Major Foreign Currencies Research Articles

Volatility of Exchange Rate in Nigeria: An Investigation of Risk on Investment

The contribution of investment in currency trading in economic growth of a nation cannot be over emphasized. Hence, the examination of the risk involved in such trading because of volatility in foreign exchange rate. Time series data and model were used in this study. The monthly exchange rate of four major foreign currencies in Nigeria namely the US Dollars (USD), the Great Britain Pounds (GBP), the EURO and CFA Francs against Nigerian Naira (NGN) from January, 2004 to December, 2019 were extracted from website of Central Bank of Nigeria (CBN) with an open access to the public. Due to the volatile nature of the exchange rate, the Generalized Autoregressive Conditional Hetroscedastic (GARCH) model was a suitable model used at order 1 for parsimony to determine volatility used in computing Value at Risk (VaR). It was discovered that the maximum loss (risk), measured by VaR, that can occur at 95% confidence interval for twelve months forecast of trading with GBP was the highest, among the four currencies, with percentage loss of between 15.5% to 16.2%. While the CFA has the lowest risk with VaR between 0.02% to 0.03%. Based on the findings, the risk of investing in foreign exchange is the highest when trading in GBP with attendant high returns due to large fluctuations up and down of the exchange rate. This study will help investors to study the pattern of risk and returns and decide on the foreign currency to trade on. The government should put policies in place to encourage existing investors and new investors that want to invest on foreign exchange to create employment, since the risk involved can be determined as it is done in this study.

Reserve currency and the volatility of clean energy stocks: The role of uncertainty

Clean energy firms are among the most risky firms to invest in. Hence, it is essential to have a clear understanding of the risk profile of clean energy stocks. The main purpose of this study is to examine how changes in the reserve currency (US dollar) value affect the volatility of clean energy stocks in the absence/presence of uncertainty information. To this end, the volatility of clean energy stocks is first obtained by considering uncertainty information in the exponential generalized autoregressive conditional heteroskedastic (EGARCH) volatility estimates. Then, using the autoregressive distributed lag (ARDL) model, this study investigates the effect of reserve currency (US dollar) value fluctuations on the volatility of clean energy stocks. Controlling for monetary conditions, financial stress, and business cycle fluctuations, the study finds that, in the absence of uncertainty information, the volatility of clean energy stocks is not influenced by US dollar fluctuations. However, an appreciation of the US dollar against major foreign currencies significantly increases the volatility of clean energy stocks when uncertainties about economic policy, exchange rate market, and gold market are included in the picture. We find that economic uncertainty mediates the link between reserve currency and the volatility of clean energy stocks. The results shed more light on the risk profile of clean energy stocks and suggest the need for predictable and stable policies to increase the attractiveness of clean energy stocks.

Interbank Markets and Central Bank Intervention during the COVID-19 Crisis

COVID-19 evolved to become the first major global crisis since the sub-prime and the euro sovereign debt crisis. Banks' funding planning was exposed to constraints and risks, especially in their major foreign currencies. Using two-way quotes for foreign exchange swaps, we find that when the crisis evolves, banks actively increase swap maturities while being confronted with increased swap spreads. Not surprisingly, this development reflects increasing uncertainty in the market. Contrary to common intuition, however, we find that after the coordinated central bank intervention in March 2020 banks only marginally reduce swap durations, while spreads continue to climb further. Our results suggest that the interventions did not yield the desired effect and uncertainty in the market remained.

Causality and dynamic spillovers among cryptocurrencies and currency markets

AbstractThis paper utilizes two methods to uncover the causality dynamic between the three leading cryptocurrencies: Bitcoin, Litecoin, Ripple, and nine major foreign currency markets. Firstly, we implement the technique of Diebold–Yilmaz to compute the spillover index between cryptocurrencies and currency markets. We find a significant return spillover effect between Bitcoin and Litecoin in the first three quarters of 2017. Still, the return spillover is merely meaningful in the first three quarters of 2015 for Ripple. However, the total volatility spillover index in the system decreases in the fourth quarter of 2017. Secondly, we apply the Bayesian graphical structural vector, autoregressive estimations, and find that the current level of Bitcoin depends only on the previous level of the Chinese Yuan. The current level of Ripple strongly depends on the prior levels of Bitcoin, followed by Litecoin. The current level of Litecoin strongly depends on the previous level of Ripple, followed by the Chinese Yuan. These results indicate that there is a significant causal relationship among cryptocurrencies. However, except for the Chinese Yuan, major traditional currencies do not significantly affect cryptocurrencies.

Forecasting of Forex Time Series Data Based on Deep Learning

This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method. On the exchange rate data of nine major foreign exchange currencies, the experimental comparison of the forecasting method shows that the C-RNN foreign exchange time series data prediction method constructed in this paper has better applicability and higher accuracy.

Ghana’s Growing Debt and Its Implications for the Economy

Management of public debt remains a major economic challenge to governments across the globe. Public debt management is more challenging to countries classified as least developed, less developed, and developing than those termed as developed or advanced economies. Challenges to economic management in some countries led to their description as highly indebted poor countries (HIPC) and low-income countries (LICS). In spite of high interest charges, most governments of developing economies borrow from international financial institutions to support their annual budgets; and to embark on social and institutional infrastructural developments in their respective economies. However, many economic experts and citizens in most of these developing economies have raised concerns about whether or not their governments use the contracted loans for the purposes intended. The purpose of this research was to assess trends in Ghana's debt, and the relative effect of debt on the economy. The present study relied on the quantitative approach to scientific inquiry. Specifically, a cross-sectional design, an example of survey design, was adapted and used in the research. This allowed the researcher to gather relevant research data over a specific period of time. Data required for the research were obtained from secondary sources including text books, journals, research papers, newspaper publications; Google search engine, index mundi, and electronic databases of the bank of Ghana and international bodies such as the international monetary fund, and world economic outlook, among others. Descriptive statistics and regression models were used to describe the research variables; and to evaluate their behavior over the stated time frame in the Ghanaian and other economies. Findings from the research revealed a positive relationship between debt and the economy; the findings revealed debt accounts for about 98% of the variation in GDP. The study recommended the need for redefinition of debt sustainability and effective implementation of the succession plan principle. Government must ensure relative stability of the cedi against the major foreign currencies to avert the possibility of issuing more notes to settle the same outstanding debts at maturity dates. Intensification of the industrialization campaign is necessary to reduce unemployment rate; and minimize the need for regular borrowing to balance government's annual economic budget.

Are key market players in currency derivatives markets affected by financial conditions?

This study investigates if the biggest players in major foreign currencies futures markets are affected by current and previous financial conditions. Using root mean squared errors (RMSE), normalized RMSE, and Nash-Sutcliffe efficiency, this study compares the impact of current, 1 and 2 week lags of financial conditions onto foreign currency futures players’ net positions. The financial conditions indices used are UFCI, STLFSI, NFCI and ANFCI with weekly data set from January 2007 till December 2018. The US dollar index futures is included as a benchmark, since the financial conditions are based on US data and the most actively traded foreign currencies are paired against the USD. While RMSE and NRMSE gave mixed results into how current, 1 week and 2 weeks lagged Financial Conditions Indices (FCIs) values are related to speculators and hedgers’ net positions, lagged NFCI captured the highest correlation with both players’ net positions in Japanese Yen. 95% prediction levels encompassed the actual net positions held, including the financial crisis of 2008-2009. Forecasts were lower (higher) for hedgers (speculators) than actual net positions held during the same period. Comparatively, in the period 2016-2017, hedgers (speculators) net positions forecasts were higher (lower) than actual positions. The latter could be explained by FCIs not being affected during this period’s event, compared to net positions. While net positions data were stationary, excess kurtosis was present pointing to non-normal and autocorrelated series. This suggests the need to look into other components like non-reportable long or short positions in future analysis.

Back-Testing Methods Used to Estimate Future Spot Exchange Rates Utilizing Bloomberg Data

Since students ask for real life examples and employers look for better excel skills, I added an empirical project to International Finance class. Using Bloomberg data students, in teams of four, calculate annual and average annual currency appreciations, as well as, various parity conditions between dollar and major foreign currency. In the last phase of the project students back-test methods for predicting future spot exchange rates. As an incentive to experiment with different lengths of the estimation period, the team with the smallest standard deviation between estimated and actual exchange rates earn bonus points.

Major Foreign Currency Futuress Hedgers and Speculators and Financial Stability

This study investigates if the biggest players in major foreign currencies futures markets are affected by current and previous financial conditions. Using root mean squared errors (RMSE), normalized RMSE, and Nash-Sutcliffe efficiency, this study compares the impact of current, 1 and 2 week lags of financial conditions onto foreign currency futures players’ net positions. The financial conditions indices used are UFCI, STLFSI, NFCI and ANFCI with weekly data set from January 2007 till December 2018. The US dollar index futures is included as a benchmark, since the financial conditions are based on US data and the most actively traded foreign currencies are paired against the USD. While RMSE and NRMSE gave mixed results into how current, 1 week and 2 weeks lagged Financial Conditions Indices (FCIs) values are related to speculators and hedgers’ net positions, lagged NFCI captured the highest correlation with both players’ net positions in Japanese Yen. 95% prediction levels encompassed the actual net positions held, including the financial crisis of 2008-2009. Forecasts were lower (higher) for hedgers (speculators) than actual net positions held during the same period. Comparatively, in the period 2016-2017, hedgers (speculators) net positions forecasts were higher (lower) than actual positions. The latter could be explained by FCIs not being affected during this period’s event, compared to net positions. While net positions data were stationary, excess kurtosis was present pointing to non-normal and autocorrelated series. This suggests the need to look into other components like non-reportable long or short positions in future analysis.

The Relationship between a Unified Financial Condition Index and Major Foreign Currency Markets

This study proposes a unified financial condition index centred around the most popular financial conditions indices used in the US and tests its relationship with the most actively traded USD based foreign currency pairs, namely the British pounds, Japanese yen, Australian dollar, Canadian dollar, Chinese yuan and the Indian rupee. Using weekly data over 1993-2018, this paper proposes a Unified Financial Condition Index (UFCI) under a principal component analysis framework. The index captures 78% of the variability inherent in St Louis Federal Reserve Financial Stress Index, the Chicago Fed National Financial Condition Index and the Adjusted National Financial Condition Index. Significant p-value of UFCI, homoscedasticity and a relatively stable root mean squared errors were observed only for EUR/USD. Mixed findings, found as lags, were increased, suggesting a weak relationship between UFCI and foreign currencies. The UFCI forecasting model is compared with the VIX (volatility index) based model, and also a random walk model. Although the UFCI model was superior only for the Canadian dollar, Chinese yuan and Indian rupee after considering heteroscedasticity in errors, results were sensitive to the number of lags and insignificant p-values.

Can the Australian Dollar and Canadian Dollar Be Predicted Using Financial Stress Index?

The purpose of this paper is to investigate if the St Louis Federal Financial Stress Index (STLFSI) can be used to predict the EUR/USD, AUD/USD and CAD/USD. Building on Gurrib (2018) who proposed a unified financial condition index and tested the predictability of the index on major foreign currency markets, this paper extends the analysis further allowing a comparison between the forecasts of the most actively traded currencies, tests for the model efficiency, and analyzes the actual and forecasted foreign currency values of the predicted model. Using weekly data over 1993-2018, and 1-week and 2-weeks ahead forecasts, the EUR/USD had the smallest normalized mean squared errors, with a significant p value of the index and homoscedasticity. Although series were stationary, the results were mixed across different currencies when lags were increased. The forecasted values were higher than the actual foreign currency values during the 2008-2009 crisis, and vice versa during the 2000-2002, explained by the STLFSI spiking up during the 2008-2009 event compared to the 2000-2002 events. The lower and upper band level under the 95% prediction interval, however, captured the effect of the global financial crisis of 2008-2009 and 2000-2002 events.

Econometric analysis of real exchange rate shocks and real growth of the tourism sector in South Africa

The aim of this study was to analyse the interactions between real exchange rate and real income from the tourism sector in South Africa. The vector autoregressive model (VAR) with Johansen multivariate cointegration approach was used to analyse monthly time series from January 2007 to December 2015. This study found a negative long-run relationship between the real exchange rate and real tourism revenue (LTR) in South Africa, where the depreciation of the local currency is associated with an increase in the LTR. Short-run results revealed that LTR is affected by short-run fluctuations in the real exchange rate; while Granger causality test showed that the real exchange rate Granger causes LTR. This study concluded that the weakening of the local currency, against major foreign currencies, seems to be good news for the South African tourism sector.

Econometric Analysis of Real Exchange Rate Shocks and Real Growth of the Tourism Sector in South Africa

The aim of this study was to analyse the interactions between real exchange rate and real income from the tourism sector in South Africa. The vector autoregressive model (VAR) with Johansen multivariate cointegration approach was used to analyse monthly time series from January 2007 to December 2015. This study found a negative long-run relationship between the real exchange rate and real tourism revenue (LTR) in South Africa, where the depreciation of the local currency is associated with an increase in the LTR. Short-run results revealed that LTR is affected by short-run fluctuations in the real exchange rate; while Granger causality test showed that the real exchange rate Granger causes LTR. This study concluded that the weakening of the local currency, against major foreign currencies, seems to be good news for the South African tourism sector.

Coexistence of Firm Competitive Advantage and Priorities: A Study amongst the Textile Units in Tirupur

To remain competitive in the globalized market, a firm needs to create the competitive advantageous condition. Identification and development of relevant competitive priorities are the first step at the operational level of manufacturing strategies for organizational competitiveness. The present study tries to identify the coexistence between determinants of competitive advantage and elements of competitive priorities set by apparel manufacturer and exporter based in Tirupur knitwear cluster. The knitwear cluster has been considered as a driving force for the socio-economic development of India continuously growing and providing ample employment opportunity and earning major foreign currencies. Considering quality, cost, time, flexibility and innovative design as elements of competitive priorities; data are collected from Tirupur based Knitwear firms through interview method. The statistical analysis reveals that there is a significant coexistence between determinants of competitive advantage and elements of competitive priorities. Time, cost and quality are the top most priorities set by the exporter for desired competitive advantages condition while demand-condition and dynamic-capabilities of the firm are the major competitive advantageous condition.

Socio-economic determinants of life expectancy in Nigeria (1980 - 2011).

Attainment of 70 years life expectancy by 2020 is one of the millennium development goals in Nigeria. This study examined the socio-economic determinants of life expectancy in Nigeria using data from 1980-2011. Judging from the endogeneity feature of the variables, A VAR and VECM frameworks were employed. Socio-economic features were proxy by secondary school enrolment, government expenditure on health, per capita income, unemployment rate and the Naira foreign exchange rate. It was found that, the conventional socio-economic variables such as per capita income, education and government expenditure on health considered to be highly effective in determining life expectancy of developing countries are not significant in the case of Nigeria. The study however suggests that, life expectancy in Nigeria could be improved if attention is given to quality of government health expenditure, unemployment and measures to halt the depreciation of the Nigerian Naira against major foreign currency.

Impact of Fluctuation on Major Foreign Currency Pairs to Indian Rupee Involved Currency Pairs

The study is a comprehensive analysis of the cause and effect of fluctuations in the major currency pairs and the effect on the Indian rupee. The study also depicts the variation in the rates of the currency pairs through correlation between them. The objectives of the study are met through the collective and comparative analysis of the data collected for the purpose of the study. Currency market (or Forex market) is a very volatile and liquid market among all the financial market. Currencies are priced in pairs and are traded in pairs. It is useful to know from an investment perspective, regarding the movement they share with each other i.e. whether they move in same direction or in opposite direction. Once the correlations between these pairs and their movement pattern are known, it can be used to control upon overall portfolio’s exposure. The study is useful for those willing to trade in foreign exchange. The inferences drawn from the study can be seen as useful information and can be used to test strategies on correlated pairs in order to avoid over-exposure; to double profitable positions; to diversify risks, and to hedge in the ever fluctuating market. This project is an overview of global forex market and the impact of movement pattern of major foreign currency pairs to Indian rupee currency pairs based on the date of fourteen years from 1/1/2000 to 31/12/2013. Investors can hedge their risks and diversify their portfolio by using the data of correlation between various currency pairs. Fluctuations in one currency pair may or may not impact to another currency pairs. Traders can build their strategies using the currency correlation between different currency pairs to make profit from the market.The study is undertaken in Wings World Finoconsult Pvt. Ltd., Bangalore which is a financial advisory company and is a Subsidiary of FW Markets Ltd. Located in Saint Vincent and the Grenadines under license number 20779 IBC 2013.From the analysis it is found that major foreign currency pairs EURUSD and USDJPY have direct and positive high correlation with EURINR and INRJPY respectively. Whereas, EURUSD and GBPUSD have low and insignificant impact to the exchange rate of USDINR and GBPINR.Investors can take an advantage of the finding of this study to construct the profitable portfolio, hedge the risk, confirm the breakouts and avoid fakeouts while trading in the forex market.

The Bangladeshi Agarwood Industry: Development Barriers and a Potential Way Forward

Agarwood (which has various names around the Asia and Pacific region) is one of the most expensive non-timber wood products of the world. Bangladesh is producing three major products namely agar-wood, agar-oil and agar dust. It has many uses in the cosmetics (especially perfume) and medicine sectors. Agarwood has traditional, religious moreover cultural uses in different parts of the world. In addition to various Asian countries, agarwood has a long history in Bangladesh, especially in Moulvibazar and nearby districts. However, a few constraints are creating hurdles for a flourishing agarwood sector in Bangladesh. This paper provides first some background on the agarwood sector in Bangladesh and concentrates then on identifying development barriers of the agarwood sector in Bangladesh. Based on the identified development barriers, it then suggests a few recommendations. Proper regulatory support from the government could play a vital role to make it one of the major foreign currency earning sectors for Bangladesh.

The Agar Wood Industry: Yet to Utilize in Bangladesh

Agar wood is one of the most expensive non-timber wood products of the world. It has a series of names around the Asia and Pacific region. Bangladesh is producing three major products namely Agar-wood, Agar-oil and Agar dust/powder in Agar wood sector. It has multidimensional uses in perfume, cosmetics and medicine sector. Agar wood has traditional, religious moreover cultural uses in different parts of the world. Major Agar wood producing countries are India, Malaysia, Indonesia, Vietnam, Cambodia, Laos, Brunei, Singapore and Bangladesh. Major market/consumers of Agar wood are the Middle East (UAE, KSA and other Arabian countries) and North East Asian countries (Taiwan, Japan and Korean Republic). It has a long history in Moulvi Bazar and nearby districts of Bangladesh too. A very few constraints are creating hurdles toward flourishing this sector in Bangladesh. In this study we would be very much concentrated to identify development barriers of Agar wood sector in Bangladesh and generate few recommendations in this regard. Proper regulatory support from the government could play a very vital role to make it one of the major foreign currency earning sectors for Bangladesh.

The Agar Wood Industry: Yet to Utilize in Bangladesh

Agar wood is one of the most expensive non-timber wood products of the world. It has a series of names around the Asia and Pacific region. Bangladesh is producing three major products namely Agar-wood, Agar-oil and Agar dust/powder in Agar wood sector. It has multidimensional uses in perfume, cosmetics and medicine sector. Agar wood has traditional, religious moreover cultural uses in different parts of the world. Major Agar wood producing countries are India, Malaysia, Indonesia, Vietnam, Cambodia, Laos, Brunei, Singapore and Bangladesh. Major market/consumers of Agar wood are the Middle East (UAE, KSA and other Arabian countries) and North East Asian countries (Taiwan, Japan and Korean Republic). It has a long history in Moulvi Bazar and nearby districts of Bangladesh too. A very few constraints are creating hurdles toward flourishing this sector in Bangladesh. In this study we would be very much concentrated to identify development barriers of Agar wood sector in Bangladesh and generate few recommendations in this regard. Proper regulatory support from the government could play a very vital role to make it one of the major foreign currency earning sectors for Bangladesh.

English

Coffee is the main source of income for a large number of households and provides major foreign currencies for the national economy. Although it covers only 6.3% of cultivated areas, coffee is produced by 500,000 households in Rwanda. Since its introduction in the country in 1904, coffee has always been operated by individual producers. However, after the liberalization of the coffee sector in 1998, the quantity and quality produced are steadily decreasing which affects the price and the income of coffee growers. To search for a solution, the Rwandan government has striven to consolidate coffee growing cooperatives in different parts of the country by building coffee washing stations (CWS). These stations were mainly meant to help coffee growing cooperatives produce high quality coffee which is competitive on the international markets. The goal is to produce a fully washed coffee, exportable and better valued on the international market, which can provide better income for peasant producers. Coffee washing stations are then seen as tools to produce quality coffee and are mainly run by cooperatives, which are structural elements in the rural world. Through these facilities and structures put in place, jobs are created for coffee producers, loans are granted, farmers are motivated due to the increase of income, the market is guaranteed and the prices become attractive for the members. Thus, the socio-economic welfare of coffee growers is improved, which reduces poverty among coffee growers’ households. This study focuses on the analysis of the role resulting from the structuring action of the peasants through CWS and coffee cooperative KOPAKAMA Rutsiro District, Western Province of Rwanda on the socio-economic development and technical assistance of members and the surrounding world. Key words: KOPAKAMA Cooperative, coffee, coffee washing stations (CWS), growers, socio-economic development.