Forecasting Exchange Rates Research Articles

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.

Forecasting the exchange rate of Nigerian Naira to United State’ Dollar using ARIMA-GARCH Model

In order to model and forecast exchange rates in both developed and emerging countries, majority of time series analysts have employed various technical and fundamental approaches, the forecast outcome differs depending on the approach chosen or implemented. In this view, this study is about hybridization of Autoregressive Integrated Moving Average (ARIMA) with Generalized Autoregressive Conditional Heteroscedastic (GARCH) model in forecasting exchange rate using monthly data of the Nigerian Naira against the U.S. Dollar for the period of January 2002 to February 2020. The stationarity of the exchange rate series is examined using unit root test of Augmented Dickey Fuller (ADF) test and Kwaitkowski-Philips-Schmidt-Shin (KPSS) which showed that the series is non stationary. To make the exchange rate series stationary, the data was transformed by first differencing and appropriate ARIMA models were obtained using Box-Jenkins method. ARIMA (0,1,1) and ARIMA(0,1,2) models were selected using AIC criteria and the residuals of these models were found to be serially correlated and heteroscedastic; hence the need for the hybridization of ARIMA with GARCH model. Therefore ARIMA models were hybridized with GARCH(1,1) to form ARIMA(0,1,1)-GARCH(1,1) and ARIMA(0,1,2)-GARCH(1,1). The results of forecast performance indicates that the best model is ARIMA(0,1,1)–GARCH(1,1) which has the lowest Root Means Square Error (RMSE) and Mean Absolute Error( MAE).

On the Combination of Harmonics and Polynoms in Econometric Modeling of RUB/AZN Exchange Rate

Conducting a combinational polynomial and spectral analysis of time series formed on the basis of daily observations of changes in the RUB/AZN exchange rate with pronounced fluctuations for the period 11.05.2017- 02.11.2018 based on computer econometric modeling.The purpose of the research. The possibility of describing the global rate dynamics by approximation with a combination of a nonlinear polynomial trend and harmonic oscillations of various frequencies relative to this curve; the ability to calculate amplitudes and phases, which can be used to estimate the power spectrum of the Fourier approximation; the ability to develop a high-precision algorithm for predicting exchange rate changes in RUB/AZN.Materials and methodology. The official statistics of the State Statistics Committee of Azerbaijan were used; classical methods of mathematical analysis and economic analysis; methods of econometrics, harmonic (Fourier) analysis, statistical spectral analysis, “Fourier analysis” of the MS Excel add-in, tools of the Eviews 8 application package with the standard deviation and average approximation error being taken into account, the necessary statistical procedures required for identifying and estimating the parameters of the model and checking its adequacy and accuracy.Results. By breaking up the empirical analysis of given time series into time-scale polynomial and time-frequency components. Combinations of the optimal degree of variants of polynomials up to the 11th degree and the number of harmonics of sines and cosines of all possible discrete frequencies were revealed.Conclusion. This result allows us to reconsider the asymmetric impact of RUB/AZN exchange rate pressure on the foreign trade balance between Russia and Azerbaijan. An increase/decrease in exchange rate pressure affects the likelihood of a ruble-manat crisis, while this phenomenon may negatively/positively affect the foreign trade balance and may make it difficult/easier to import resources, goods and services between countries. This, in turn, adds significance to the task of further detailed structuring and analysis of exchange rate changes in RUB/AZN in the face of increased sanction pressures against Russia, thereby actualizing the development of the retrospective part of the study.

Do Divisia monetary aggregates help forecast exchange rates in a negative interest rate environment?

This paper contributes to the literature as the first work of its kind to examine the role and importance of Divisia monetary aggregates and concomitant User Cost Price indices as superior monetary policy forecasting tools in a negative interest rate environment. We compare the performance of Divisia monetary aggregates with traditional simple-sum aggregates in several theoretical models and in a Bayesian VAR to forecast the exchange rates between the euro, the dollar and yuan at various horizons using quarterly data. We evaluate their performance against that of a random walk using two criteria: Root Mean Square Error ratios and the Clark-West statistic. We find that, under a free-floating exchange regime, superior Divisia monetary aggregates outperform their simple sum counterparts and the benchmark random walk in a negative interest rate environment, consistently.

Forecasting exchange rate: A bibliometric and content analysis

The study aims to present a systematic overview of the research in the field of exchange rate projection models through bibliometric techniques and content analysis. First, 775 articles published in journals within the scope of the international Web of Science database from 1966 to May 2021 were analyzed through bibliometric techniques. Second, a selected sample of 69 articles was analyzed through a detail content analysis to identify hot topics and new avenues of interest in the field. The research findings suggest that the scientific production on the subject is in wide development. New approaches have been incorporated, such as neural networks, requiring a broad perspective by the researcher in the evaluation of the empirical results.

International currency markets and the COVID‐19 pandemic

AbstractWe find that quantifying COVID‐19 pandemic shocks is critical to understanding international currency market returns. Scaled by population, shocks from between‐country differences in the number of weekly COVID‐19 deaths are informative in predicting exchange rate returns. Following Alfaro et al. (2020), we estimate the expected number of COVID‐19 deaths based on an exponential model and use it to construct two pandemic shocks that measure the unanticipated number of deaths on a weekly basis and the time‐varying correction of forecast provided new information from the previous week. We document negative impacts of COVID‐19 propagation on currency returns. In addition, we find that the government response, in particular fiscal and monetary stimulus packages, can help mitigate negative effects of COVID‐19 on currency returns. Our findings are robust to country‐specific pandemic measures, window sizes of the exponential model, and the choice of forecast model.

IMF trade forecasts for crisis countries: Bias, inefficiency, and their origins

External sector surveillance and stabilization are core missions of the International Monetary Fund (IMF). Since 1992, the IMF approved over 600 crisis country loan programs, conditional on reforms and performance targets that are contingent on IMF crisis assessments and recovery forecasts. The literature evaluating IMF crisis forecasts has primarily focused on GDP, inflation, and fiscal budgets, but IMF programs often originate with the balance of payments crises. Our evaluation of IMF imports/exports/exchange rates in crisis countries reveals a surprising dichotomy: import forecasts are largely efficient and unbiased, while exports and exchange rate forecasts exhibit substantial biases and inefficiencies. We show forecast errors in the full sample are driven by deeply flawed IMF forecasts for LICs in crisis. Fixed exchange rate LICs (predominantly African franc zone countries) receive systematically inefficient import forecasts. Exchange rate forecasts for LICs with flexible exchange rates are so inefficient that they cannot outperform a naïve random walk, and over 30 percent of the forecasts cannot match the exchange rate’s directional movement during the first year of the recovery. Examining the sources of biases and inefficiencies, we highlight effects of conditionality and geopolitics that were not fully accounted for in IMF forecasts, specifically those relating to arrears (domestic and foreign), fiscal finance (balance and credit limits), policy reforms (trade and government), (civil) wars, and elections.

Perbandingan Metode Moving Average dan Exponential Smoothing pada Peramalan Nilai Tukar Rupiah terhadap Dollar AS

The currency exchange rate or known as the exchange rate is the price of one unit of foreign currency in the domestic currency or can be referred to as the currency exchange rate against current or future payments between the two currencies of each country or region. The exchange rate of a country's currency is strongly influenced by the flow of capital between countries. The Indonesian economy is heavily influenced by the international economy so that the Rupiah exchange rate is very much needed by the community in their economic life. Exchange rate data has very high volatility and tends not to be stationary. This study discusses the forecasting of the Rupiah exchange rate against the Dollar AS with two methods, namely Moving Averages and Exponential Smoothing. Accuracy analysis using Mean Absolute Deviation (MAD) and Mean Squared Error (MSE) methods. The software used in this research is the Quantitative Method (QM 5.3) software. The results of the study explain that the most appropriate forecasting method is used in analyzing the data. The Exponential Smoothing method forecasts the exchange rate of the Rupiah against the US Dollar with = 1.0 for January 1, 2022, which is Rp. 14,278 with MAD worth 29,105 and MSE worth 1564,619

Forecasting Exchange Rate of Pakistan Using Time Series Analysis

Exchange rates are crucial in regulating the foreign exchange market's dynamics. Because of the unpredictability and volatility of currency rates, the exchange rate prediction has become one of the most challenging applications of financial time series forecasting. This study aims to build and compare the accuracy of various methods. The time series model Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) are utilized to forecast the daily US dollar to Pakistan rupee currency exchange rates (USD/PKR). Lagged observations of the data series and moving average technical analysis are used in both models. Explanatory factors were used as indicators, and the prediction performance was assessed using a variety of commonly known statistical metrics. These statistical metrics suggested the presence of conditional heteroscedasticity. Thus, the process turns to capture the volatility effect of conditional heteroscedasticity through GARCH modeling. It may be inferred based on the results of tentative models; that the ARCH model outperforms the GARCH model in terms of predicting the USD/PKR exchange rate.

Implementasi Sliding Window Algotihm pada Prediksi Kurs berbasis Neural Network

<p><em>Time series is sequential data based on time sequence.</em><em> </em><em>Time series data can be used for prediction topics, one of the prediction topics that is always interesting to study is exchange rate prediction. In the case of exchange rate prediction, an appropriate data preprocessing stage is required. The success of this preprocessing stage will have a major effect on the resulting RMSE value. There is an important technique in determining the best RMSE value, especially in time series data, one of which is the windowing technique. The windowing technique is the stage of transforming time series data into cross sectional. Window size has an important role in time series data. However, there is no standard in window size. The Window size experiment starts with a small value and then increases to a larger value until it reaches a certain point with the best RMSE. In this research, an experiment will be conducted on windows size on exchange rate data based on a neural network. The purpose of this research is to optimize the RMSE of a data mining model based on windows parameters. The implementation of sliding windows is carried out in the scenarios of window sizes 4, 6, and 28. Based on the experiments conducted, the best RMSE is on windows size 6 = 0.014 +/- 0.000. With a combination of neural network parameters in the form of training cycles = 1000, learning rate = 0.1 and momentum = 0.1.</em></p>

Forecasting cryptocurrencies prices using data driven level set fuzzy models

The paper develops fuzzy models to forecast cryptocurrencies prices using a data-driven fuzzy modeling procedure based on level set. Data-driven level set is a novel fuzzy modeling method that differs from linguistic and functional fuzzy modeling in how the fuzzy rules are built and processed. The level set-based model outputs the weighted average of output functions of active fuzzy rules. Output functions map the activation levels of the fuzzy rules directly in model outputs. Computational experiments are done to evaluate the level set method in one-step-ahead forecasting of the closing prices of cryptocurrencies. Comparisons are made with the autoregressive integrated moving average, multi layer neural network, and the naïve random walk as a benchmark for Cardano, Binance Coin, Bitcoin, Ethereum, Chainlink, Litecoin, Tron, Stellar, Monero and Ripple. The results suggest that the random walk outperforms most methods addressed in this paper, confirming the Meese–Rogoff puzzle for the case of digital coins, i.e. the difficulty to surpass the naïve random walk in predicting exchange rates. However, when performance is measured by the direction of price change, the level set-based fuzzy modeling performs best amongst the remaining methods.

Out-of-sample forecasting of foreign exchange rates: The band spectral regression and LASSO

We propose to utilize the band spectral regression for out-of-sample forecasts of exchange rates. When one period ahead forecast is considered, there is some evidence that the band spectral regression improves its accuracy, especially when the Taylor rule fundamentals model is employed. However, when the forecasting horizon increases, the purchasing power parity (PPP) fundamentals model is found to be powerful, and we can improve the out-of-sample forecast by selecting appropriate frequency bands. Bayesian model averaging shows that placing a large weight on the business cycle frequency improves the accuracy of the out-of-sample forecasting of the PPP model (as well as the monetary fundamentals model) when a longer forecasting horizon is our focus. Without specifying the frequency bands prior to applying the regression, LASSO can provide better out-of-sample exchange rate forecasts for many cases – most patently for the PPP fundamentals model – and provide information about the dynamic relationship between forecasting variables and exchange rates. The frequency domain approach not only improves the accuracy of exchange rate forecast but provides insights for understanding why the PPP fundamentals act as a powerful predictor when the forecasting horizon increases and there is a possible improvement in the time domain regression forecast.

Hybrid BRNN-ARIMA Model for Financial Time Series Forecasting

The accurate forecasting of time series is difficult and for exchange rate more difficult as well. Because it is difficult to predict as they continuously fluctuate during trading hours. Exchange rate forecasting plays a vital financial problem in recent era. It is extensively acknowledged that exchange rate stability implies that macroeconomic stability. In this study, a hybrid model is proposed to forecast exchange rates. Bayesian regularized neural network (BRNN) model is assembled with Autoregressive integrated moving average model (ARIMA) and develop hybrid BRNN-ARIMA model. Furthermore, the comparison of proposed hybrid model has been done with standalone BRNN, standalone ARIMA and random walk model. Quarterly exchange rate data from 1970Q1 to 2021Q2 of six countries comprises developed (UK, Canada, and Singapore) and developing (Pakistan, India, and Malaysia) are forecast. To evaluate the performance of these models RMSE, MAE and MAPE are applied. The results indicate that the proposed hybrid BRNN-ARIMA model outperforms the other studied model in forecasting exchange rates.

Can Oil Price Predict Exchange Rate? Empirical Evidence from Deep Learning

This paper critically analyses the predictability of exchange rates using oil prices. Extant literature that investigates the significance of oil prices in forecasting exchange rates remains largely inconclusive due to limitations arising from methodological issues. As such, this study uses deep learning approaches such as Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) to predict exchange rates. In addition, the Empirical Mode Decomposition (EMD) of time series dataset was utilized to ascertain its effect on the quality of prediction. To examine the efficacy of using oil prices in forecasting exchange rates, bivariate models were also built. Of the three bivariate models developed, the EMD-CNN model has the best predictive performance. Results obtained show that oil price information has a strong influence on forecasting exchange rates.

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.

Application of Deep Learning Model in the Avoidance of Investment Risk of Multinational Enterprises.

With the continuous improvement and development of the socialist market economic system, China's economic development has full momentum, but the domestic market is no longer sufficient to meet the needs of enterprise development. China has always focused on peaceful diplomacy, and the world market has a strong demand for Chinese products. This work aims to improve the accuracy of exchange rate forecasting. The risk factors that may be encountered in the investment process of multinational enterprises can be effectively avoided. Combining the advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), the LSTM-CNN (Long Short-Term Memory-Convolutional Neural Network) model is proposed to predict the volatility trend of stocks. Firstly, the investment risk of multinational enterprises is analyzed, and, secondly, the principles of the used CNN and LSTM are expounded. Finally, the performance of the proposed model is verified by setting experiments. The experimental results demonstrate that when predicting the 10 selected stocks, the proposed LSTM-CNN model has the highest accuracy in predicting the volatility of stocks, with an average accuracy of 60.1%, while the average accuracy of the rest of the models is all below 60%. It can be found that the stock category does not have a great impact on the prediction accuracy of the model. The average prediction accuracy of the CNN model is 0.578, which is lower than that of the Convolutional Neural Network-Relevance model, and the prediction accuracy of the LSTM model is 0.592, which is better than that of the Long Short-Term Memory-Relevance model. The designed model can be used to predict the stock market to guide investors to make effective investments and reduce investment risks based on relevant cases. The research makes a certain contribution to improving the company's income and stabilizing the national economic development.

Exchange Rate and its Forecasting: Market-Based Forecasting and Forecasting with the Use of Currency Betas (βS)

This paper is using the market-based and the currency beta (β) theories of exchange rate forecasting. It is testing empirically these theories by using data, spot and forward rates, from seven different countries with respect the U.S., as our domestic country. The countries are: U.S. with Euro-zone, Mexico, Canada, U.K., Switzerland, Japan, and Australia. The results show that both methods, the market-based forecasting and the currency betas are giving very good forecasting for these seven exchange rates by minimizing the standard error of the regression (SER) and the root mean squared error (RMSE). Of course, uncertainty exits always in the forecasting of any economic variables, due to unanticipated public policies (monetary, fiscal, and trade) and other “innovations” in our financial markets and new philosophies in our way of living.

Extreme Learning With Metaheuristic Optimization for Exchange Rate Forecasting

Model with better learning ability and lower structural complexity is desirous for accurate exchange rate forecasting. Faster convergence to optimal solutions has always been a goal for the researcher in building forecasting models. And this is achieved by extreme learning machines (ELMs) due to their single hidden layer architecture and superior generalization ability. ELM is a simple training algorithm used to find the hidden-output layer weights by a random selection of input-hidden layer weights. Metaheuristics algorithms like Fireworks algorithm (FWA), Chemical reaction optimization (CRO), and Teaching learning-based optimization (TLBO) are employed to pre-train the ELM owing to their fewer optimizing parameters. This article aims to pre-train ELM using the said metaheuristics separately, ensuring the optimal solution of a single feedforward network (SLFN) with improved accuracy. The pre-trained ELMs provide accurate results. The same was verified using other primitive optimization algorithms

The Impact of Macroeconomic Sustainability on Exchange Rate: Hybrid Machine-Learning Approach

This paper constructed a robust methodology to investigate the impact of news regarding macroeconomic policies on exchange rate fluctuations, and to examined the applicability of qualitative information alongside historical data to predict exchange rates. To do so, hybrid machine learning algorithms comprised of natural language processing, fuzzy logic, and support vector regression have been constructed. This study emphasizes the significance of qualitative information on investors’ subjective consideration, the decision-making process, and causality on exchange rate volatility. To perceive the causality of expected and unexpected macroeconomic news on exchange rate fluctuations, news regarding the inflation rate, interest rate, unemployment rate, balance of trade, and credit ratings has been extracted from the web. Learning automata has been adopted to construct a unique lexicon for textual analysis. Subjective considerations of decision makers based on news have been evaluated by processing using the prospect theory and composing fuzzy antecedents for the fuzzy logic phase. The fuzzy logic method attained the correlation value between the macroeconomic news and the exchange rate. Finally, support vector regression predicted the exchange rate on a daily basis. The statistical test results indicated a strong correlation between recently published macroeconomic news on daily exchange rate fluctuations and their usability for predicting exchange rates in the short term, while emphasizing the significance of sustainable macroeconomic policies on exchange rate stability.

The influence of international oil prices on the exchange rates of oil exporting countries: Based on the hybrid copula function

Achieving accurate exchange rate forecasts has a significant impact in construction of international trade and currency markets. However, because of the volatility of exchange rate series, accurate exchange rate prediction is still a difficult issue. In prior studies, researchers tend to conduct prediction research on individual variables of the real exchange rate and ignore the direct influence of other relevant economic factors on the real exchange rate forecasts, which leads to unsatisfactory prediction accuracy. At present, oil price shocks are often used as the dominant factor to explain the actual exchange rate behavior, and the analysis of the relationship between the two has become a hot issue. To explore the direct impact of oil prices on the real effective exchange rate forecast, a bivariate scheme is proposed, proving the important effect of oil price variable on exchange rate forecasting. The framework of this article starts from two aspects. First, several Copula functions are used to study the relationship between the two sequences, and the basic Copula functions including Clayton, Gumbel, and Frank functions are selected, and the three Copula functions are employed to obtain a hybrid Copula function using the improved Dragonfly optimization strategy. Next, a binary forecasting framework is constructed and a data preprocessing method is added to construct a forecasting model. Finally, this article demonstrates that the bivariate scheme achieves better forecasting capabilities than the univariate forecasting frame.