Future Exchange Rate Research Articles

Management of Exchange Rate Forecasting Through Vanilla Long Short-Term Memory Networks (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA)

Predicting foreign exchange rates presents a formidable challenge within financial forecasting, given its pivotal role in influencing a country's economic trajectory. To address this challenge, numerous forecasting models are employed with the aim of anticipating future exchange rate movements. This study aims to determine the efficacy of two prominent machine learning models, namely Vanilla Long Short-Term Memory Networks (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA), in forecasting the exchange rate between the Malaysian Ringgit (MYR) and the United States Dollar (USD). Employing Python's robust statistical packages for time series forecasting, both Vanilla LSTM and ARIMA models undergo rigorous training on the dataset. Leveraging Python's programming capabilities enables in-depth analysis, essential for model refinement and accuracy assessment. Upon comparing the error measures of both models, it becomes evident that the Vanilla LSTM model outperforms ARIMA, exhibiting lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) values. Specifically, the MSE and RMSE for Vanilla LSTM stand at 0.0102 and 0.1011, respectively, surpassing ARIMA's 0.0113 and 0.1062. Thus, affirming Vanilla LSTM's superiority in exchange rate forecasting. Consequently, the study concludes that Vanilla LSTM emerges as the most accurate model for exchange rate prediction, with a projected exchange rate of RM4.22 for July 2022.

Comparative Studies of Hybrid ARIMA and Artificial Neural Network (ANN) Techniques for Predicting Exchange Rate in Pakistan

For predicting time series data the ARIMA and ANN provides a good technique in the field of research. Time series data often contain both linear and nonlinear patterns. Therefore, neither ARIMA nor neural networks can be adequate in modeling and predicting time series data. When applying the linear models, most existing studies seem to use the same specification for estimation and forecasting, but the dynamic impact of the concerned variables is ignored. In this study combined the ARIMA and Artificial neural network model by adopting both an equal weighted approach and profit weighted approach to capture both th linear and nonlinear components of the exchange rate, and also developed a hybrid techniques by using models of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) and their performance were compared to ANN and Hybrid ARIMA models. The Hybrid models are used for forecasting the future exchange rate for dollar, the exports and imports of data The findings showed that combining both ARIMA and ANN models reap the advantage of linear and nonlinear modeling. The capability of the two models are analyzed based on standard statistical measures such as, mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE). Models effectiveness The effectiveness of the models are analyzed for the foreign exchange rate, imports and exports of the data and concluded that hybrid techniques provided the best forecasting results.

Exchange rates and fundamentals: Forecasting with long maturity forward rates

We show that in a popular model of exchange rate determination, the unobserved expected future exchange rate can be substituted with the observed forward exchange rate. This allows the derivation of a new error-correction forecasting model, which approximates the gap between the fundamental equilibrium exchange rate and the actual exchange rate with the long-maturity forward exchange rate. Our out-of-sample forecasting results for major currencies are unprecedented. The forecasting model is simple, easy to replicate, and the data we use are available in real time and not subject to revisions.

Unveiling the potential: Exploring the predictability of complex exchange rate trends

Forecasting exchange rates is challenging due to its diverse features and complex patterns. Inspired the theory of receptive fields, we proposed two models: the Transformer with Convolutional Neural Network (CNN-Transformer) and the Long Short Term Memory networks with Convolutional Neural Network (CNN-LSTM). These models leverage Convolutional Neural Network (CNN) modules to expand the learnable timestep of Long Short Term Memory networks (LSTM) and reduce the complexity of Transformer. Generally, CNN-LSTM demonstrates the highest predictive accuracy. Furthermore, there is a silver lining: the sentiment in past news is closely linked to future exchange rates. In order to introduce the news sentiment to improve the performance of models, we fine-tune a pre-trained model, Bidirectional Encoder Representations from Transformers with Whole Word Masking (BERT-WWM), to extract the sentiment information from news dataset. However, the effectiveness of introducing news sentiment is greatly dependent on the timesteps. Longer timesteps often increase the likelihood of success for this method. We believe this pattern relates to the timeliness of news and the delayed impact of news events. Also, it's important to note that the content of news text significantly influences the forecasting performance.

Prediction Of the Impact of The Exchange Rate Between Euro and US Dollar Based on BP Neural Network

In order to forecast the euro's future exchange rate against the US dollar while on the subsequent years., the writers use the self-programmed backpropagation neural network to import the opening and closing rates of the exchange rate for the latest 10 years from the US dollar to the euro and selects 1044 sets of data from them. Among them, 844 sets are used as the training set and the test set is comprised of the remaining 200 sets. A training model is established and then the nonlinear exchange rate changes are predicted using this training model. After repeated random simulation, conclusions are drawn, and results with a reasonable gap between the actual data are obtained to complete the training and finally achieve the purpose of prediction. Judging from the final experimental results, this training model has a high accuracy, and the backpropagation neural network is expected to achieve a rapid, relatively accurate and valuable prediction of the Euro to US dollar conversion rate. In the meantime, it can also provide some suggestions for the future data model from this experiment and put forward suggestions for correction.

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).

Predicting multi-horizon currency exchange rates using a stacked ensemble of random forest and SVR

A thorough exploration of the effects of a given minute’s currency exchange rates on subsequent 1, 5, 10, 15, 30, 45, and 60 minutes’ currency exchange rates is presented in this article, with machine learning and ensemble methods being applied. The focus is on twelve currency pairs, including EUR/AUD, EUR/GBP, and EUR/PLN, with a data set of per-minute logs of these pairs’ exchange rates from 2022 being leveraged. A stacked ensemble of Random Forest and Support Vector Regression (SVR) is used to predict future exchange rates. A comparison of this model is also made with the single RF, single SVR, and an average ensemble of RF and SVR models. The research method is further fortified by the use of k-fold cross-validation and ANOVA tests. The findings of the study reveal significant predictive accuracy of the stacked ensemble model, emphasizing the intricate interconnections of currency exchange rates. The potential of machine learning and ensemble techniques in predicting short-term currency exchange rates is underlined, thereby augmenting financial forecasting research.

The Impact of the Interest Rates Raised by Federal Reserve System on the Exchange Rate of the US Dollar Against the Chinese Yuan

On March 16, 2022, the U.S. raised interest rates for the first time in response to inflationary pressures, which announced a 25-basis point increase. After the latter four rate hikes happened in May, July, September and November, the cumulative increased total is 425 basis points. This change had a huge impact, especially on the exchange rate between the US dollar and the Chinese currency. This paper uses data on the exchange rate of the US dollar to the Chinese yuan after 2010, and ARIMA model is built to model and analyze the data in order to study the impact of the Federal Reserves interest rate hike on the exchange rate of dollar and yuan. Therefore, the analysis discovered that the Feds interest rate increase caused the appreciation of the USD and the depreciation of the CNY. Furthermore, the model helps to predict the trend of the future exchange rate, which in turn will provide suggestions for Chinas future development.

Forecasting Exchange Rates and Trade Balances: An ARIMA Analysis of USD/RMB and China's Trade Dynamics

This paper utilizes an ARIMA model to predict the future exchange rate and trade balance of China. The primary data is the monthly average exchange rate of USD/RMB, while China's monthly trade balance serves as auxiliary data. The findings indicate a projected downward trend in the USD/RMB exchange rate, continuing from 2023 to 2024 and stabilizing around 6.7 from 2024 to 2025. This implies a depreciation of the Chinese currency against the US dollar. Additionally, China's trade balance is expected to experience modest growth over the next two years, albeit at a significantly reduced rate compared to previous years. These projections highlight the challenges faced by Chinese exporters and suggest evolving global trade dynamics. The paper discusses policy implications for managing exchange rate fluctuations and sustaining balanced trade relations. It emphasizes the usefulness of the ARIMA model for forecasting exchange rates and trade balances while acknowledging the limitations and potential impact of unforeseen events or policy changes on the outcomes. The study concludes by suggesting avenues for future research to improve the accuracy and robustness of such forecasts, encouraging continued exploration in this dynamic field of study.

Exchange rate volatility predictability: A new insight from climate policy uncertainty

Against the background of growing concern about climate change, this study attempts to examine the impact of US climate policy uncertainty on exchange rate volatility. Specifically, this paper explores whether climate policy changes and their extreme observations, as well as the short-term asymmetry of exchange rate volatility, can help predict future exchange rate volatilities. The in-sample estimation results show that the uncertainty of US climate policy significantly affects the volatility of the US exchange rate, and extreme positive shocks of climate policy uncertainty have a greater impact on the exchange rate than extreme negative shocks of the same magnitude. In addition, we find significant asymmetries in the short-term components of exchange rate volatility, especially in the USD-CNY exchange rate and the USD-EUR exchange rate. Multiple out-of-sample forecasting tests show that models incorporating extreme values of climate policy uncertainty exhibit superior forecasting performance. Notably, the predictive performance of these models tends to be stronger during periods of low volatility and relatively weak during periods of high volatility. This paper provides valuable insights for stakeholders to make informed decisions and optimize strategies in the face of uncertain climate policies.

On the driving forces of real exchange rates: Is the Japanese Yen different?

We estimate variance decompositions of the real exchange rate (q) for 19 currencies based on a present-value relation. At very short horizons, the driving force of q is predictability of the future exchange rate. At long horizons, return predictability drives most variation in q, with predictability of interest differentials playing a secondary role. This pattern is especially strong for the Non-G10 currencies. However, the long-run predictability mix associated with the Japanese Yen clearly deviates from the other currencies and is unstable over time. The quantitative simulation of a liquidity-based exchange rate model largely replicates our main empirical findings.

Liquidity yield and exchange rate predictability

In this paper, we extend the Taylor rule model of exchange rate determination by incorporating the liquidity yield on government bonds, and investigate exchange rate predictability from the augmented Taylor rule model. We find that the liquidity yield on government bonds delivers additional predictive power to future exchange rate movements beyond the model with Taylor rule fundamentals, using both in-sample and out-of-sample tests. In particular, the augmented model with liquidity yield exhibits superior predictive power after the currency swap market frictions are controlled.

Autoregressive conditional durations: An application to the Surinamese dollar versus the US dollar exchange rate

AbstractThe parallel market nominal exchange rate of the United States dollar vis‐à‐vis the Surinamese dollar (USD/SRD) exhibited periods of severe volatility which were often followed by episodes of stability, usually at a cost of sharp depreciations. This study seeks to model this exchange rate using autoregressive conditional duration models. These models are suitable for modelling events occurring with irregular intervals. Exchange rates in developing countries have distinct features compared to exchange rates in countries with well‐established and accessible financial markets. A key feature is that for these developing countries, exchange rates only occasionally experience jumps. Our findings suggest that past exchange rate changes appear to be a significant driver of future exchange rate jumps. Furthermore, our results show that money, international reserves, and commodity prices can explain jumps in the market USD/SRD exchange rate.

Dynamic Changes in US Technical Company under Uncertain Market

Since the outbreak of the COVID-19 epidemic, great changes have taken place in the world's economic situation. The interest rate increase by the Federal Reserve has become one of the most influential actions. Researchers have found that the Fed's interest rate hike not only has an impact on the American financial market but also on the world economic situation to a certain extent. Therefore, this paper collects the share price of US dollar and the exchange rate between US dollar and RMB since June, 2021, and uses VAR model and ARMA-GARCH model to model and analyze the data, to examine how the Fed's interest rate hike will affect the dollar's value and Apple's share price, and to predict the future exchange rate and the development trend of Apple's share price in the case of the Fed's interest rate increase, so as to further study the dynamic changes of American technology companies in the uncertain market.

Forex market as the best possible way of investing money during an economic boom and recession

Abstract Since the beginning of the 1980s, a continuous process of integration of national and regional markets into one global market for goods, services and capital can be noticed. Both economic theory and market practice indicate that the level of the exchange rate primarily depends on macroeconomic variables (such as interest rates or the number of new jobs in the non-agricultural sector). The results of the research presented in the article regard the importance of US macroeconomic data publications for the short-term volatility of EUR/USD exchange rate. The main purpose of the study was to show whether macroeconomic data from the United States affects the short-term development of the EUR/USD exchange rate and whether the Forex market is a good way to multiply capital. The following research questions have been posed: does the EUR/USD exchange rate react to the published macroeconomic data from the American economy? Second, is whether investing in the Forex market could be a way to multiply capital in times of economic boom and recession. This paper presents the effects of my own research and observations in terms of the impact of US macroeconomic data, on shaping exchange rates of the Forex market. Based on my own investment experience my goal is to prove, that Forex market is a perfect way to multiply capital. My investment decisions regarding future exchange rate fluctuations, were based on the presented macroeconomic data from the US economy, as well as on the basis of important leading economic indicators. The position was opened and closed on the same day. The trading contracts have been made throughout 7 working days. The underlying financial instruments were EUR/USD and OIL. The conclusions of this study are as follows. The USD/PLN exchange rate reacted to the published macroeconomic data from USA. The strongest exchange rate reaction was noticed after publications of data regarding US Non-farm Payrolls (NFP), Initial Jobless Claims and ISM Services PMI. Strong exchange rate reaction was recorded after ADP US Private Sector Jobs and Factory orders report. When taking described investment examples into account, it can be clearly stated that investing in the Forex market is an excellent alternative to stock investments.

Yield curve shapes and foreign exchange rates: the term structure of interest rates model approach

ABSTRACT Exchange rates play a crucial role in the international economy. This study investigates the explanatory power of yield curve factors in exchange rate predictions for countries in three currency pairs: Japanese yen (JPY)–US dollar (USD), British pound (GBP)–USD, and Canadian dollar (CAD)–USD. The results empirically establish that each country’s yield curve factors, extracted from the term structure of interest rates model, play a key role in determining future exchange rates. Specifically, an increase in the US level factor causes depreciation in the USD relative to the JPY for the full sample period (June 1994–October 2020) and in the GBP and CAD for the post-global financial crisis period. The study’s model provides more useful information than the baseline uncovered interest rate parity model for explaining the variations in the three currency pairs. Additionally, the yield curve factors also explain the variation in countries’ excess currency returns. This study has important theoretical implications for identifying which of the two countries’ yield curve factors predominantly explain the exchange rate changes and the related policy implications. It also provides key policy insights by substantiating the association between macroeconomic variables and exchange rate changes.

Trading Prediction Model and Strategy Formulation Based on Grey System

In this paper, we study the optimal investment strategy between gold and Bitcoin, establish a market price forecasting system using the grey forecasting model GM (1,1), and calculate the optimal investment between gold and Bitcoin through the association rule algorithm. Optimal investment ratio, and then determine the maximum return. The establishment of predictive investment models helps investors to formulate optimal investment plans. The established model can use certain data from previous years to predict the future exchange rate prices of gold and bitcoin and help trading investors make optimal buying and selling decisions. It has a certain model planning ability for investment in real life and has specific practical significance for trade investors.

Modeling the stochastic volatility of MAD/EURO and MAD/USD the exchange rates by the Bayesian approach and the MCMC (Monte Carlo Markov Chain) algorithm

PurposeOne of the crucial issues in the contemporary finance is the prediction of the volatility of financial assets. In this paper, the authors are interested in modelling the stochastic volatility of the MAD/EURO and MAD/USD exchange rates.Design/methodology/approachFor this purpose, the authors have adopted Bayesian approach based on the MCMC (Monte Carlo Markov Chain) algorithm which permits to reproduce the main stylized empirical facts of the assets studied. The data used in this study are the daily historical series of MAD/EURO and MAD/USD exchange rates covering the period from February 2, 2000, to March 3, 2017, which represent 4,456 observations.FindingsBy the aid of this approach, the authors were able to estimate all the random parameters of the stochastic volatility model which permit the prediction of the future exchange rates. The authors also have simulated the histograms, the posterior densities as well as the cumulative averages of the model parameters. The predictive efficiency of the stochastic volatility model for Morocco is capable to facilitate the management of the exchange rate in more flexible exchange regime to ensure better targeting of monetary and exchange policies.Originality/valueTo the best of the authors’ knowledge, the novelty of the paper lies in the production of a tool for predicting the evolution of the Moroccan exchange rate and also the design of a tool for the monetary authorities who are today in a proactive conception of management of the rate of exchange. Cyclical policies such as monetary policy and exchange rate policy will introduce this type of modelling into the decision-making process to achieve a better stabilization of the macroeconomic and financial framework.

Backtesting the evaluation of Value-at-Risk methods for exchange rates

PurposeSmall- and medium-sized enterprises can be highly affected by losses caused by exchange rate changes. The aim of this paper was to find the optimal Value-at-Risk (VaR) method for estimating future exchange rate losses within one year.Design/methodology/approachThe analysis focuses on five VaR methods, some of them traditional and some of them more up to date with integrated EVT or GARCH. The analysis of VaR methods was concentrated on a time horizon (1–12 months), overestimation predictions and six scenarios based on trends and variability of exchange rates. This study used three currency pairs EUR/CZK, EUR/USD and EUR/JPY for backtesting.FindingsIn compliance with the backtesting results, the parametric VaR with random walk has been chosen, despite its shortcomings, as the most accurate for estimating future losses in a medium-term period. The Nonparametric VaR confirmed insensitivity to the current exchange rate development. The EVT-based methods showed overconservatism (overestimation predictions). Every parametric or semiparametric method revealed a severe increase of liberality with increasing time.Research limitations/implicationsThis research is limited to the analysis of suitable VaR models in a long- and short-run period without using artificial intelligence.Practical implicationsThe result of this paper is the choice of a proper VaR method for the online application for estimating the future exchange rate for enterprises.Originality/valueThe orientation of medium-term period makes the research original and useful for small- and medium-sized enterprises.

Predicting Exchange Rate under UIRP Framework with Support Vector Regression

This study aimed to forecast the exchange rate between the Vietnamese dong and the US dollar for the following month in the context of the COVID-19 pandemic. It used the Support Vector Regression (SVR) algorithm under the Uncovered Interest Rate Parity (UIRP) theoretical framework; the results are compared with the Ordinary Least Square (OLS) regression model and the Random Walk (RW) model under the rolling window method. The data included the VND/USD exchange rate, the bank interest rate for the 1-month term, and the 1-month T-bill from January 01, 2020, to September 11, 2021. The research discovered a linear link between the two nations' exchange rates and interest rate differentials. Interest rate differentials are input variables to forecast interest rate differentials. Furthermore, the connection between the exchange rate and interest rate differentials during this era does not support the UIRP hypothesis; hence, the error for OLS predictions remains large. The study provided a model to forecast future exchange rates by combining the UIRP theoretical framework and the SVR algorithm. The UIRP theoretical framework can anticipate exchange rate differentials using the input variable and the interest rates between two nations. Meanwhile, the SVR algorithm is a robust machine learning technique that enhances prediction accuracy. Doi: 10.28991/ESJ-2022-06-03-014 Full Text: PDF