This paper is dedicated to studying and modeling the interdependence between the oil returns and exchange-rate movements of oil-exporting and oil-importing countries. Globally, twelve ...countries/regions are investigated, representing more than 60% and 67% of all oil exports and imports. The sample period encompasses economic and natural events like the Great Recession period (2007–2009) and the COVID-19 pandemic. We use the dynamic conditional correlation mixed-data sampling (DCC-MIDAS) model, with the aim of investigating the interdependencies expressed by the long-run correlation, which is a smoother (but always daily observed) version of the (daily) time-varying correlation. Focusing on the advent of the COVID-19 pandemic in 2020, the long-run correlations of the oil-exporting countries (Saudia Arabia, Russia, Iraq, Canada, United States, United Arab Emirates, and Nigeria) and (lagged) WTI crude oil returns strongly increase. For a subset of these countries (that is, Saudia Arabia, Iraq, United States, United Arab Emirates, and Nigeria), the (lagged) correlations turn out to be positive, while for Canada and Russia they remain negative as before the advent of the pandemic. In addition, the oil-importing countries and regions under investigation (Europe, China, India, Japan, and South Korea) experience a similar pattern: before the COVID-19 pandemic, the (lagged) correlations were negative for China, India, and South Korea. After the COVID-19 pandemic, the correlations of these latter countries increased.
We study the integrated operational and financial hedging decisions faced by a global firm who sells to both home and foreign markets. Production occurs either at a single facility located in one of ...the markets or at two facilities, one in each market. The company has to invest in capacity before the selling season starts when the demand in both markets and the currency exchange rate are uncertain. The currency exchange rate risk can be hedged by delaying allocation of the capacity to specific markets until both the currency and demand uncertainties are resolved and/or by buying financial option contracts on the currency exchange rate when capacity commitment is made. A mean-variance utility function is used to model the firm's risk aversion in decision making. We derive the joint optimal capacity and financial option decision, and analyze the impact of the delayed allocation option and the financial options on capacity commitment and the firm's performance. We show that the firm's financial hedging strategy ties closely to, and can have both quantitative and qualitative impact on, the firm's operational strategy. The use, or lack of use of financial hedges, can go beyond affecting the magnitude of capacity levels by altering global supply chain structural choices, such as the desired location and number of production facilities to be employed to meet global demand.
Due to the potential impact of the (currency) exchange rate risk in the financial market, forecasting exchange rate (FET) has become a hot topic in both academic and practical worlds. For many years, ...the various methods have been proposed and used for FET problems including the method of the artificial neural network (ANN). However, in many cases of FET, there is the limitation of using separate methods since they are not able to fully capture financial characteristics. Recently, more researchers have been beginning to pay attention to FET based on an ensemble of forecasting models (in other words, the combination of individual methods). Previous studies of ensemble methods have shown that the performance of an ensemble depends on two key elements (1) The individual performance and (2) diversity degree of base learners. The main idea behind this paper comes from these key elements, the authors use ANNs as the base method (or weak learners), and weights of these ANNs will be optimized by using multi-objective evolutionary algorithms (MOEAs) including the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Non-Dominated Sorting Differential Evolution (NSDE) using directional information. To assist MOEAs, a number of diversity-preservation mechanisms are used to generate diverse sets of base classifiers and finally we propose to use modified Adaboost algorithms to combine the results of weak learners for overall forecasts. The results show that the proposed novel ensemble learning approach can achieve higher forecasting performance than those of individual ones.
Tourism is one of the world's largest industries and an increasingly important source of foreign currency that is used to finance economic growth. The purpose of this study is to examine the ...long-term and short-term relationships between tourism and economic growth in Iran, by using annual data covering the 1985-2013 period and autoregressive distributed lag and the Error Correction model to examine the relationships between variables. The findings showed that there is a positive relationship between tourism expenditure and economic growth in the long term and short term. The result indicate that there is also positive relationship between the real effective exchange rate (REER), foreign direct investment (FDI) and economic growth. The Granger causality test shows a bidirectional causality running between tourism expenditure and economic growth.
This paper investigates the exchange rate pass-through effect (ERPT) on the aggregate import prices within seven countries of Southeast Europe. The main goal is to investigate whether there is a ...high, as well as a complete pass-through effect in the short-term and long-term for Croatia, Bulgaria, Romania and Slovenia. Compared to the previous survey results, it is found that the “pass-through” is not reduced. On the other hand, the results confirmed the previous results, according to which pass-through effect is higher in transitional countries than in developed countries. In addition, in the short and long term, depreciation leads to the “pass through” asymmetry for Croatia, Bulgaria, FYR Macedonia and Slovenia, while appreciation leads to the “pass-through” asymmetry for Slovenia.
•This paper investigates the exchange rate pass-through effect (ERPT) on the aggregate import prices.•We found that the “pass-through” is not reduced.•We came to the results that confirm previous results.
•Two types of exchange rate flexibility contracts are proposed.•These contracts are studied under uncertain demand and currency exchange rate.•Due to exchange rate volatility, the effective wholesale ...price become uncertain.•The impacts of the contracts parameters on the expected profits are investigated.•Expected profits of both parties improve if payment is made in supplier’s currency.•Numerical examples are provided to illustrate the findings of our study.
This paper analyzes a decentralized global supply chain under a newsvendor setting, where a supplier delivers a certain quantity of a single product to a buyer in accordance with the terms of a mutually agreed upon contract. This contract is signed prior to the delivery of the product and subsequent payment, thus, exposing the supply chain to the risk of currency exchange rate fluctuations. We propose two types of currency exchange rate flexibility contracts to explore the characteristics of exchange rate risk mitigation policies for the buyer and the supplier. Furthermore, we investigate the effects of the contract structures on the optimal order quantity, as well as the expected profits of both supply chain members. Our results show that the optimal order quantity of the buyer decreases when the wholesale price is uncertain due to exchange rate volatility. Also, both our proposed contracts tend to improve the expected profits of both the buyer and the supplier, when the payment is made in the supplier’s currency, indicating the desirability of adopting such contractual agreements from the perspective of both parties. On the other hand, when the payment is made in the buyer’s currency, our suggested contracts do not yield such win-win scenarios. Finally, we examine the effectiveness of availing the services of a local vendor, which is capable of satisfying any demand in excess of the quantity ordered from the foreign source with short notice, in order to mitigate the risks associated with an overseas order.
We find that climate-related risks forecast the intraday data-based realized volatility of exchange rate returns of eight major fossil fuel exporters (Australia, Brazil, Canada, Malaysia, Mexico, ...Norway, Russia, and South Africa). We study several metrics capturing risks associated with climate change, derived from data directly on variables such as, for example, abnormal patterns of temperature. We control for various other moments (realized skewness, realized kurtosis, realized upside and downside variance, realized upside and downside tail risk, and realized jumps) and estimate our forecasting models using random forests, a machine learning technique tailored to analyze models with many predictors.
•Climate-related risks forecast the realized volatility of exchange rate returns.•Study focuses on eight major fossil fuel exporters.•Study uses wide array of metrics capturing climate-related risks.•Study controls for various other moments.•Results are derived using random forests and other machine learning techniques.
The article focuses on determining the impact of the consequences of the russian invasion of Ukraine on macroeconomic stability and the exchange rate of the national currency. The relevance of the ...chosen topic is substantiated. The problems of assessing the negative consequences of the russian invasion of Ukraine are outlined, including: violation of general macroeconomic stability, significant fluctuations in foreign exchange markets and destabilization of the national currency. The study considers the theoretical and methodological foundations of the study of foreign exchange markets and analyzes global trends in the development of the global currency market. The structure of world currency reserves is analyzed according to the assessment of the currency composition of the official currency reserve (COFER) of the IMF. The analysis of the dynamics of average spreads of purchase and sale of reserve currencies against the US dollar for traditional and non-traditional reserve currencies is carried out. As a result of studying world trends, the main factors that affect fluctuations in the exchange rate of currencies are identified. The main problems regarding the functioning of the national currency market in the context of the hostilities in Ukraine are outlined, in particular, the existing inflationary processes are analyzed. The dynamics of currency interventions of the NBU for 2021-2022 is considered. The features (advantages) of the application and the results of using the singular spectral analysis (SSA) method for predicting fluctuations in the US dollar and Euro in the national foreign exchange market are presented. The practical implementation of this method was carried out using the CaterpillarSSA software product. Evaluation of the accuracy of the forecast is performed using the MAPE method. The obtained forecast errors (less than 6%) suggest that the constructed models are adequate and can be used for further research and recommendations. Thus, it can be concluded that for Ukraine the most important factors affecting the exchange rate are seasonality and successful trade policy of the government, which, in turn, largely depends on resource prices. The carried out research allowed to highlight the features and main aspects of the functioning of the foreign exchange market of Ukraine during the period of ongoing military aggression.
The purpose of this study was to develop and evaluate a Long Short-Term Memory (LSTM) model for Forex prediction. The data used was reprocessed and the LSTM model was developed and trained using a ...supervised learning approach with popular deep learning frameworks. The performance of the model was evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. In addition, we examined the literature on energy efficiency, highlighting its potential for reducing computational load and, consequently, energy consumption. We also considered the environmental impact of using such models. The results showed that the LSTM model was effective in Forex prediction and demonstrated superior performance compared to other predictive models. The best model among the several LSTM models evaluated had 90 epochs. These results provide evidence for the efficacy of the LSTM model in Forex prediction and highlight the potential benefits of using deep learning techniques in this field, particularly in terms of energy efficiency and environmental sustainability.
In this study, we assess the dynamic evolution of short-term correlation, long-term cointegration and Error Correction Model (hereafter referred to as ECM)-based long-term Granger causality between ...each pair of US, UK, and Eurozone stock markets from 1980 to 2015 using the rolling-window technique. A comparative analysis of pairwise dynamic integration and causality of stock markets, measured in common and domestic currency terms, is conducted to evaluate comprehensively how exchange rate fluctuations affect the time-varying integration among the S&P 500, FTSE 100 and EURO STOXX 50 indices. The results obtained show that the dynamic correlation, cointegration and ECM-based long-run Granger causality vary significantly over the whole sample period. The degree of dynamic correlation and cointegration between pairs of stock markets rises in periods of high volatility and uncertainty, especially under the influence of economic, financial and political shocks. Meanwhile, we observe the weaker and decreasing correlation and cointegration among the three developed stock markets during the recovery periods. Interestingly, the most persistent and significant cointegration among the three developed stock markets exists during the 2007-09 global financial crisis. Finally, the exchange rate fluctuations, also influence the dynamic integration and causality between all pairs of stock indices, with that influence increasing under the local currency terms. Our results suggest that the potential for diversifying risk by investing in the US, UK and Eurozone stock markets is limited during the periods of economic, financial and political shocks.
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