Engel (1996).
Thus the empirical evidence will be shown through the comparison of the euro mark
against both the dollar and pound and then presented in a model of Vector Error Correction
Model (VECM), in order to identify the forward premiums of the forecast.
The Econometric Model
Mixing frequencies
In the case, one is supposed to use the information at two frequencies, either weekly or
monthly. While mixing these two frequencies in this model, more accurate and keen eye should
be employed on the frequencies of weekly and treating the monthly data as a form of weekly
series. The use of end of period (e.o.p.) data is crucial. In every week, say the e.o.p is Friday (last
day of the week). More so, while considering the monthly data, the e.o.p value will be assigned
24
to the last Friday of every month. Specifically, if we let Yt to be the monthly series that is
observable in every last Friday of each month putting into consideration that some months might
be having four Fridays while others have five.
Considering in the first instant, there is four- Friday in every month, the short-run
frequency series is the aggregation of the month-weekly series, Xt, which in this case, will be in
assumption while observing. In order to obscure us from getting into an non-linear model, it is
useful to adhere to Mariano and Murasawa (2003) and Camacho and Perez-Quiros (2010), and
put an approximation of arithmetic mean with that of geometric mean. Thus, in the four-Friday
case we assume that the flow data is four times the geometric mean of the weekly series within
the given month
Yt=4(XtXt-1Xt-2 Xt-3)¼…………………………………………………………….. (6)
Introducing logs in the equation, while taking the differences of all four-period t. Therefore the
equation obtained
gt= (¼) Xt + (2/4)Xt-1 + (¾)Xt-2 + Xt-3 + (¾)Xt-4 + (2/4)Xt-5 + (¼)Xt-6………… (7)
In this case, (gt) is the four-duration difference of the logarithm of Yt. Xt is the oneduration difference of the logarithm of Xt. therefore, we are supposed to express the monthly-onmonthly rate of growth (gt) as a weighted average of the weekly-on-weekly rate of growth (Xt-i,
i=0….6) as a weekly series. Thus, arriving at the operating analogously, the need to have an
analogous equation even on the case of five-Friday month is paramount.
Additionally, the use of State-Space Representation is crucial in the factor modeling
literature. It is the standard of considering how each indicator is used alongside with the model to
25
bring up the “sum of two orthogonal components” (Bianco, et al. 2012). These components are
used as common factor, ft, to capture the movements of the forecasting parameters among the
series that are in existence as a result of common shocks in the market. The estimation of the
variance of the weekly euro-dollar and pound exchange rate as well as the forward exchange rate
variations is very vital in the case of forecasting in short-run, and this laid the basic critical
model in this study.
Summary of the Chapter
Researchers are supposed to understand clearly that one of the major and salient issues
which have being dominating the empirical literature available is whether the exchange rates in
the market are predictable. There had been past assessments on the issue of nominal exchange
rate identification that have focused on the verge models underpinning the literature in the 1970s
period (Chinn 2003). It is crucial to note that, in this paper, the focus is much more weighted on
an econometric model on the forecast of euro-dollar and pound exchange rate as it the model
have a distinctive feature while utilizing the economic parameters as quoted in various
frequencies which explains the forecasting variations in a weekly exchange rate frequencies.
However, due to the high frequency in the international finance variables, the literature has found
that the already existing literature is difficult to sufficiently explain the forecasting and
prediction of the foreign exchange rate movements as well as extensive phenomenon forecast.
This is in respect to the facts that the researchers cannot generally ignore the simple random walk
model which was much more used in the prediction of the exchange rate that remains unchanged
(Bianco, et al. 2012). Therefore, it was useful to know that the best predictor in the spot rate is
better while comparing in the next period.
26
Nevertheless, it is noteworthy to understand that there is “no econometric model is ever
truly complete,” (Hymans 2008). As all models are containing some variables which are not
capable to be predicted as there are forces which influences the determination of the model.
However, care is much need in the analysis as this dictates the results anticipated in the study, for
the realization of the research objectives. Thus, there is much need to use all the parameters
accordingly for the in an econometrical manner so as to have a useful economic forecasting.
The important of examining the model in which one is using is relevant. This is putting into
consideration the period the study is undertaking. Evaluation and re-evaluation of the available
data is paramount. As Chinn (2003) argues, the success of any long-run regression in congruence
to the moment/duration the frequency series are considering. Thus, while forecasting, the choice
of the model to utilize in the study plays role in determining and explaining the results from the
study. The need to have the predictive power while in the structuring of the useful model is
critical din the study.
It is crucial to note that the dynamism of the econometric model is vital in the analysis,
predicting and forecasting, and fits most in-sample weekly functions while studying the
exchange rate, and is considered to be at least 80% fitness and good (Bianco, et al. 2012). On the
other hand, after extensive and thorough literature while forecasting the exchange rate indicates
that when there is good in-sample results, the adversity is on the out-sample results. In this case,
when we evaluate the performance of the out-of-sample forecasting in the dynamic model while
still using the standard regression- recursive rules, the obvious achievement is to enable the
model to boost the predictability and beat the random walk model. This will be in terms of
traditional errors of measurements such as the MAE and MSPE.
27
Furthermore, various forecast tests in accuracy indicate that there is much need to
improve the observation in order to ensure that the results are statistically significant. This is in
connection to the understanding that at every short-run frequency, there is the probability of
exerting fundamental economic errors that may errors in explaining the fluctuating exchange
rate.
Finally, failure to look upon the macroeconomic fundamentals while forecasting and
explaining the exchange rates has triggered the academic professions to shift their study to the
microstructure and operations of foreign exchange markets (Bianco, et al. 2012). The sole aim is
to identify while still gaining insights of the way exchange rate fluctuates and thus the literature
assist in ensuring the order of the flows, in the measuring techniques, are adhered and followed
in the understanding fundamentals of foreign exchange transactions.
28
CHAPTER THREE
Methodology
When conducting this research, considering and understanding how the knowledge is
generated is very important. The philosophy of the research on the other hand is vital because it
has a significant impact and effects the research procedures which include the methods used in
the research, the data collection and analysis. When the research fully understands the
philosophy of the research, it becomes easier to understand the research design. The research is
always directed by the philosophy that the research has adopted (Creswell, 2003:32).
Research ethics
This research involves a human subject as they are involved in the determining the
exchange rates and on the other hand they affect their businesses as well. In this regard, ethical
issue is vital and should be considered. The ethics that have been considered in respect to the
scholars whose contents have been used in this dissertation through acknowledging their work
and honesty. This has helped in coming up with scholarly integrity and acceptability of this
dissertation. This dissertation has considered several and different ethical issues to help in
validating the research process and the findings that have been achieved. To show the integrity
and commitments to the ethical consideration of this task, all the materials that have been used
from other scholars have been cited and acknowledged (Schein 2004:96).
The feelings participants in this research were given a great consideration to ensure all
ethical aspects have been employed. This was through ensuring that the participants did not face
any difficulty throughout the process or be subjected to any discomfort or a feel of insecurity that
the information they provide could be used for any other purposes beside the academic research.
29
Due to the sensitivity and importance of this research, considering ethical issue was an
important factor that was considered. The research ensured that there was no privacy violation
that was done and all information provided by the participants remained confidential as this is
one of the most important ethical consideration every research requires (Livingston 2004:49).
Research Strategy
A research strategy is regarded as a way of conducting a specific research when
following a specific procedure step by step. On the other hand, a research technique is a way of
collecting data and analyzing it.
In any business research, there are six main strategies that are used including the survey,
design and creation, the case study, experiment and ethnography (Creswell, 2003). In this
research, case study is adversely utilized as this research focuses on the factors that are affecting
the euro-dollar and pounds exchange rates in the Nigerian market. On the other hand, case study
will offer useful information regarding the topic and other researches that were conducted by
other researchers previously. In this regard, the researcher will collect data from different sources
including bank data and other global finance information sources like yahoo finances to analyse
and understand the factors affecting the exchange rates in regard to euro-dollar and pounds in
respect to Nigerian Naira.
Data analysis techniques
When the data collection is completed, comprehensive data analyses was done in order to
interpret the data and have an accurate and a conclusion that can be depended on. The literature
review was also vital in data analysis. The findings that the researcher achieved were compared
with other results that were attained by other researchers that conducted similar research
30
previously and found in the literature review. The results that were achieved were used to justify,
support and authenticate the research (Schneider 2004: 99).
The analysis of the data that was collected in this research was determined by the
research questions and through comparing it with the results that were achieved by other
researchers who contributed to this topic. This on the other hand was of great help in crafting a
conclusion that can be used in future research in achieving and understanding the factors that
affects the euro-dollar and pounds exchange rates (Saunders et al, 2003: 123).
After collecting the data, the data was analysed and discussed in order to give the best
results and findings.
Constraints
In this type of research, qualitative research for that matter, reliability is very important
and very vital. According to Livingston (2004: 69), the two main elements that measure the
qualitative research include the validity and the reliability of the research. To achieve an external
reliability in a qualitative research is difficult because of the ever changing external settings. This
makes it hard to conduct a research twice the same way because the settings keeps on changing,
thus making it hard to get the same results. The external validity in the research deals with the
generalisation of the results when it extends beyond the research settings (Schelkle, 2009: 77).
Research that is not reliable can never be regarded as a valid research. A valid research
must agree on whether the research question is honest and truthful while the predictive research
deals with the ability to forecast on what will be in the future without stating conclusive facts.
The validity of the research is the extent to which the findings and the results achieved are able
to present on the reality of the research and accuracy. On the other hand, the research must be
able to prove the validity and originality of the results achieved (Livingston, 2004:104).
31
Case study
Case study is an excellent methodology that is useful to bring an understanding of
complex issues which incorporate experience as well as adding the strength to the already
existing variety of knowledge that hails from the previous research. The methodology majorly
emphasizes on the contextual analysis from the limited numbers of events and links them with
the relationships of the variables. According to Yin (1994) “A case study is an empirical inquiry
that investigates a contemporary phenomenon within its real-life context, especially when the
boundaries between phenomenon and context are not clearly evident.” Much of the attention on
the use of a case study is glued towards the contextual conditions whose focus is mainly on the
contemporary conditions or events coupled with the experience manifested by the experience of
the researchers.
The case study method is regarded as flexible and assists in production of diverse
outcomes in the study (Darke et al, 1998), as well as underneath all forms of philosophical
paradigms. Case studies can be exploratory, explanatory or descriptive (Yin, 1994). They are
important in the generation and/or in the testing of theory within the positivist paradigm (Lee and
Baskerville, 2003). More so they are also considered to be intrinsic, instrumental especially in
the provision of an insight on a situation or an issue of concern as well as a collective – based
method of more than one site (Stake, 2000). They also provide a wide and a rich description
towards the social phenomenon as well as generating variety of knowledge especially in the
interpretivist paradigm (Macpherson et al, 2000). Therefore, case study must be used to
maximize the research outcomes with the comparison of the available time or period in the study.
Additionally, they are also considered to be multi-perspective analysis (Tellis, 1997), and
incorporate all the actors in the study. Thus making case study as a methodology as a salient
32
method in this study as it will assist to investigate relevant data and resourceful information
pertaining the study of forecasting the exchange rate.
In this study, the case will be within Nigeria and the major focus will be on different
financial institutions and how they examine as well as investigating their forecast strategy on the
foreign exchange rate. Through thorough study of the relevant data and available information
regarding the study topic, more accurate and relevant explanations will be deduced while still
making salient inferences, conclusions and recommendations.
Importance of case study
It is useful to use this methodology fin this study. According to Yin (2003), it is important to
consider case study when:

The focus of the study is mainly to address the questions “why” and answer “how” things
are to be done.

One cannot manipulate the behavior of the respondents within the study

One wants to cover a contextual condition on which one believes of its relevancy to the
phenomenon of the topic under study.

The boundaries are such distinctive between the context and the phenomenon in the
study.
Steps used in the of case study

Determine and define the research questions:
This is the first steps in which the research has to establish a firm to focus to in the
research that a researcher will have to refer to in the course of the study while
investigating a complex phenomenon. The research forms the research questions those
33
will be the guideline in the study. This is in context to answer the questions which are
either “how” or “why”.
In this study, the research questions to be addressed are:
 What are the fundamentals of exchange rate using euro-dollar and pound?
 How is the exchange rate focused and determined?
 Are there any difference in forecasting exchange rates for euro-dollar and pounds
and other currencies?

Select the cases and establish the data gathering and analysis techniques
In this phase, the researcher determines the approaches applicable in the study that will
be used in the selection of the real-life cases whether single or multiple. More so, one
need to determine the instruments useful in the collection and gathering of the data.
Proper case studies examine and carefully select the cases to study as well as the research
tools applicable in the study. The techniques applicable may include: documentation,
interviews, archival records, participant observation, direct observation, and even
physical artifacts (Yin 1994). However, no single source has much advantage which is
complete enough to be used singly, but so much helpful when used all in tandem.

Preparing to collect the data
As case study is generated from a wide range of sources the need to systematically
organize the available data in accordance to their relevancy in the study. More so the
researcher must be able to prepare the available databases in order to assist in
“categorizing, sorting, storing, and retrieving data for analysis,” (Soy 1997).

Collection of the data from the field
34
In this phase, researcher has to collect and store the available sources of data in a
systematically and a comprehensive manner that will necessitate the exploration and
investigation of the topic of the study. The data should be organized in such a way that
will reflect the area of study. In this case, the case study should be much more flexible in
order to accommodate the relevant documents.

Analyzing and evaluating the data
The researcher should use the intuition gained in the research skills course to carefully
examine and interprets the raw data collected while still trying to find a link between the
objectives of the research and the outcomes inferred from the research questions.

Prepare the report
Lastly, an excellent case study should be able to report the data gathered in a manner that
will transforms the complexity of the issue in a way that will be understood by those
targeted by the study. The data should be publically available and accessible and in a
chronological manner while still linking the results with the real-life context.
Advantages of case study
Case studies are considered to be advantageous in the following aspects:
1. They allow a lot of detail to be collected that would not normally be easily obtained by
other research designs. They provide a rich amount of description and details.
2. They tend to be conducted mostly in rare cases where volume samples of participants are
not applicable.
3. Helpful in the adoption of ideas as well as offering novel hypothesis that are useful in
future testing
Limitations of case study
35
Nevertheless, the use of case study is not short of criticisms. Some of the limitations are:
1. Not useful in the study of a small number of cases as the incident will reduce the
reliability of the findings. Thus, the data/ information gathered cannot be generalized to
the wider population
2. Some of the case studies may not be specific and thus very difficult to deduce an inferred
or definite effect/cause of the studies.
However, in general, the use of case studies have much more important and is a useful
method of collecting data, especially in a phenomenon which are rare and do not have much
information. They help to relate and links the data collected with the real-life situation and offer
the best possible solutions to the topic in the study.
Summary of the Chapter
In this study, case study is the preferred methodology and all the research entities must be
considered. This is because, case study is much considered to capture the complexity of the
situation and especially in a single case. This methodology is much regarded more in the
development of studies in the social sciences. Proper use of this method will ensure that the
results and the findings drawn from this study are validated and reliable for the future researches.
Adequate collection of relevant information/data in this study which was qualitative and
interpretive will bring about a holistic approach even though forecasting of exchange rates might
be varying mainly due to the time frequencies and fluctuations of the market. This study will
facilitate that all other research strategies are considered and combined together due to the
effectiveness of the study.
Additionally, it is vital for the researcher to adhere to the professionalism while
investigating and deducing the conclusions from this study. Professionalism is a guarantee. The
36
ability to act in accordance to the professionalism is very essential as one is able to maintain the
research ethics and be able to make inferred outcomes from the study. Avoiding personalization
in the study and building the professionalism repertoire is crucial, and case studies are significant
in maintaining so thus their preferences. The researcher must also try to minimize the limitations
and constraints for the reliability of the study din future researches. Intrinsic and analytical
selected cases should be considered purposefully for the benefit of achieving the set objectives
and address the research questions effectively. This will offer a good room for the generalization
of the study in order for the future reference by other researches in the field.
37
References
Afonso, A., Furceri, D. and Gomes, P. (2012). Sovereign credit ratings and financial markets
linkages: Application to European data. Journal of International Money and Finance.
Vol.31(3), p. 606-638.
Altavilla, C. and Grauwe, De-P. (2010). Forecasting and combining competing models of
exchange rate determination. Applied Economics. Vol. 42, p.3455-3480
Andersen, T. G., Bollerslev, T., Diebold, F. X., and Vega, C. (2007), Real-time price discovery
in global stock, bond and foreign exchange markets, Journal of International Economics,
Vol. 73, p. 251-77
Andersen, T., Bollerslev, T., Diebold, F. and Vega, C. (2003). Micro effects of macro
announcements: real-time price discovery in foreign exchange. American Economic
Review. Vol.391, p.38- 62.
Arezki R., Candelon B. and Sy A. (2011). Sovereign rating news and financial market spillovers:
Evidence from the European debt crisis. IMF Working Paper No. 11/68.
Baker, S. R., Bloom, N. and Davis, S. J. (2012). Measuring economic policy uncertainty.
Available from: <http://www.policyuncertainty.com/media/BakerBloomDavis.pdf>.
Accessed [Online] 25th October 2013
Beetsma, R., Giuliodori, M., de Jong, F. and Widijanto, D. (2012). Spread the news: How the
crisis affected the impact of news on the European sovereign bond markets. CEPR
Discussion Paper No. 9043
Berkowitz, J., and Giorgianni, L. (2001), Long-horizon exchange rate predictability?, Review of
Economics and Statistics, Vol. 83, No. 1, Feb., p. 81-91
Bianco, M., Camacho, M., and Porez-Quiros, G. (2012). Short-run forecasting of the euro-dollar
exchange rate with economic fundamentals.BVA Working Papers 12/01.
Boudoukh, J., Richardson, M., and Whitelaw, R. F. (2008), The myth of long-horizon
predictability, Review of Financial Studies, Vol. 21, No. 4, p. 1577-605
Brzeszcynski, J., and Melvin, M. (2006). Explaining trading volume in the euro. International
Journal of Finance and Economics, Vol. 11, p. 25-34
38
Camacho, M., and Perez-Quiros, G. (2010). Introducing the Euro-Sting: short term indicator of
euro area growth. Journal of Applied Econometrics Vol. 25 (4), June/July, p. 663-694
Cheung, Y.-W., Chinn, M. D., and Garcia Pascual, A. (2005), Empirical exchange rate of the
nineties: are any fit to survive?, Journal of International Money and Finance, Vol. 24,
No. 7, Nov., p. 1150-75.
Chinn, M. D., and Frankel, J. A. (2008). Why the euro will rival the dollar. International
Finance, Vol. 11(1), p. 49-73
Chinn, M. D., and Meese, R. A. (1995), Banking on currency forecasts: How predictable is
change in money?, Journal of International Economics, Vol. 38, No. 1-2, Feb., p. 16178.
Chinn, M.D. (2003). Explaining Exchange Tate Behavior. The National Bureau of Economic
Research. Available from: <http://www.nber.org/reporter/spring03/explaininge.html>
Accessed [Online] 25th October 2013
Clarida, R. H., and Taylor, M. P. (1997). The term structure of forward exchange premiums and
the forecastability of spot exchange rates: Correcting the errors, Review of Economics
and Statistics. Vol. 79 (3), p. 353-61
Closterman, J. and Schnatz, B., (2000). The Determinants of the Euro-Dollar Exchange Rate –
Synthetic Fundamentals and a Non-Existing Currency. Applied Economics Quarterly,
Vol. 3, p. 274–302.
Conrad, C. and Lamla, M. (2010). The high-frequency response of the EUR-USD exchange rate
to ECB communication. Journal of Money, Credit and Banking. Vol. 42(7), p. 13911417.
Creswell, J.W. (2003). Research design: Qualitative, quantitative, and mixed methods approach.
Thousand Oaks: Sage: p. 12-27
Darke P, Shanks G and Broadbent M (1998) Successfully completing case study research:
combining rigour, relevance and pragmatism. Information Systems Journal. Vol. 8, p.
273-289.
39
Ehrmann, M. and Fratzscher, M. (2005). Exchange rates and fundamentals: New evidence from
real-time data. Journal of International Money and Finance. Vol.24(2), p.317-341.
Ehrmann, M., M. Fratzscher and R. Rigobon (2005). Stocks, bonds, money markets and
exchange rates – Measuring international financial transmission. NBER Working Paper
11166
Engel, C. (1996).The forward discount anomaly and the risk premium: A survey of recent
evidence. Journal of Empirical Finance, Vol. 3, p. 123-92
Evans, M.D.D. and Lyons, R.K. (2005). Do currency markets absorb news quickly?, Journal of
International Money and Finance Vol.24(2), p.197- 217.
Faust, J., Rogers, J. H., and Wright, J. H. (2003), Exchange rate forecasting: The errors we've
really made. Journal of International Economics, Vol. 60, No. 1, May, pp. 35-59
Faust, J., Rogers, J.H., Wang, S.B. and Wright, J.H. (2007). The high-frequency response of
exchange rates and interest rates to macroeconomic announcements. Journal of Monetary
Economics. Vol.54(4), p.1051- 1068.
Frankel, J. A., and Rose, A. K. (1995), Empirical research on nominal exchange rates, in G.
Grossman and K. S. Rogoff (Eds.), Handbook of International Economics, Vol. III,
Chapter 33, pp. 1689-729, Elsevier
Fratzscher, M. (2006). On the long-term effectiveness of exchange rate communication and
interventions. Journal of International Money and Finance. Vol. 25(1), p.146- 167.
Fratzscher, M. (2009). What explains global exchange rate movements during the financial
crisis?, Journal of International Money and Finance. Vol.28(8), p.1390-1407.
Grech, H. (2004). Macroeconomic Models and Forecast for Austria: Comment on “Evaluating
Euro Exchange Rate Predictions from a Battery of Multivariate Models”.
Oesterreichische National Bank: OeNB Workshops Papers No. 5.
Hymans, S. (2008). Forecasting and Econometric Models. The Concise Encyclopedia of
Economics. Available from:
<http://www.econlib.org/library/Enc/ForecastingandEconometricModels.html>
Accessed [Online]. 25nd October 2013
40
Jansen, D.-J. and de Haan, J. (2005). Talking heads: The effects of ECB statements on the eurodollar exchange rate. Journal of International Money and Finance.Vol. 24(2), p.343-361.
Johnson, G. and Schneeweis, T. (1994). Jump-diffusion processes in the foreign exchange rate
markets and the release of macroeconomic news, Computational Economics Vol.7(4),
p.309-329.
Kiff, J., Nowak, S.B. and Schumacher, L.B. (2012). Are rating agencies powerful? An
investigation into the impact and accuracy of sovereign ratings. IMF Working Paper No.
12/23.
Kilian, L. (1999), Exchange rate and monetary fundamentals: What do we learn from longhorizon regressions?, Journal of Applied Econometrics, Vol. 14, No. 5, Sep., p. 491-510
Kilponen, J., Laakkonen, H. and Vilumen, J. (2012). Sovereign risk, European crisis resolution
policies and bond yields, Bank of Finland Working Paper No. 22.
Kim, B. J. C., and Mo, S. (1995), Cointegration and the long-fun forecast of exchange rates,
Economics Letters, Vol. 48, No. 3-4, June, p. 353-9
Kim, S.-J. (1998). Do Australian and the U.S. macroeconomic news announcements affect the
USD/AUD exchange rate? Some evidence from E-GARCH estimations. Journal of
Multinational Financial Manangement.Vol.8(2-3), p.233-248.
Kim, S.-J. (1999). Do macro-economic news announcements affect the volatility of foreign
exchange rates? Some evidence from Australia. Applied Economics Vol.31(12), p.15111521.
Lee A and Baskerville R (2003) Generalizing Generalizability in Information Systems Research.
Information Systems Research Vol. 14 (3).
Livingston, S. (1994) Talk on Television: Audience Participation and Public Debate. London &
New York: Routledge: p. 98-119.
MacDonald, R., and Taylor, M. P. (1994), The monetary model of the exchange rate: Long-run
relationships, short-run dynamics and how to beat a random walk, Journal of
International Money and Finance, Vol. 13, No. 3, June, p. 276-90
Macpherson I, Brooker R and Ainsworth P (2000) Case study in the contemporary world of
research: using notions of purpose, place, process and products to develop some
41
principles for practice. International Journal of Social Research Methodology. Vol. 3,
(1).
Mariano, R. S., and Murasawa, Y. (2003). A new coincident index of business cycles based on
monthly and quarterly series. Journal of Applied Econometrics, Vol. 18, p. 427-43
Mark, N. C. (1995), Exchange rate and fundamentals: evidence on long-horizon predictability.
American Economic Review, Vol. 85, No. 1, Mar., p. 201-18
Mark, N. C., and Sul, D. (2001), Nominal exchange rates and monetary fundamentals. evidence
from a small post-Bretton Woods panel, Journal of International Economics, Vol. 53,
No. 1, Feb., p. 29-52
Meese, R.A., and Rogoff, K. S. (1983), Empirical exchange rate models of the seventies. do they
fit out of sample?, Journal of International Economics, Vol. 14, No, 1-2, Feb., p. 3-24
Melvin, M., Saborowski, C., Sager, M. and Taylor, M.P. (2009). Bank of England interest rate
announcements and the foreign exchange market. CESifo Working Paper No. 2613.
Mink, M. and de Haan, J. (2012). Contagion during the Greek sovereign debt crisis, DNB
Working Paper No. 335.
Mishkin, F.S. (2000). International Experiences with Different Monetary Policy Regimes. NBER
Working Papers 7044. National Bureau of Economic Research, Inc.
Mohl, P., and Sondermann, D. (2013). Has political communication during the crisis impacted
sovereign bond spreads in the euro area?, Applied Economics Letters. Vol. 20(1), p.4861.
Obstfeld, M., and Rogoff, K. S. (2000), The six major puzzles in international macroeconomics:
Is there a common cause?, in B. Bernanke and K. S. Rogoff (Eds.), NBER
Macroeconomics Annual 2000, Vol. 15, MIT Press, Cambridge, pp. 339-90.
Ogiogio, G.O. (1996). A Statistical Analysis of Foreign Exchange Rate Behavoiur in Nigeria’s
Auction . AERC Research Paper, 49.
Oyediron, O.B. and Afieroho, E. (2013). Simulation of Exchange Rates of Nigerian Naira
Against US Dollar, British Pound and the Euro Currency. Studies in Mathematical
Sciences, Vol.6 (2).
42
Posen, Adam, S. (2008), Why the euro will not rival the dollar. International Finance, Vol. 11
(1), pp. 75-100.
Rapach, D. and Wohar, M., 2004, Testing the Monetary Model of Exchange Rate Determination:
A Closer Look at Panels, Journal of International Money and Finance, Vol.23, p. 841–
865
Sager, M. J, and M. P. Taylor (2004). The impact of ECB Governing Council announcements on
the foreign exchange market: A microstructural analysis. Journal of International Money
and Finance. Vol.23(7-8), p.1043-1051
Salvatore, D. (2005), The euro-dollar exchange rate defies prediction, Journal of Policy
Modelling, Vol. 27, pp. 455-64
Sanusi, J. O. (2004). Exchange Rate Mechanism: The Current Nigerian Experience. A Paper
presented to the Nigerian-British Chamber of Commerce, Feb 24,2004
Saunders, M., Thornhill, A., and Lewis, P. (2003). Research methods for business students. (3th
ed).New Jersey: Pearson Education. P.87-138
Schein, E. (2004) Organizational Culture and Leadership. Manchester: Sage: p. 88-128.
Schelkle, W. (2009). Automatic fiscal stabilizers. LSE-EI Working Paper, April: p. 56-100.
Schneider, B. and White, S. (2004). Service quality: Research perspectives. London: Sage
Publications. P. 67-110
Soy, S.K. (1997). The Case Study as a Research Method. Available from:
https://www.ischool.utexas.edu/~ssoy/usesusers/l391d1b.htm. Accessed [Online] 25th
October 2013.
Stake Robert (2000) Case Studies Ch.16 of Denzin and Lincoln (ed) Handbook of Qualitative
Research. (2nd ed), Sage.
Stock, J. and Watson, M., 2001, Vector Autoregressions, Journal of Economic Perspectives,
Volume 15,(4) p. 101–115
Taylor, M. P. (1989). Covered interest arbitrage and market turbulence. Economic Journal. Vol.
99, June, p. 376-91
Tellis,W. (1997). Application of a Case Study Methodology. The Qualitative Report. Vol.3 (3).
43
Trading Economics, (2011). Nigeria’s Exchange Rate – USD/NGN. Available from:
<http://www.tradingeconomics.com//nigeria/currency> Accessed [Online]. 25th October
2013.
Umar, A. and Soliu, H. (2013). The Foreign Exchange Rates in Nigeria: Convergence or
Divergence. Available from:
<http://www.africametrics.org/documents/conference09/papers/Umar_Soliu.pdf>
Accessed [Online] 25th October 2013.
Van Arle, B., Boss, M. and Hlouskova, J. (2000). Forecasting the Euro Exchange Rate Using
Vector Error Correction Models, Weltwirtschaftliche. Archiv, Volume 136(2), p. 232–
255.
Yin R (1994) Case Study Research Design and Methods (2nd ed). Applied Social Research
Methods Series, Vol. 5, Sage Publications
Yin, R. K. (2003). Case study research: Design and methods. (3rd ed.). Thousand Oaks, CA:
Sage.