Exchange Rate Data Research Articles

A neural network model for predicting price changes of the exchange rate EUR/USD

With the rapid development of the global economy, economic exchanges and cooperation between countries have become increasingly frequent, and the importance of the foreign exchange market has become increasingly prominent. As a link between international trade, foreign exchange plays a crucial role in formulating relevant financial policies and the risk management of multinational enterprises. However, accurately predicting foreign exchange changes is very challenging because the foreign exchange market is characterized by non-linear and dynamic changes and exchange rate fluctuations are also affected by historical data. The analysis method based on time series has always been an effective solution for studying exchange rate issues, and the model trained by the deep learning model based on time series data can further simulate and predict the exchange rate. Therefore, this article uses the BP neural network algorithm based on the time series model to predict the price change of the exchange rate EUR/USD. This article first uses the historical exchange rate data of the euro against the US dollar as a training set and performs data preprocessing. Secondly, the activation function and learning rate of the model are tuned, and the performance of the model is compared by evaluating indicators to improve prediction accuracy. The final experimental results show that the neural network model is highly accurate and predictive in predicting EUR/USD exchange rate price changes.

Exchange rate forecast between Euro and US dollar based on recurrent neural network

In the global financial market, the euro and the US dollar are the two most important currencies. The euro is the official currency of EU member states and the second largest trading currency in the world. Its exchange rate is determined by market supply and demand. The US dollar is the world's largest trading currency, and its position in international trade and financial markets is irreplaceable. They play an important role in international trade, financial markets, and travel. It plays an important role in various aspects and has a profound impact on the global economy. The relative value of these two currencies is influenced by various factors, and predicting their exchange rate movements is of great significance for investors, economists, and policymakers. This paper introduces a method to predict the exchange rate between Euro and US dollar using recurrent neural network (RNN). By processing and analyzing historical exchange rate data, the RNN-based model is trained and used to predict future exchange rate changes. Our model shows high accuracy, which proves the predictive ability of RNN in this respect. Deep-learning-based forecasting models and their applications in finance have attracted considerable attention in recent research (Fischer and Krauss 2018; Huck 2019; Kim et al. 2019).

Pemodelan ARIMA-GARCH dalam Peramalan Kurs Rupiah Terhadap Yen dengan Masalah Keheterogenan Ragam

The currency exchange rate is the price of a country's currency expressed into another country's currency. At the beginning of 2020, the COVID-19 pandemic affected the weakening and changes in the Rupiah exchange rate against hard currencies, one of which was the Japanese Yen. This affects the expectations of LCS cooperation between Indonesia and Japan in terms of increasing the value of trade to investment between the two countries. Therefore, forecasting the upcoming currency exchange rate is indispensable to determine the upcoming macroeconomic policy. ARIMA is a commonly used quantitative method to forecast future data using past data patterns. The weakness of this method arises when the data violates the assumption of homogeneity of variety that often occurs in financial data, one of which is currency exchange rate data. The ARCH/GARCH model is an effective model for data with uncertain diversity characteristics. However, there is potential to combine ARIMA and ARCH/GARCH into an ARIMA-ARCH/GARCH hybrid model to obtain forecasting results with greater accuracy. In this study, the minimum return data on the Indonesian Rupiah (IDR) exchange rate against the Japanese Yen (JPY) shows the results that the ARIMA(0,0,1) model provides RMSE accuracy of 0.008. While the best forecasting model that can be used to forecast the maximum return data of the IDR exchange rate against JPY is ARIMA(1,0,0)-GARCH(1,1) with a small RMSE accuracy of 0.014. The forecasting results for the minimum return data for buying and selling are expected to strengthen the exchange rate. Meanwhile, the forecasting results for the maximum return data for buying and selling are expected to experience exchange rate weakening.

Identifying the Probability Distribution of Exchange Rates in Sri Lanka

The exchange rate is one of the main components that represents the status of a country’s economic condition. This study mainly focuses on identifying an appropriate distribution that can capture the asymmetric behaviour of important currency exchange rates related to Sri Lanka. The true behaviour of the exchange rates of USD, EURO, JPY, AUD, GBP, CHF, SGD and CAD in terms of LKR was determined using flexible distributions of generalised lambda distribution (GLD), Normal Inverse Gaussian and Skew-Normal. The parameter estimation was carried out by maximum likelihood estimation (MLE), while for GLD, few more parameter estimation techniques were applied. To understand how well the fitted distributions identify the real behaviour of data, we conducted the goodness-of-fit (GOF) tests. The results from Cramer–Von Mises (CvM) and Anderson–Darling (AD) GOF tests pointed out that the exchange rates data follow a GLD with the parameter estimation of maximum product of spacings (MPS). Further, plots of histograms with theoretical densities, cumulative distribution functions (CDFs) with empirical CDF and Quantile–Quantile plots illustrated the same idea. This study presented the findings based on different suitable parameter estimation techniques and this study is the first investigation on the distribution identification of exchange rates in Sri Lanka. JEL Codes: C12,C13,G10

Forecasting multi‐frequency intraday exchange rates using deep learning models

AbstractThis paper examines the behavior of currencies' intraday exchange rates with mainly focuses on predicting these behaviors through deep learning models. The time series data are used in this study and comprise intraday exchange rate data for seven volatile currencies, recorded at two different frequency intervals: 1 h and 30 min. The data cover the time frame from January 1, 2018, to December 31, 2020. Firstly, wavelet maximal overlap discreet wavelet transform (MODWT) type “haar” is applied in order to identify the noise representing the volatile trend. Then learning models are applied to historical data that includes support vector regression (SVR), recurrent neural network (RNN), and long short‐term memory (LSTM). The main findings of the study provide strong evidence that the deep learning technique (i.e., LSTM) outperforms the other compatible models. This has been confirmed by the statistical measures for accuracy purposes. This study intends to address complex trends exhibited by volatile behavior of intraday exchange rates through non‐conventional learning techniques for the time of need‐based for the currency traders and other stakeholders.

The Effect of Exchange Rate and Exchange Rate Uncertainty on Domestic Consumption in SAARC Member Countries from 2005 to 2020

The exchange rate and its uncertainty are the key economic variables that influence other macroeconomic variables through various channels. Domestic consumption will be influenced by the exchange rate and its uncertainty. This research investigates the impact of the exchange rate and its uncertainty on the private sector consumption of the member countries of the South Asian Association for Regional Cooperation (SAARC) from 2005 to 2020. The data has been extracted from the World Bank websites, the Asian Development Bank, and the Ministry of Finance of Afghanistan. The uncertainty of the exchange rate data has been extracted using the ARCH method, and the research model has been estimated using the GLS method. The research results indicate that the exchange rate and its uncertainty have a significant and positive effect on private-sector consumption. The positive relationship between the exchange rate and its uncertainty with consumption is consistent with the theory of currency pass-through. Additionally, the impact of income on domestic consumption is positive and statistically significant, while the impact of government consumption expenditure on private-sector consumption is negative and significant.

ARDL Inference for Some Macroeconomic Variables on Nigerian Stock Prices

Abstract This study presents inferences about the effect of quarterly returns of some macroeconomic variables on Nigerian stock prices (NSP) by means of the autoregressive distributed lag (ARDL) model. Four models were studied using NSP, inflation rate (IFR), broad money supply (BMS), and exchange rate (EXR) data that spanned from 1999 to 2021. The rolling correlation was first carried out to test the significance of signals between each input variable and NSP. In testing for co-integration existence and to account for short- and long-term relationships between the study variables, the ARDL bounds test and error correction model (EMC) were utilized, respectively. Also, in determining the stability of the estimated parameters, the recursive cumulative sum (RCUSUM), RCUSUM of squares, and recursive moving sum (RMOSUM) charts were applied. The first model results revealed that at 5% IFR, it has no influence on NSP, both in the long run and short run. However, the second model revealed that BMS has a significant positive effect, both in the long run and short run, on NSP. The third model results showed that the EXR effect on NSP is not significant at 5% in the long run, but in the short run, it significantly negatively affects NSP in the second and third lags. Results from the fourth model revealed that IFR at lag one and BMS are positive and significant in influencing NSP in the long run. In contrast, EXR has a significant negative influence on NSP in the short run, but BMS has a positive and significant effect on NSP in the short run at 5%. The RCUSUM, RCSUMS, and RMOSUM charts revealed that the estimated parameters for the respective models are stable. The study concludes that NSP is influenced negatively by EXR and positively by IFR and BMS. The study recommends that the monetary policy rate should be controlled to lower the cost of borrowings, enhance liquidity levels in the stock market, and develop policies that can have a combined positive impact on stock prices.

PREDICTING THE JAPANESE YEN'S VOLATILITY IN RELATION TO THE INDONESIA RUPIAH

Japan has experienced low inflation and continuous deflation since the economic bubble of the 1980s. Following the COVID-19 pandemic, the world was preoccupied with deflation in various countries, worsening Japan's financial condition. As a result, the Japanese currency's exchange rate could not compete with other currencies. This situation significantly affects the value of Indonesia's exports and imports, as Japan is one of the leading export destinations and a primary supplier of implications for Indonesia. The purpose of this study is to forecast the volatility of the exchange rate between the yen and the IDR for the future. The data used in this research is the buying rate variable from the yen to IDR exchange rate data from January 3, 2020, to November 27, 2023. The method used for the high-volatility data is the GARCH model. The best modeling obtained is GARCH(0,1). The forecast results provide an insight into the buying exchange rate of the yen against the IDR until the beginning of 2024.

A Study of Bayesian Quantile Regression for Forecasting RMB Exchange Rates

Accurate forecasting of the RMB exchange rate is crucial for global financial market participants. This study proposes a Bayesian quantile regression approach to enhance the forecasting method. This paper uses RMB and US dollar exchange rate data from the State Administration of Foreign Exchange from 2018 to 2022 to build a Bayesian quantile regression model and empirically analyze the RMB exchange rate forecast. The results show that the proposed Bayesian quantile regression model yields accurate forecasts, with a root mean squared error (RMSE) of 1.8329 and a mean absolute error (MAE) of 1.2988. Furthermore, robustness and sensitivity analyses confirm the model's reliability. The findings of this study have practical implications for financial market participants and policymakers in managing and responding to foreign exchange risk.

Are Global Factors Useful for Forecasting the Exchange Rate?

Abstract This paper applies a common factors analysis framework to an exchange rate data panel, in order to better understand the forces behind exchange rate dynamics and provide a set of variables for exchange rate forecasting. Results demonstrate the model’s ability to extract key information to under-stand the driving forces of exchange rate movements using the exchange rate Brazilian Real to U.S. dollar; in addition, the model also shows statistically significant improvement in terms of forecasting performance in relation to the random walk benchmark as well as to traditional macroeconomic models found in economics literature. JEL classification numbers: F31, F37, F47. Keywords: Exchange rate, Common factors, Forecasting.

The Impact of Inflation and Exchange Rate on Stock Market Returns in Tanzania

The influence of exchange rate and inflation on stock market returns in Tanzania was investigated using monthly inflation and exchange rate data from the BOT and NBS, as well as monthly market returns from the DSE, over the period 2011 to 2020. This effect was investigated using the autoregressive distributed lag (ARDL), cointegration approach, and the error correction parameterization of the ARDL model. The results from the analysis show that in both the short-run and long-run, stock market return is impacted by inflations rate and exchange rate. Moreover, the empirical results demonstrate the significant negative impact of inflations rate and exchange rate in long run on stock market return while there is positive impact between stock market return and exchange rate and inflation at short run on the basis of the above analysis it can be conclude that two selected macroeconomic variables are relatively significant and likely to impact stock market return of the Dar es salaam stock exchange basing on their coefficients and their significance relationship both in short and long run impacts of which its consistence with theories .The outcome of this thesis discovered potential need to focus on impact of macroeconomic variables in designing monetary and fiscal policy of the country’s economy, since; the capital market does respond quickly to the arrival of new information.

Uncovered interest rate, overshooting, and predictability reversal puzzles in an emerging economy

By using realized and survey-based expected exchange rate data, the paper presents five key findings regarding the Uncovered Interest rate Parity (UIP) and related puzzles in an Emerging Market (EM). First, Fama regressions, when not accounting for shifts in the UIP relationship, yield slopes that are statistically identical to one, irrespective of whether survey-based expected exchange rates or realized exchange rates are used. Second, caution is necessary however, as our analysis identifies three distinct sub-periods within each exchange rate measure, each exhibiting varying levels of puzzling behavior. Third, under realized exchange rates, expectation errors can introduce both downward and upward biases or no bias at all, depending on the sub-period. On the other hand, currency risk premiums consistently lead to a downward bias. Under expected exchange rates, currency risk premiums continue to exert a downward bias at varying degrees across sub-periods. Fourth, responses to interest rate differential shocks by expectation errors are pivotal in inducing both downward and upward biases or removing biases altogether when utilizing realized exchange rate data. Fifth, evidence concerning overshooting and reversal puzzles, as well as their link to the UIP puzzle, varies depending on the specific sub-period and the choice of exchange rate measurement, making it more intricate than the previous literature has documented.

Least squares estimators for reflected Ornstein–Uhlenbeck processes

In this article, we investigate the parameter estimation problem for reflected Ornstein–Uhlenbeck processes with mean reversion. Both estimates based on either continuously or discretely observed processes are considered. The explicit formulas for the estimators are derived using the least squares method. Under regular conditions, we obtain the strong consistency and establish the asymptotic normality for the estimators. Simulation results demonstrate that the performance of our proposed estimators for the drift parameters is superior to the moment estimators. The currency exchange rate data is used to illustrate the theoretical results.

Economic Factors Influencing Housing Prices in Pakistan

The aim of this research is to explore the factors influencing housing prices in Pakistan. The author used monthly time series data for the period from 2011 to 2020, which were obtained from different sources: housing prices data from zameen.com, Karachi interbank offered rate (KIBOR) as a proxy for monetary policy, consumer price index as a proxy for inflation, and exchange rate data from the State Bank of Pakistan. Various methods, such as autoregressive distributed lag (ARDL), comparative analysis and deductive analysis were employed. Before using the ARDL technique, a proper lag length was selected, which turned out to be 11 months. Various diagnostic tests indicated model stability with no autocorrelation or structural breaks. The author concluded that the KIBOR rate negatively affected housing prices, while inflation and exchange rates affected house prices positively.

Exchange rate And Enterprise Investment Efficiency

With the change of the political and economic situation at home and abroad, the economic policy and economic environment also change accordingly, and the exchange rate, as an important macroeconomic policy indicator, is also constantly changing. Based on the data of the actual effective exchange rate of RMB released by the Bank of International for International Settlements from 2010 to 2022, this paper verifies the relationship between the actual effective exchange rate of RMB and the investment efficiency of China's A-share listed companies. After empirical analysis, the research results of this paper show that: The rise of the real effective exchange rate of RMB reduces the degree of non-efficient investment of enterprises, mainly reducing the degree of insufficient investment of enterprises, and thus improving the investment efficiency of listed companies. In addition, this paper makes an in-depth analysis from the two aspects of equity concentration and enterprise scale, and concludes that in small-scale enterprises and enterprises with high equity concentration, the positive impact of RMB appreciation on enterprise investment efficiency will be more significant.

Comparative Analysis of Exchange Rate Models for Eastern African Countries

This paper presents a comprehensive study comparing three distinct models including Autoregressive Integrated Moving Average (<i>ARIMA</i>), Geometric Brownian Motion (<i>GBM</i>), and Artificial Neural Networks (<i>ANN</i>) for predicting Rwandan franc exchange rates in Eastern Africa countries. Utilizing historical exchange rate data from the National Bank of Rwanda (<i>BNR</i>), predictive models are constructed for each method. The analysis reveals that both the <i>ANN</i> and <i>ARIMA</i> outperform <i>GBM</i> in accurately approximating Rwandan franc exchange rates. These findings highlight the superior forecasting capabilities of <i>ARIMA</i> and <i>ANN</i> for Eastern African exchange rates, while providing insights into the performance of <i>GBM</i> in comparison.

Mapping the Causal Connections among Exchange Rate Indicators and Exchange Rate: New Evidence from NARDL Econometric Approach

The aim of the study is to find out the symmetric or asymmetric relationship betweenthe macroeconomic fundamentals and exchange rate of Pakistani Rupee against the US Dollar which has never been analyzed briefly in previous literature. The NARDL approach hasbeen applied with the selected macroeconomic fundamentals i.e., GDP, foreign reserves, inflation rate, interest rate, oil price, gold price, trade balance, and money supply for thedata analysis. The data of exchange rate and selected macroeconomic fundamentals havebeen taken during the time period of 2011 to 2022 from the official IMF IFS database. Thefindings indicate that foreign reserves and inflation possess an asymmetric relationship withthe exchange rate in long run. The increase in productive inflation only helps to stabilize theexchange rate whereas all other significant variables weakens the Pakistani currency eitherin short -run or long run i.e., decrease in money supply, GDP, inflation and increase in interestrate. The findings will be helpful for the policymakers and economists to implement theirpolicies accordingly to prevent the further depreciation the of Pakistani Rupee against USDollar.

DAMPAK KETERBUKAAN EKONOMI TERHADAP PENYERAPAN TENAGA KERJA DI INDONESIA TAHUN 1993-2022

Indonesia's population growth every year is increasing. Population growth certainly results in an increase in the number of labor force. However, this will become a problem in the world of work if the increasing number of labor force is not proportional to the amount of labor absorption. This problem is very important considering its relation to the level of social welfare. This study aims to determine the impact of economic openness on employment in Indonesia. This research is a quantitative study and uses time series data, namely Export, Exchange Rate, FDI and Labor data using the ECM model. The results of this study indicate that in the long run Exports, Exchange Rates and FDI have a positive and significant effect on Absorption of Labor in Indonesia. Whereas in the short term Exports, Exchange Rates and FDI have a positive and insignificant effect on the Absorption of Labor in Indonesia in 1993-2022. And the ECT value in the short term has a negative and insignificant effect which indicates that there is a short-term imbalance in the long term.

Nominal and Real Shocks in the EURALL Exchange Rate. (A SVAR Guide)

Exchange rates are one of the most important topics in the economic and financial sectors. Considering the exchange rate as the price between currencies, financial theories aim to understand the behavior of exchange rates from a more rational viewpoint. Domestic and foreign inflation has a direct impact on the real interest rates and the exchange rates. This paper aims to analyze the nominal and real shocks of the exchange rate between the Euro and Albanian lek [EURALL]. As the most important foreign currency used in the Republic of Albania, the euro is becoming more and more a stable determinant of prices and asset values in the Albanian economy. The vast majority of import and export is dependent on the EURALL trend. That is one of the main reasons why it is so important to study the shocks in the EURALL exchange rate. The Structural Vector Autoregressive (SVAR) models can be used to understand the effects of imposing some long-run restrictions. In addition, some short-run shocks play a major role and continue to have effects even in the long run. This paper takes into consideration the monthly data of exchange rates and inflation of Albania and the Eurozone from 2016 – 2022 (the last 7 years). As one of the most important models, SVAR models can be used in both fiscal and monetary policymaking. Studying nominal and real exchange rates means studying inflation in the eurozone and Albania. Real shocks have a significant impact on both nominal and real exchange rates. Meanwhile, there is no evidence that the nominal shocks (such as institutional interventions or central bank operations) have an important impact on the nominal and real exchange rates. Referring to that, we can conclude that policymaking institutions should be cautious about the real exchange rate.

Exchange Rate Fluctuation and Management of Foreign Donor-Funded Program Budget

The study was about the effect of exchange rate fluctuation on the management of foreign donor-funded program budgets. Humanitarian and development agencies receive funding from foreign donors, who require the program budgets and financial reports in their respective country currencies although the actual utilization of the budget in the host country’s local currency during the program implementation. The study explored currency conversion methods and identified the best principles for effective management of the effect of exchange rate fluctuation. The research design used was both quantitative and qualitative where relevant literature related to exchange methods, donor requirements, and average monthly exchange rate data were reviewed. The sampling year used in this study is 3 years, 2021 to 2023 and the currency sample selected was Uganda Shillings (UGX) and United States Dollars (USD). First In and First Out (FIFO), Weighted average exchange rate methods, and daily spot rate methods were the most common currency conversion methods identified during the study. The study further noted the currency conversion method chosen should be documented in the written financial manual of the grant holder for reference. The study identified budget monitoring, requests for budget amendment, effective planning, provision for contingency reserves in the budget, and constant communication with the donors among the best ways to manage the effect of exchange rate fluctuation. The Study, therefore concluded that exchange rate fluctuation has either adverse or favorable effects on foreign donor-funded program budget and needed to be closely monitored and communicated to the donor for further guidance. Keywords: Exchange rate, Fluctuation, Foreign Donor- funded, Program and Budget.