Exchange Rate Time Series Research Articles

The Impact of the Monetary Policy of the US Federal Reserve on the Ratio of the US Dollar To the Japanese Yen in the Foreign Exchange Market during 2015-2023

The information base of the study is the time series of the daily exchange rate of the currency pair USD/JPY, the US 10-year Treasury bond (TB 10), the Japanese government bond 10-year (JGB 10), the volatility index (VIX) of the Chicago Board Options Exchange (CBOE), which calculated by Bloomberg. Study period: December 1, 2015 (the date the Federal Reserve raised interest rates, which initiated a new regime – began easing its ZIRP zero interest rate policy) to December 29, 2023. The relationship between monetary policy and the currency pair USD/JPY is evaluated using 3 approaches: 1) a rally dynamic system based on the standard theory of nonlinear differential equations aimed at eliminating psychological noise (1/f noise, pink noise, trend noise with spectrum capacity); 2) regression of instrumental variables with heteroskedasticity aimed at managing unobservable sources of variability; 3) an event tracking method to measure anomalous asset price response, analyzing the daily price action of USD/JPY to detect market-related fluctuations in the exchange rate and separate the impact of general market trends from related differences in interest rates. The study found that the US Federal Reserve’s (Fed’s) monetary policy only occasionally affects the USD/JPY exchange rate, and it changes mainly when monetary policy deviates from expected future trajectories. Within the studied period, this happened only 3 times: growth on December 16, 2015; emergency rate reduction due to COVID-19 on March 15, 2020; and the first increase of 75 basis points on June 15, 2022 (the article estimates the impact of each of these episodes on the exchange rate). Statements by the Fed on monetary policy affect the price dynamics of the USD/JPY pair in the short term (since short-term psychological expectations play a decisive role in shaping the dynamics of the currency market), but not often (if the statement on monetary policy is consistent with market forecasts, it is practically will not affect exchange rates). The Fed’s adoption of tools such as “forward guidance” and official statements to inform the future course of monetary policy indirectly affects the price dynamics of USD/JPY. The price action of USD/JPY was driven by the fundamentals of the difference in interest rates between the US and Japanese bonds, which are influenced by the monetary policy of the US Federal Reserve and the Bank of Japan (BoJ). In 2023, it could be hard to attempt to challenge FX intervention except to curb “excess volatility and disorderly movements in exchange rates” like the Minister of Finance’s (MoF) 2022 intervention.

The Impact of the Measure Used to Calculate the Distance between Exchange Rate Time Series on the Topological Structure of the Currency Network.

Structural properties of the currency market were examined with the use of topological networks. Relationships between currencies were analyzed by constructing minimal spanning trees (MSTs). The dissimilarities between time series of currency returns were measured in various ways: by applying Euclidean distance, Pearson's linear correlation coefficient, Spearman's rank correlation coefficient, Kendall's coefficient, partial correlation, dynamic time warping measure, and Kullback-Leibler relative entropy. For the constructed MSTs, their topological characteristics were analyzed and conclusions were drawn regarding the influence of the dissimilarity measure used. It turned out that the strength of most types of correlations was highly dependent on the choice of the numeraire currency, while partial correlations were invariant in this respect. It can be stated that a network built on the basis of partial correlations provides a more adequate illustration of pairwise relationships in the foreign exchange market. The data for quotations of 37 of the most important world currencies and four precious metals in the period from 1 January 2019 to 31 December 2022 were used. The outbreak of the COVID-19 pandemic in 2020 and Russia's invasion of Ukraine in 2022 triggered changes in the topology of the currency network. As a result of these crises, the average distances between tree nodes decreased and the centralization of graphs increased. Our results confirm that currencies are often pegged to other currencies due to countries' geographic locations and economic ties. The detected structures can be useful in descriptions of the currency market, can help in constructing a stable portfolio of the foreign exchange rates, and can be a valuable tool in searching for economic factors influencing specific groups of countries.

Exchange rate prediction research based on LSTM-ELM hybrid model

The fluctuation of exchange rates holds paramount importance for a country's economic and trade activities. Due to the non-stationary and nonlinear structural characteristics of exchange rate time series, accurately predicting exchange rate movements is a challenging task. Single machine learning models often exhibit lower precision in exchange rate prediction compared to combined machine learning models. Hence, employing a combined model approach aims to enhance the predictive performance of exchange rate models. Both Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) exhibit intricate structures, making their direct integration challenging. To address this issue, an innovative weighted approach is adopted in this study, combining LSTM and ELM models and further refining the combination weights using an improved Marine Predators Algorithm. This paper encompasses both univariate and multivariate prediction scenarios, employing two distinct allocation strategies for training and testing datasets. This is done to investigate the influence of different dataset allocations on exchange rate prediction. Finally, the proposed LSTM-ELM weighted combination exchange rate prediction model is compared with SVM, Random Forest, ELM, LSTM, and LSTM-ELM average combination models. Experimental results demonstrate that the LSTM-ELM weighted combination exchange rate prediction model outperforms the others in both univariate and multivariate prediction settings, yielding higher predictive accuracy and superior fitting performance. Consequently, the LSTM-ELM weighted combination prediction model proves to be effective in exchange rate forecasting.

Monetary Datum Fractal Analysis Using the Time Series Theory

The paper will allow the interpretation of exchange rate fluctuations for several international currencies, the EUR—European currency and the CHF—Swiss Franc, respectively. The fractal dimension versus box-counting dimension, together with the fractal dimension versus log scale for CHF and EUR, respectively, are thoroughly presented. The exchange rate time series for CHF and EUR during March–June 2022 were also analyzed. The Hurst exponent H was numerically evaluated. By the application of directional symmetry (DS) statistics, the model efficiency in predicting the direction modification of time series has been verified. Ultimately, the prognosis of EUR-RON and CHF-RON exchange rates of the time series fluctuations for the last months (August–December) of the year 2022 was performed.

THE IMPLICATIONS OF BREXIT REFERENDUM ON THE LONG-RUN RELATIONSHIP BETWEEN THE EURO AND POUND STERLING: RESIDUAL AUGMENTED LEAST SQUARE BASED EVIDENCE

Purpose- The aim of this study is to investigate the economic effects of Brexit from the viewpoint of exchange rates. This research studies the impact of Brexit on long-run relationship between euro and pound sterling. It is crucial to assess how Britain’s vote to leave the European Union affects long-term relationship between euro and pound sterling because such action may have significant financial and economic implications on both economies (Diez et al., 2019; Korus & Celebi, 2019; Nagarakatte & Natchimuthu, 2022). Methodology- This study utilizes daily trade-weighted real effective exchange rate indices for euro (EUR) and pound sterling (GBP) over the period of January 1, 2013 to December 31, 2019 to investigate the impact of Brexit referendum took place on June 23, 2016 on the long-run relationship between euro and pound sterling. We exclude European sovereign debt crisis and pandemic periods. As stated previously, this study considers June 23, 2016 as the reference to assess the impact of Brexit on the relationship between currencies. Therefore, our analysis involves three periods including full sample period, pre- and post-Brexit. Empirical investigation relies on residual augmented least squares (RALS) based approach. Due to Im & Schmidt (2008), Im, Lee, & Tieslau (2014), and Lee, Lee, & Im (2015), we use RALS-based unit root test and cointegration analysis. While traditional tests (augmented Dickey-Fuller unit root and Engle-Granger cointegration) assume that error terms are normally distributed, which is a very strict assumption, RALS-based tests relax that condition. We also conduct Granger (non-) causality test proposed by Toda & Yamamoto (1995) to investigate the direction(s) of possible causalities between euro and pound sterling for each period. Findings- RALS based unit root test results suggest that the time series of exchange rates contain unit root for full sample, pre- and post-Brexit periods. The only exception is the time series of sterling pound is I (0) in post-Brexit period. According to the unit root test results, we can conclude that foreign exchange markets in both economies follow weak form efficiency for all sample periods. However, this is not valid for the sub sample where the time series of pound sterling is stationary at level, in post-Brexit. RALS based cointegration test results show that there is no cointegration between the time series of EUR and GBP. This general result is confirmed by both models, RALS ADL and RALS EG2. However, we observe a cointegrating relationship between two currencies in post-Brexit period when we treat GBP as the dependent variable. According to the Granger non-causality test results, we fail to reject null hypothesis and conclude that there is no causality running from EA to UK and vice versa for all sample periods. Test results reveal one exception. In the post-Brexit period, EUR Granger causes GBP implying that the past values of EUR are useful to forecast GBP. Conclusion- Interdependence of world economies is increasing as the financial integration is deepening. Key political, economic, and financial developments in the global world should be followed and assessed. Such developments at home or abroad may influence the prospects. Therefore, it is crucial to assess the outcomes of Brexit-like events that clearly have impact on economic and financial environment to take necessary actions. Keywords: Exchange rates, Brexit referendum, Euro, Pound Sterling, RALS Cointegration Test JEL Codes: C32, F31, G15

TÜRKİYE'DE DOLAR/TL KURUNU TAHMİN ETMEK: UZUN-KISA BELLEK SİNİR AĞLARI YAKLAŞIMI

The prediction of the exchange rate time series has been quite challenging but is an essential process. This is as a result of the inherent noise and the volatile behavior in these series. Time series analysis models such as ARIMA have been used for this purpose. However, these models are limited due to the fact that they are not able to explain the non-linearity as well as the stochastic properties of foreign exchange rates. In order to perform a more accurate exchange rate prediction, deep-learning methods have been employed withremarkable rates of success. In this paper, we apply the Long-Short Term Memory Neural Network to predict the USD/TL exchange rate in Turkey. The result from this paper indicates that the Long-Short Term Memory Neural Network deep learning method gives higher prediction accuracy compared to the Auto Regressive Integrated Moving Average and the Multilayer Perception Neural Network models.

Robustness and sensitivity of some wavelet multifractal models in fractal data modelling

AbstractMany multifractal models such as self‐similar and scaling law types have been proved to be efficient modellers and estimators in many fields such as financial time series where the data hide fractal and multifractal structures, allowing its processing without sophisticated models to be difficult. However, in statistical analysis, a necessary part that should take place for any model and estimator consists in tests of performance such as confidence intervals and generally statistical tests to confirm the adequacy of the model. The present paper provides the consideration of multifractal models based on wavelets and self‐similar type processes to study statistical tests. To test the efficiency, accuracy and robustness of the models, different inferential statistics are introduced, provided with some empirical examples due to the EURO/USD exchange rate time series with a sample covering the period 03/01/2000 to 30/08/2022. Contrarily to existing works, we showed in the present work that quasi‐self‐similar type models are better for many reasons. They indeed guarantee the well fitting of the data dynamics, the nonlinearity in both the model and the multifractal spectrum, the renormalization parameters which may differ from one scale to another and the preservation of the quasi multiplicative structure.

RETRACTED ARTICLE: How Have the COVID-19 Pandemic and Market Sentiment Affected the FX Market? Evidence from Statistical Models and Deep Learning Algorithms

This paper attempts to investigate the impact of the COVID-19 pandemic and market sentiment on the dynamics of USD/JPY, GBP/USD, and USD/CNY. We compose the market sentiment variable and incorporate the newly confirmed COVID-19 cases and sentiment variable into the traditional exchange rate forecasting model. We find that confirmed COVID-19 cases and sentiment variables in the US, Japan, UK, and China in the period of January 23rd, 2020 to September 14th, 2021 are significant in explaining the bilateral exchange rate movement. Recurrent neural network (RNN) and long short-term memory (LSTM) models outperform the other deep learning models and vector autoregressive (VAR) model in forecasting the bilateral exchange rate movement during the COVID-19 pandemic period. Further analysis using high-frequency intraday data and ensemble models shows that ensemble models significantly improve the accuracy of exchange rate prediction, as they are better at coping with the nonlinear and nonstationary features of exchange rate time series.

A bi‐level ensemble learning approach to complex time series forecasting: Taking exchange rates as an example

AbstractForecasting complex time series faces a huge challenge due to its high volatility. To improve the accuracy and robustness of prediction, this paper proposes a bi‐level ensemble learning approach by combining decomposition‐ensemble forecasting and resample strategies. The bi‐level ensemble approach consists of four steps: data decomposition via singular spectrum analysis (SSA), resampling by employing a bagging algorithm, individual forecasting utilizing extreme learning machine (ELM), and introducing sorting‐pruning and simple addition ensemble strategies for integrating the inner and outer results, respectively. To verify the effectiveness of the established forecasting approach, three exchange rate time series are selected as samples. The results reveal that the proposed model is significantly better than the other benchmarks at different lead times, which indicates that it can be regarded as an effective and promising tool for complex time series forecasting.

Regional Economic and Financial Interconnectedness and the Impact of Sanctions: The Case of the Commonwealth of Independent States

The war in Ukraine and the direct and indirect political, economic and financial involvement of many countries worldwide in this conflict demonstrates the difficult process of developing the new world order. Over 10,000 sanctions have already been imposed on Russia by the United States, the European Union and their allies. Many countries are significantly affected by sanctions regardless of whether they are imposing them, being targeted by them, or have economic and trade partnerships with either—or both—of the sides. Commonwealth of Independent States (CIS) countries have been significantly affected by sanctions related to the Russian–Ukrainian war. Seasonally adjusted real quarterly time series, including gross domestic product and external trade, monthly nominal exchange rate time series, exogenous dummy variables for sanctions, and a combination of the vector autoregressive model and the Granger causality test were used in the estimations. We demonstrate how sanctions have affected the Russian economy and foreign exchange market and how their impact may spill over to the economies and foreign exchange markets of other CIS countries. Based on the research findings and contemporary political and economic conditions in the region and the world, we make suggestions helpful for improving the international economic and trade policies of the CIS countries.

Relationship between the COVID-19 pandemic and currency exchange rates studied by means of the Dynamic Time Warping method

The COVID-19 pandemic affected the entire global economic system, including currency exchange rates. The main objective of this study is to assess the similarity between time series of currency exchange rates before and during the COVID-19 crisis. In addition, the study aims to examine the relationship between the exchange rates of currencies and the COVID-19 time series in particular countries. The Dynamic Time Warping (DTW) method was applied to check if changes in the exchange rates were related to the spread of COVID-19, and if they were, to what extent it was so. The use of the DTW allows the calculation of the distance between analysed time series. In this study, it made it possible to group the analysed currencies according to their change relative to the pandemic dynamics. The study is based on data from the Stooq and Our World in Data websites. Data on the 17 studied currencies denominated in the New Zealand dollar came from the period between 1 January 2019 and 10 November 2021, and the COVID-19 data from the period between 1 March 2020 and 10 November 2021. The results demonstrate that exchange rates evolved differently in all the three analysed periods: the pre-pandemic period and the first and the second phase of the pandemic. The outbreak of the pandemic led to the concentration of most currencies around the US dollar. However, when the economies unfroze, a polarisation of the currency market occurred, with the world’s major currencies clustering either around the US dollar or the euro.

US Dollar/IQ Dinar Currency Exchange Rates Time Series Forecasting Using ARIMA Model

The use of currency exchange estimation as a tool for economic planning is being researched as a technique for gaining economic stability. The main purpose of this study is to use the ARIMA model to forecast monthly US dollar and IQ dinar exchange rates. The information was gathered from January 2010 to December 2020. We got the information from the website (sa.investing.com). The minimum value of Root Mean Square Error (RMSE) and the mean absolute error are used to select the optimal model (MAE). ARIMA was found to be the best model for the US Dollar/IQ Dinar series (2, 1,0). This is the forecasted meaning for the future of this exchange rate time series, which indicates a perpetual increase continuously in the next two years. Statgraphics version 15 was the statistical software package utilized to complete this project.

Empirical evidence of the monetary approach to the exchange rate determinants under a fully flexible regime: the case of Mexico

The purpose of this paper is to examine the empirical evidence on the evolution of the nominal peso-dollar exchange rate based on the monetary approach under a fully flexible exchange rate regime. We use a standardised framework where the uncovered interest rate (UIP) and the purchase power parities (PPP), flexible prices, and a typical demand for real money balances determine prices in the long run. Once we identify that time series of the nominal exchange rate and the fundamental macroeconomic variables are non-stationary, we estimate a vector error-correction model (VECM) and, for comparative purposes, an ARIMA-EGARCH model and an ARIMA-EGARCH model with monetary approach. Models' assessment based on the post estimation results shows that the model with the lowest HRMSE for all the steps-ahead forecast is the ARIMA-EGARCH model followed by the VECM model. Likewise, the lowest HMAE for the first three-steps ahead forecast is the VECM model, followed by the ARIMA-EGARCH model.

The Causal Impact of the Rapid Czech Interest Rate Hike on the Czech Exchange Rate Assessed by the Bayesian Structural Time Series Model

I have employed the Bayesian Structural Time Series model to assess the recent interest rate hike by the Czech Central Bank and its causal impact on the Koruna exchange rate. By forecasting exchange rate time series in the absence of the intervention we can subtract the observed values from the prediction and estimate the causal effect. The results show that the impact was little and time limited in one model specification and none in the second version. It implies that the Czech Central Bank possesses the ability to diverge significantly from the Eurozone benchmark interest rate at least in the short term. It also shows that the interest rate hike will not be able to curb global inflation forces on the domestic price level.

Empirical Testing of Purchasing Power Parity Validity in Selected European Union Countries

Abstract In this article, the authors carried out an empirical analysis of the validity of purchasing power parity (PPP) in Slovenia, Croatia, the Czech Republic, Slovakia and Austria. The results provide mixed support for PPP, which is typical for extransition economies. In the first phase of the empirical part of the research, the authors tested the stationarity of the real exchange rate in a logarithm, while in the second phase, the cointegration of nominal exchange rate, domestic and foreign price levels was tested.The Vector Error Correction Model (VECM) was used in the third phase to test if the signs of variables are in accordance with economics and econometrics theories, while in the final phase, restrictions were imposed for the symmetry and proportionality of coefficients. Slovenia is subject to limitations on the symmetry and proportionality of coefficients, which means the validity of both the absolute and relative versions of the PPP theory. Croatia is subject to a limitation on symmetry, but not to a limit on the proportionality of coefficients, which means the validity of the relative version of the PPP theory. In the case of the Czech Republic, Slovakia and Austria, restrictions on the symmetry and proportionality of the coefficients do not apply, which consequently constitutes an invalidity of both versions. However, to the authors’ knowledge, and taking into account Liu (1992), who states that it is more important to check the presence of co-integration than to check the symmetry and proportionality of the coefficients, since there is a cointegratation between the nominal exchange rate, foreign prices and domestic prices, the theory of PPP is valid for all the selected countries. The empirical results suggest that all the real exchange rate time series are stationary, additionally, cointegration exists among all the variables for all countries, and the signs of coefficients are statistically significant for all variables in all countries, however, the coefficient restrictions are only statistically significant in Slovenia and Croatia.

Impact of Exchange Rate Devaluation in Ethiopia and Forecasting Foreign Exchange Rate Using ARIMA Model

The exchange rate is an important indicator of economic activity that policymakers use to formulate policies. This paper is based on fitting historical nominal exchange rate time series expressed in terms of past values of itself plus current and lagged values of error term using the Autoregressive Integrated Moving Average (ARIMA) model over the period 1971-2020, resulting in the model (2,1,1) to forecast the next 15 years. The projection's performance is assessed using data from the preceding fifty years sourced from World Bank, and the forecast indicates that domestic currency depreciation will become more prevalent in order to stimulate exports. Moreover, the ARDL model is used to examine the impact of exchange rate, import, export, and inflation rate on RGDP from 1977 to 2018. The unit root test (Augmented Dickey- Fuller test) was used to verify the integration order of the variables. The five variables are used in the cointegration analysis. The bounds test of cointegration was used in this work. The results showed that the F-statistics value is greater than the upper boundaries at all levels of significance, indicating that individual variables have a long-term relationship with RGDP. This led to the estimation of the Error Correction Model (ECM), in which 25.1 percent of the disequilibrium is adjusted annually to bring the system back into equilibrium. Furthermore, nominal exchange rate and import has positive contribution to RGDP while inflation has adverse effect on RGDP in the long-run. Besides, inflation has a negative impact to RGDP in the short run due to its lag period effects. Keywords : forecasting, ARIMA model, ARDL model DOI: 10.7176/EJBM/13-17-02 Publication date: September 30 th 2021

The Nexus Between Monetary Policy and Economic Growth in Pakistan: Empirical Evidence Based on Bound Test Approach

The objective of this research is to examine the monetary policy's impact on economic growth. Variables of study are Gross domestic product, Inflation, rate of interest, Exchange rate, Money supply, Investment, and Consumer Price Index and time series data is collected from. Gross domestic product is a dependent variable and all other variables are independent and have a great effect on the explanatory variable. In this study, the Augmented dicky fuller test is used to check out the stationarity of our selected variables and after that autoregressive distributed lag model co-integration technique is applied to estimate the parameters of the model. The result shows that inflation, interest rate, and consumer price index show a negative impact on gross domestic product. While other variables such as exchange rate, money supply, and investment show a positive impact on GDP. The study recommended that the desired level of output and employment can be attained by adopting sufficient strategies that reduce inflation in the economy.

The Leverage Effect and Other Stylized Facts Displayed by Bitcoin Returns

In this paper, we explore some stylized facts of the Bitcoin market using the BTC-USD exchange rate time series of historical intraday data from 2013 to 2020. Despite Bitcoin presenting some very peculiar idiosyncrasies, like the absence of macroeconomic fundamentals or connections with underlying assets or benchmarks, an asymmetry between demand and supply and the presence of inefficiency in the form of strong arbitrage opportunity, all these elements seem to be marginal in the definition of the structural statistical properties of this virtual financial asset, which result to be analogous to general individual stocks or indices. In contrast, we find some clear differences, compared to fiat money exchange rates time series, in the values of the linear autocorrelation and, more surprisingly, in the presence of the leverage effect. We also explore the dynamics of correlations, monitoring the shifts in the evolution of the Bitcoin market. This analysis is able to distinguish between two different regimes: a stochastic process with weaker memory signatures and closer to Gaussianity between the Mt. Gox incident and the late 2015, and a dynamics with relevant correlations and strong deviations from Gaussianity before and after this interval.

Studying Changes on Stock Market Transactions Using Different Techniques for Multivariate Time Series

There are many studies dealt with univariate time series data, but the analysis of multivariate time series are rarely discussed. This article discusses the theoretical and numerical aspects of different techniques that analyze the multivariate time series data. These techniques are ANN, ARIMA, GLM and VARS models. All techniques are used to analyze the data that obtained from Egypt Stock Exchange Market. R program with many packages are used. These packages are the "neuralnet, nnet, forecast, MTS and vars". The process of measuring the accuracy of forecasting are investigated using the measures ME, ACF, MAE, MPE, RMSE, MASE, and MAPE. This is done for seasonal and non-seasonal time series data. Best ARIMA model with minimum error is constructed and tested. The lags order of the model are identified. Granger test for causality indicated that Exchange rate is useful for forecasting another time series. Also, the Instant test indicated that there is instantaneous causality between Exchange rate and other time series. For non-seasonal data, the NNAR() model is equivalent to ARIMA() model. Also, for seasonal data, the NNAR(p,P,0)[m] model is equivalent to an ARIMA(p,0,0)(P,0,0)[m] model. For these data, we concluded that the ANN and GLMs of fitting multivariate seasonal time series is better than multivariate non-seasonal time series. The transactions of Finance, Household and Chemicals sectors are significant for Exchange rate in non-seasonal time series case. The forecasts that based on stationary time series data are more smooth and accurate. VARS model is more accurate rather than VAR model for ARIMA (0,0,1). Forecasts of VAR values are predicted over short horizon, because the prediction over long horizon becomes unreliable or uniform.

Forecasting currency exchange rate time series with fireworks-algorithm-based higher order neural network with special attention to training data enrichment

Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a method of enriching training dataset through exploring and incorporating of virtual data points (VDPs) by an evolutionary method called as fireworks algorithm trained functional link artificial neural network (FWA-FLN). The model maintains the correlation between the current and past data, especially at the oscillation point on the time series. The exploring of a VDP and forecast of the succeeding term go consecutively by the FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other models trained similarly and produces far better prediction accuracy.