Exchange Rate Data Research Articles

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.

World Oil Prices and Exchange Rates on Islamic Banking Risks

Islamic banking risks are influenced by many factors, facing internal and external banking risks. In this study, the internal banking risks are represented by Credit risk as measured by Non-Performing Financing (NPF) and liquidity risks as measured by Financing to Deposit Ratio (FDR). At the same time, world oil prices and exchange rates represent the external banking risks. World oil price data is obtained from OPEC Price, and exchange rate data is obtained from the Pacific Exchange Rate Service. While the data from NPF and FDR is obtained from BI Statistics of Sharia Banking. The results of this study found that world oil prices have a significant effect on credit risk and liquidity risk in Islamic banking. In contrast, the exchange rate only has a significant effect on credit risk in Islamic banking.

Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model

In light of the increasing level of correlation and dependence between the crude oil markets and the external influencing factors in the related financial markets, we propose a new multivariate empirical decomposition convolutional neural network model to incorporate the external influence of financial markets such as stock market and exchange market in a multiscale setting into the modeling of crude oil market risk movement. We propose a multivariate empirical model decomposition to analyze the finer details of interdependence among risk movement of different markets across different time horizons or scales. We also introduce the convolutional neural network to construct a new nonlinear ensemble algorithm to reduce the estimation bias and improve the forecasting accuracy. We used the major crude oil price data, stock market index, and the euro/United States dollar exchange rate data to evaluate the performance of the multivariate empirical model decomposition convolutional neural network model. The combination of both the multivariate empirical model decomposition and the convolutional neural network model in this paper has produced the risk forecasts with significantly improved risk forecasting accuracy.

ANALYSIS OF THE MOST COMMON CALCULATION METHODS “VALUE AT RISK, VAR”

The Regulation of the National Bank of Ukraine on Risk Management in Ukrainian Banks stipulates that this Regulation obliges banks to use the VaR methodology to assess market risk. The article shows that VaR is the absolute maximum amount of losses of the market investment portfolio due to fluctuations in prices for financial instruments during a certain fixed period of time (VaR horizon) in normal market conditions at a given level of confidence level (confidence level). A study of the Value at Risk methodology as the absolute maximum size of market investment portfolio losses due to price fluctuations in financial instruments during a certain fixed period of time (VaR horizon) in normal market conditions for a given level of confidence level (confidence level). It was found that there are three main methods of VaR: parametric VaR, or delta normal VaR, historical method, or historical simulation, Monte Carlo, or simulation and other methods and models. The parametric VaR method, or the delta-normal VaR method, is based on the assumption of a normal distribution of a random variable. This means that the data are distributed according to the normal distribution law, ie you can calculate the mean and standard deviation. It is proved that these two indicators are the basis of VaR by the parametric method. It is shown that nonparametric VaR methods do not require the hypothesis of normal distribution. Therefore, the form of distribution is determined by empirical data, and percentiles are stored as empirical percentiles of the historical distribution of profitability. The article considers the nonparametric VaR method in comparison with the parametric one on a practical example. It is proved that in the domestic scientific literature there is little research on the practical application of the VaR method in finance and in particular in banking. Therefore, the practical aspects of applying different VaR models to the NBU exchange rate data are demonstrated and conclusions are drawn. It is shown that the practical application of methods is a modern tool for assessing market risks, but with the expansion of the database, the parameters of the models should be refined in combination with the economic method. This requires monitoring and back-testing.

HOW DO TRADE, INFLATION, EXCHANGE RATES, AND INFORMATION TECHNOLOGY INFLUENCE INTERNATIONAL TOURIST VISITS IN INDONESIA?

This study aims to determine the short-term and long-term effects of trade, inflation, exchange rates, and information technology on international tourist visits in Indonesia. This study uses a quantitative approach within the Vector Error Correction Model (VECM) technique. The data used in this study are annual time series data of Indonesia trade, inflation, exchange rate and information technology on 1991-2019 period. The data used in this study were sourced from World Bank, International Monetary Fund (IMF), and Central Statistics Agency data. The results of the VECM research in the short term show that the variables of trade and information technology have a significant positive effect on international tourist visits. Meanwhile, inflation and exchange rates have a significant negative effect on international tourist visits. Then the results of VECM research in the long term show that variables of trade and information technology have a significant positive effect on international tourist visits. Meanwhile, inflation has a significant negative effect on international tourist visits. This study bring any literature contribution to future study that macroeconomics variables (trade, inflation) and information technology can influence the rise and fall of tourist visits to Indonesia. Practical contributions also brougt by this study as recommendations to increase international tourist visit in Indonesia for government and tourism stakeholder.

Impact of COVID-19 on Indian Currency

Abstract: In this article the author examines the impact of COVID -19 on Indian rupee. The data has been collected through secondary method such as RBI bulletin, newspapers and other research papers. The objectives of the study to investigate the impact of COVID -19 on Indian rupee, currency growth, FDI, export - import, economic stability in Indian economy and protection of Indian rupee against the U.S. dollar by making efficient economic policies. This paper cover the data of exchange rate from 2012 to 2022 to know the impact of corona. Further, this study observe the effect of Covid -19 on Indian currency by classifying the data into two categories like pre Covid -19 and post Covid -19. Pre Covid including the periods from 2012 -18 and post Covid including 2020 -22. The value of domestic currency declined in comparison to foreign currency, because the demand for foreign currency is more than domestic currency. Thus, the domestic rate of exchange increased and the value of domestic exports become cheaper than foreign imports. The study has used statistical measures such as: Arithmetic Mean (A.M.), Standard deviation {S.D.} and Coefficient of variance (C.V.).

Regime Shift Behavior and Interconnectedness of Stock Market Volatility and Exchange Rate Movements: Empirical Evidence from Emerging and Developed Economies

Purpose: The purpose of this study is to examine the regime shift behavior and interconnectedness between stock market volatility and exchange rate in a pre and post COVID context.
 Design/Methodology/Approach: For this purpose Pakistan India and China has been taken as emerging economies and USA UK and Japan as developed economies. Daily data of exchange rates and stock market indices have been used from Jan,1 2011 to Apr 30, 2021. Markov regime switching model, GARCH model and Granger causality test has been used.
 Findings: The outcomes of this study empirically confirmed that the Markov regime switching model is flexible model because it captures regime shifts in the mean and variance equation of all sample economies. Moreover, the sum of GARCH (1,1) indicates that volatility shocks are persistent except India. Study also concluded that exchange rate volatility has significant positive impact on stock market of Pakistan, USA and Japan in a pre COVID context.
 Implications/Originality/Value: A post COVID context has significant impact on the stock market of India, U.S.A. and U.K. Granger causality results indicates unidirectional relationship for Pakistan, China and USA. Whereas for Japan bi-directional relationship is found but for India and UK no directional causality exist.

TURİZM ODAKLI BÜYÜME HİPOTEZİNİN YAPISAL KIRILMALAR İLE SINANMASI: TÜRKİYE ÖRNEĞİ

The aim of this study is to examine the relationship between economic growth and tourism revenues in Turkey within the scope of the tourism-led growth hypothesis with the help of tests that take into account structural breaks. In this context, causality and cointegration tests based on the Fourier approach were used by considering the industrial production index, tourism revenues and real effective exchange rate data for the January 2005-December 2019 period. The results of the causality test revealed that, unlike the Granger causality test, which does not take into account the structural break, the results of the causality tests that take into account the structural break have a unidirectional causality relationship from tourism revenues to economic growth. Similarly, contrast to the Johansen test, which does not take into account the break in the cointegration analysis, the Fourier Johansen test detected the existence of a long-term relationship between economic growth, tourism revenues and the real exchange rate. In addition, the long-term cointegration estimation showed that tourism revenues have a positive and statistically significant effect on economic growth.

The Relationship between Exchange Rate and Stock Market Performance: Empirical Evidence from Sri Lanka

The stock market is one of the fastest-growing sectors in the world at present. Such a stock market can be seen growing in Sri Lanka as well. The performance of a stock market is affected by various factors. Among those factors, foreign market stock prices, GDP, corporate performance, and the exchange rate are important. Of these, the exchange rate is the crucial one. Data of exchange rate depict the increasing pattern over time while stock market performance shows high fluctuation in Sri Lanka. Thus, this research aims to identify the impact of the exchange rate on the performance of the Colombo Stock Exchange (CSE). For this purpose, we used price index of all stocks, exchange rate, inflation, foreign direct investment, and interest rate as the variables. We employed annual secondary data from Central Bank of Sri Lanka over the period of 1985 – 2018. The Augmented Dickey Fuller and Phillips Perron unit root tests approaches confirmed that none of the variables are I(2) which allows us to examine the long-run relationship between the variables using Auto-Regressive Distributed Lag (ARDL) bounds testing method. AIC is suggested to employ ARDL(1,1,0,4,4) model among the top 20 models. The bounds testing results detected the cointegrating relationship between the variables. Our results also suggest that there is no correlation between exchange rate and all share price indexes in the long run, whereas there is a positive relationship between exchange rate and all share price indexes in the short run. Inflation has a positive impact on all share price indexes in the long run while it does not have significant impact on all share price indexes in the short run. Moreover, the interest rate has a negative and weakly significant impact on all share price indexes both in the long run and in the short run. The Granger causality test indicates that there is a unidirectional causality between the price index of all stocks and the exchange rate. Therefore the results of this research emphasize that the exchange rate can be used as a policy tool to increase stock market performance.

Recurrent conditional heteroskedasticity

SummaryWe propose a new class of financial volatility models, called the REcurrent Conditional Heteroskedastic (RECH) models, to improve both in‐sample analysis and out‐of‐sample forecasting of the traditional conditional heteroskedastic models. In particular, we incorporate auxiliary deterministic processes, governed by recurrent neural networks, into the conditional variance of the traditional conditional heteroskedastic models, for example, GARCH‐type models, to flexibly capture the dynamics of the underlying volatility. RECH models can detect interesting effects in financial volatility overlooked by the existing conditional heteroskedastic models such as the GARCH, GJR, and EGARCH. The new models often have good out‐of‐sample forecasts while still explaining well the stylized facts of financial volatility by retaining the well‐established features of econometric GARCH‐type models. These properties are illustrated through simulation studies and applications to 31 stock indices and exchange rate data. An user‐friendly software package, together with the examples reported in the paper, is available at https://github.com/vbayeslab.

Cryptocurrency Shock and Exchange Rate Behaviour in Nigeria

This study examined the relationship between cryptocurrency shocks and exchange rate behaviour in Nigeria. Selected cryptocurrencies for the study are Bitcoin, Ethereum, Litecoin, Ripple and Binance coin which are the most traded cryptocurrencies in Nigeria. Augmented Dickey-Fuller (ADF), Johansen Cointegration and Vector Autoregressive (VAR) tests were used to analyze the monthly data of exchange rate and selected cryptocurrencies for four years (45 months). The result of the cointegration test revealed the existence of a long-run relationship among the variables. ECM result showed that about 6% of the short-run disequilibrium are being corrected and integrated into the long-run equilibrium relationship. In addition, the Variance Decomposition result showed that Ripple has the highest variations to exchange rate in the short and long runs. The present value of exchange rate adjusts slightly to changes in cryptocurrency. Ripple and Bitcoin have the highest shocks on the exchange rate. Therefore, monetary authorities should give adequate attention to cryptocurrency transactions and make policy decisions on how to reduce the prevailing high exchange rate in Nigeria by integrating crypto transactions in their systems. Transaction in cryptocurrency is still at the early stage, especially in Nigeria; only five years data can be gotten on commonly traded cryptocurrencies in Nigeria. This is a limitation to the study in terms of the number of cryptocurrencies used in the study. More cryptocurrencies can be included in future studies.

The Influence of International Coffee Prices and Rupiah Exchange Rate on Export Volume of Coffee in Central Java

<em>The aim of this study is to determine the influence of the International Coffee Price and Rupiah Exchange Rate toward Volume of Central Java Coffee Exports in period 2015-2019. The data used in this research is quantitative data. This study use secondary data which obtained by collecting data from some sources which relevant to this study such as Badan Pusat Statistik (BPS), Bank Indonesia, International Coffee Organization and others. Regarding the data period, the data in this study use monthly data of International Coffee Price and Rupiah Exchange Rate toward Volume of Central Java Coffee Exports in period 2015-2019 with total 60 data. The study explored with multiple linier regression statistical analysis, along with descriptive analysis and classical assumption test namely normality test, heteroscedasticity test, multicollinearity test, autocorrelation test, and linearity test. The results showed that partially international coffee prices had a negative and significant effect on the volume of coffee exports in Central Java and the rupiah exchange rate had a negative and insignificant effect on the volume of coffee exports in Central Java. Meanwhile, the international coffee price and the rupiah exchange rate simultaneously have a significant effect on the volume of coffee exports in Central Java.</em>

The Influence of Tea Total Production and Exchange Rate toward Export Volume of Indonesian Tea (Case on Export Volume of Indonesian Tea 2015-2019)

<em>Tea is one of Agriculture and Plantation business sector commodity that become one of Indonesia’s main export commodity and give contribution to Indonesia Gross Domestic Product until 12,72% at 2019. The aim of this study is to know the influence of the Amount of Tea Production and Exchange Rate toward Volume of Indonesian Tea Exports in period 2015-2019. The data used in this research is quantitative data. This study uses secondary data which obtained by collectiong data from some sources which relevant to this study such as Badan Pusat Statistik (BPS), Bank Indonesia, Direktorat Jenderal Perkebunan, Food and Agriculture Organization, and others. Regarding the data period, the data in this study use monthly data of Tea Production, Exchange Rate and Volume of Indonesian Tea Exports in period 2015-2019 with total 60 data in months. The study explored with multiple linier regression statistical analysis along with descriptive analysis and classical assumption test namely normality test, heteroscedasticity test, multicollinearity test, autocorrelation test, and linierity test. The F-test stated that total production and exchange rate have significant influence simultaneously. Based on the t-Test result, it is found that total production has insignificant influence toward volume export of Indonesian tea partially. While exchange rate has partial significant negative influence toward volume export of Indonesian tea.</em>

Is there a price bubble in the exchange rates of the developing countries? The case of BRICS and Turkey

PurposeThe last decades have experienced increasingly integrated global political and economic dynamics ranging especially from the influence of exchange rates and trade amid other sources of uncertainties. The purpose of this study is to examine the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) and the Republic of Turkey.Design/methodology/approachGiven this perceived global dynamics, the current study examined the BRICS countries and the Republic of Turkey's exchange rate dynamics by using the United States (US) monthly dollar exchange rate data between January 2002 and August 2019. The price bubble which is expressed as exceeding the real value of assets' prices which is observably caused by speculative movements is investigated by using the Supremum Augmented Dickey-Fuller (SADF) and the Generalized Supremum Augmented Dickey-Fuller (GSADF) approaches.FindingsAccordingly, the GSADF test results opined that there are price bubbles in the dollar exchange rate of other countries except for the United States Dollar (USD)/Indian Rupee (INR) exchange rate. As the related countries are classified as developing countries in terms of their structure, they are also expectedly the subject of speculative exchange rate movements. Speculative movements in exchange rates may cause serious problems in national economies.Originality/valueThus, the current study provides a policy framework to the BRICS countries and the Republic of Turkey.

BPNN's Empirical Analysis of Daily Rupiah Exchange Rate Volatility Utilizing Hidden Neuron Optimization

The exchange rate is the greatest financial market in its application. As a result, traders, investors, and other money market participants must be aware of the movement of currency exchange rate data. The fluctuation, or rise and fall, of currency exchange rates reveals the level of volatility in a country. The Backpropagation Neural Network is one of the models that can grasp the features of currency exchange rates (BPNN). BPNN is made up of three layers: input, hidden, and output, and each layer contains neurons. One of the challenges in designing a BPNN network architecture is determining the ideal number of hidden layer neurons. In this work, ten methodologies will be utilized to determine the number of hidden neurons; the ten approaches provide distinct empirical results in accordance with the goal of this study, which is to perform an empirical analysis of currency exchange rates by maximizing the number of hidden neurons. Empirical results reveal that the approach for calculating the number of hidden neurons performs well in terms of MAE and MSE. For the following seven periods, the best approach is used to forecast the Rupiah exchange rate.

Sieve bootstrap test for changes between unit root and long memory process with time trend

This paper studies the hypothesis testing problem where the null hypothesis is that a time series is an unit root process and the alternative hypothesis is that there is a change point so that the time series shifts from an unit root process to a long memory process or vice verse. We propose a residual based Dickey-Fuller type ratio statistic and derive its null distribution. We also establish the limiting distribution of the test statistic under the long memory process with no change point hypothesis. In particular, a sieve bootstrap method is designed to determine the critical value of the test statistic. Simulations show that the new test can control the empirical size well, even for the heavy-tailed long memory process. Our proposed test also gives competitive empirical power compared to the well known CUSUM of squares-based test. Finally, the validity of the new test is illustrated by a set of foreign exchange rate data between RMB and U.S. dollars.

Asymmetric Laplace Distribution Models for Financial Data: VaR and CVaR

In the field of financial risk measurement, Asymmetric Laplace (AL) laws are used. The assumption of normalcy is used in traditional approaches for calculating financial risk. Asymmetric Laplace distribution, on the other hand, reveals the properties of empirical financial data sets much better than the normal model by leptokurtosis and skewness. According to recent financial data research, the regularity assumption is frequently broken. As a result, Asymmetric Laplace laws offer a simple, creative, and useful option to normal distributions when it comes to modeling financial data. We here engage AL distribution to explore specific formulas for the two commonly used risk measures, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). The currency exchange rates data are used to and worked out to illustrate the proposed methodologies.

Purchasing Power Parity Between Vietnam and United States

This study uses the annual data of consumer price index, exchange rate and inflation rates spanned from 1986 to 2014 in order to observe whether the PPP hypothesis holds between Vietnam and United States. First, the findings come out based on the graph aproach which is used in order to examine both short-run and long-run PPP. Second, the Engle-Granger approach is applied in order to test for the long – run PPP again. In the short-run, the result from the graph approach indicates that PPP holds during hyperinflation years. After hyperinflation period, the exchange rate and relative price tend to be close together in several years. This finding in the short-run seems to reveal a good guide in the long-run PPP. Both graph and the Engle-Granger approach show the same result in the long –run. Upon the stationary testing, the finding of the Engle-Granger approach demonstrates that residuals from the estimation are stationary, therefore, there is an existence of a long-run relationship between nominal and real exchange rate. In other way, PPP seems to hold between Vietnam and United States. The result leads to the implication that multinational companies should set the same prices for products in Vietnam and United States market based on PPP. Otherwise, investors can gain profit through arbitrage strategies.

Forecasting Inflation, Exchange Rate, and GDP using ANN and ARIMA Models: Evidence from Pakistan

Purpose: The purpose of this study is to specify an efficient forecast model for the accurate prediction of macroeconomic variables in the context of Pakistan.
 Design/Methodology/Approach: We particularly investigate the comparative accuracy of Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models-based predictions using monthly data of inflation, exchange rate, and GDP from 1990 to 2014.
 Findings: According to our findings, the ANN-based forecasted inflation series is more precise as compared to ARIMA-based estimates. On the contrary, the ARIMA model outperforms the ANN model for exchange rate forecasts with the forecasted values being very close to the actual values. Further, ARIMA performs comparatively better in forecasting GDP with relatively smaller forecast error. On the whole, our findings suggest the ARIMA model provides appropriate results for forecasting exchange rates and GDP, while the ANN model offers precise estimates of inflation.
 Implications/Originality/Value: Our findings have important implications for the analysts and policymakers highlighting the need to use appropriate forecasting models that are well aligned with the structure of an economy.

A Bayesian paradigm in a large class of Lévy-driven CARMA models for high frequency data

Continuous-time time series are widely used for modeling the realizations of those phenomena where it is theoretically possible to have observation at any point of the sampling domain. However, technical restrictions cause to see the realizations of such processes as discrete-time sample paths. We project time-domain observations into frequency-domain periodograms and employ the Whittle’s likelihood approximation to make the inference about the parameters of CARMA processes. The inference is given under the Bayesian paradigm and some scenarios about the prior distributions are discussed. We show that our proposed estimator is robust for a large family of Lévy-driven distributed noises under some mild conditions. The Bayes estimates of parameters are obtained using a fast MCMC algorithm and they are examined numerically for the non-informative priors. The mean squared errors of the presented estimates are also compared to two other likelihood-based estimators, numerically. We also examine the performance of this Bayes estimate in prediction procedure of exchange rate data as a real dataset.