Exchange Rate Data Research Articles

Exact likelihood for inverse gamma stochastic volatility models

We obtain a novel analytic expression of the likelihood for a stationary inverse gamma stochastic volatility (SV) model. This allows us to obtain the maximum likelihood estimator for this nonlinear non‐Gaussian state space model. Further, we obtain both the filtering and smoothing distributions for the inverse volatilities as mixtures of gammas, and therefore, we can provide the smoothed estimates of the volatility. We show that by integrating out the volatilities the model that we obtain has the resemblance of a GARCH in the sense that the formulas are similar, which simplifies computations significantly. The model allows for fat tails in the observed data. We provide empirical applications using exchange rates data for seven currencies and quarterly inflation data for four countries. We find that the empirical fit of our proposed model is overall better than alternative models for four countries currency data and for two countries inflation data.

Forecasting currency exchange rates using EMD-ARIMA Model

In today’s global economy, accuracy in forecasting currency exchange rates is of much importance to any future investment. Currency exchange rates portray non-linear and non-stationary characteristics hence to address these characteristics; this paper proposes a hybrid forecasting model using the Empirical Mode Decomposition (EMD) technique, and the ARIMA model. EMD is used to decompose the raw currency exchange rate data into several intrinsic mode functions and one residual. The process of extracting the IMFs from the data is called the sifting process. EMD was used to detect the moving trend of currency exchange rate data and improve the forecasting success of the ARIMA model. The data were obtained from the Central Bank of Kenya website between the periods January 2005 to May 2017. The best ARIMA model fitted to the raw data before decomposition based on information criterion statistics was found to be ARIMA (1,0,3) for the KShs/AE. Dirham, ARIMA (1,0,1) for KShs/Australian dollar and ARIMA (1,0,3) for KShs/Canadian dollar currency exchange rates. After forecasting, we then compared the forecasted values with the actual data to check the suitability of the ARIMA model. Further, EMD was applied to the exchange rate data and then fitted an ARIMA model to the IMFs. The best model was found to be ARIMA (1,0,1) for the KShs/AE. Dirham, ARIMA (0,0,1) for KShs/Australian dollar and ARIMA (1,0,1) for KShs/Canadian dollar currency exchange rates. The appropriateness of these models was tested using the Ljung-Box test. The forecasting performance of each model was evaluated using the RMSE.

Analyzing Rupiah-USD Exchange Rate Dynamics: A Study with ARCH and GARCH Models

The study aims to analyze the volatility of the Rupiah-USD exchange rate and predict future fluctuations using the Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The exchange rate data, spanning from January 2010 to December 2023, is sourced from Bank Indonesia (BI) and adheres to the Jakarta Interbank Spot Dollar Rate (JISDOR) regulations, focusing solely on business days. ARCH and GARCH models are widely applied in financial time series analysis because they capture and forecast time-varying volatility. This study analyzes historical exchange rate data to evaluate the persistence of volatility and detect any structural breaks that could impact future exchange rate behavior. The findings reveal that both models effectively capture the volatility of the Rupiah-USD exchange rate, but the GARCH (1,1) model demonstrates superior forecasting accuracy. This model's ability to account for long-term volatility clustering makes it particularly useful for predicting exchange rate dynamics. The research contributes to a deeper understanding of the factors driving exchange rate fluctuations, offering valuable insights for policymakers, investors, and businesses. These insights can help stakeholders manage exchange rate risks more effectively within Indonesia's open economy, where global financial conditions and external shocks significantly shape currency movements. The study emphasizes the importance of using advanced econometric models for accurate volatility predictions and informed decision-making.

Local linear estimators for reflected diffusions

In this paper, we investigate the nonparametric local linear estimator for the drift function of reflected stochastic differential equations based on the discretely observed process. Under certain regularity conditions, we prove the strong consistency and establish the asymptotic normality for our proposed estimator. We also propose the optimal bandwidth selector for the estimator and show that the estimator does not have a boundary effect. Simulation results demonstrate that our method is superior to the method proposed by Cholaquidis et al. [Stat. Sin. 2021, 31: 29–51]. We apply the EUR/USD currency exchange rate data to illustrate our theoretical results.

Enhancing energy security and efficiency through a regional integration policy: The case of the Southern Cone

This paper addresses the knowledge gap concerning foreign exchange risk in energy transactions within the Regional Initiative Energy Integration System of the Southern Cone Countries (SIESUR) of the Latin American region. Focusing on the cross-border electricity, it investigates the impact of exchange rate fluctuations on the transaction model in SIESUR: US dollar-denominated cross-border energy contracts with a time lag between invoicing and payment. The objective of this paper is to estimate foreign exchange risk exposure and propose mitigation solutions. Analyzing foreign exchange risk of energy trading among SIESUR member countries reveals substantial financial costs hindering electricity integration. Energy importing countries, facing greater exposure of their local currencies and weakened bargaining power, are particularly vulnerable. A Monte Carlo simulation model, using historical exchange rate data, highlights significant currency volatility, emphasizing the substantial foreign exchange risk exposure within SIESUR. Notably, cost overruns of energy imports due to foreign exchange risk exposure can reach 12.6% of transaction value. Through a global analysis, potential strategies for risk management are explored to foster electricity exchange expansion. Efforts to mitigate foreign exchange risk are crucial for advancing electricity integration within the SIESUR region. This study underscores policy implications for enhancing regional energy security and promoting sustainable development.

Analisis Kinerja Model ARIMA dan LSTM dalam Memprediksi Jakarta Interbank Spot Dollar Rate (JISDOR)

JISDOR exchange rate data is a collection of time series data with certain patterns, such as trend, seasonal, horizontal, and cyclical. According to Cowpertwait and Metcalfe (2009), one of the time series forecasting techniques is based on statistical mathematical models. As technology develops, forecasting methods are becoming more sophisticated and diverse. This research compares two main methods, namely AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). ARIMA, a classic method of time series analysis, is used to forecast trends and seasonal patterns. LSTM, a subset of artificial neural networks, promises a solution to long-term dependencies and complex patterns in data. In this study, the stationarity of the data was checked, which showed that the data was not stationary, so differencing was performed. Based on the ACF and PACF plots, the parameter values of p,d,q are (2, 1, 0). Both ARIMA and LSTM models were tested to get the best model based on RMSE and MAPE. The ARIMA model has RMSE 82.537 and MAPE 0.4193, while the LSTM has RMSE 75.807 and MAPE 0.4152. These results show that LSTM is better than ARIMA because the RMSE and MAPE values are lower. Therefore, JISDOR exchange rate forecasting is carried out using the LSTM model.

Modeling and Forecasting High Frequency Currency Exchange Rates. A Comparative Study of ARIMA, ANN, and Hybrid ARIMA-ANN Models

Aims/ Objectives: The study develops comparative results on the modeling and prediction performance of the autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and the hybrid ARIMA-ANN time series models for high frequency data. Methodology: The study made use of ARIMA, ANN, and hybrid ARIMA-ANN models to forecasts the East Africa Community countries' daily currency exchange rates data which were obtained from the Central Bank of Kenya website and covered the period from January 2017 to December 2023. Stationarity of the time series data was established using the ADF test. The Ljung Box test and ACF plots were used to establish and compare the goodness-of-fit of the resultant models while the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error(RMSE) values were used to compare the prediction performance. Results: The study established that the hybrid ARIMA-ANN methodolgy provided better-fitting models for the currency exchange rates data compared to ARIMA and ANN modeling strategies since all the Lyung Box test statistics had p values greater than 5%. Comparatively, the hybrid methodology registered lower MAPE and RMSE values hence had better prediction accuracy compared to ARIMA and ANN methods. Conclusion: The Hybid methodology improves the modeling and forecasting accuracy over the ARIMA and ANN models for high frequency time series data due to its ability to captures both the linear and nonlinear patterns in the time series data.

The Effect of Exchange Rate Volatility on Foreign Tourist Visits in Indonesia: GARCH Analysis

In this era of globalization, currency exchange rates are one of the crucial factors affecting a country's economy. The exchange rate of a country's currency plays an important role in economic stability and international trade, especially in the tourism sector. This study aims to conduct an in-depth analysis to identify patterns of rupiah exchange rate volatility using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and then examine whether these patterns have a significant correlation with the number of foreign tourist arrivals in Indonesia. In analyzing the data, researchers conducted several classical assumption tests, namely the normality test and the heteroscedasticity test. The test results show that the data used, namely the rupiah exchange rate data and the number of foreign tourist visits, are not normally distributed and heteroscedastic. In this case, it resulted in researchers not use the Pearson correlation test so they used the Kendall-Tau correlation test and Granger Causality test which resulted in the two variables being correlated. Furthermore, GARCH modeling is carried out which provides forecasting on both data to increase in 2024. The results of this study are expected to provide a basis for consideration for stakeholders to determine policies, especially in the financial and tourism sectors.

Modelling Petrol Prices in Kenya from 2014 to 2023 Using Sarimax Model: A Case Study of Nairobi County

The requirement for petrol price information is crucial for majority of enterprises. This is because fluctuations in petrol prices impact inflation hence affecting daily lives of citizens. In analyzing the prices of petrol, researchers have employed several models but encountered various limitations. These limitations include; the Error Correction Model can examine only one co-integrating association. The Vector Autoregression (VAR) model does not account for the structural changes in the data. Additionally, the AutoRegressive Integrated Moving Average (ARIMA) model does not take into consideration the seasonal component in the data. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model assumes that over time the volatility is constant. Moreover, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model does not integrate the external factors. Hence in this study Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model was employed since it captures seasonality in data and incorporates the exogenous variables. The research’s aim was to model prices of petrol in Kenya for the period between 2014 to 2023 with exchange rates as an external factor. Secondary data was obtained from Energy and Petroleum Regulatory Authority (EPRA), Kenya National Bureau of Statistics (KNBS) and Central Bank of Kenya (CBK) websites. R software was used to analyze the data. By the use of historical data of petrol prices and exchange rates, the study sought to fit the best Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, validate the model and predict the petrol prices. The petrol price data was found to be non-stationary using Augmented Dickey Fuller test (ADF). Regular differencing was conducted to make the data stationary. Seasonal differencing due to seasonality component available in the data was also performed. Best SARIMAX model was chosen from various SARIMAX models according to Box-Jenkins methodology which uses least Akaike Information Criterion (AIC) value. SARIMAX (0,1,1)(2,1,2)<sub>12</sub> model was selected since it had least Akaike Information Criterion (AIC) value of 656.3733 and the model validated using the hold out technique. The forecasts errors from the training set were; Mean Squared Error (MSE)=10.4970, Root Mean Square Error (RMSE)=3.239911, Mean Absolute Percentage Error (MAPE)=2.309268% while those from the testing set were; Mean Squared Error (MSE)=3271.1012, Root Mean Square Error (RMSE)=57.193542, Mean Absolute Percentage Error (MAPE)=26.695390%. There was less error in the training set than in the testing set as it was expected hence the model suited the data well and could be used for future predictions. The model was then used for five year forecast into the future. This study’s findings will offer sound suggestions to policymakers, businesses and consumers. This study recommends a model to be fitted using other factors affecting petrol prices and fitting Fourier terms, Behavioral Assessment Tools (BATS) and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) models.

A Bayes analysis of autoregressive model having functional-coefficients and its application on exchange rate data

A Bayes analysis of autoregressive model having functional-coefficients and its application on exchange rate data

Does the Exchange Rate Have a Pass-Through Effect on Domestic Food Prices in Türkiye? An Empirical Study Using a Combined ANN Algorithm and VAR Method

Exchange rate fluctuations have far-reaching consequences for national economies, companies and, above all, consumers. It is therefore important to understand the mechanisms by which these fluctuations affect food prices in domestic markets. In this context, we examine the complex relationship between exchange rates and domestic food prices and attempt to calculate the pass-through effect of the exchange rate on domestic prices. To examine the pass-through effect of the exchange rate on food prices, we use monthly USD/TRY exchange rate data from January 2003 to July 2023 as well as the global food price index, the export-import rate for food and the producer price index for food in Türkiye. We employ the vector autoregression (VAR) method to determine the optimal lags of the variables included in the analysis and the artificial neural network (ANN) algorithm to calculate the overall pass-through effect of the USD/TRY exchange rate on domestic food prices. The results show that the pass-through effect of the variable on food prices persists for three months: the current month, the previous month and the two previous months. The total pass-through effect of the exchange rate on food prices reaches up to 27%. Half of this impact comes from the previous month's food prices. In addition, the ratio of food exports to imports accounts for about 12% of the increase in domestic food prices. The results also show that the variable with the highest pass-through effect is the producer price index and that 90% of the increase in food prices is due to domestic dynamics, while the effect of price increases in foreign markets is only about 10%.

Forecasting of the Unemployment Rate in Turkey: Comparison of the Machine Learning Models

Unemployment is the most important problem that countries need to solve in their economic development plans. The uncontrolled growth and unpredictability of unemployment are some of the biggest obstacles to economic development. Considering the benefits of technology to human life, the use of artificial intelligence is extremely important for a stable economic policy. This study aims to use machine learning methods to forecast unemployment rates in Turkey on a monthly basis. For this purpose, two different models are created. In the first model, monthly unemployment data obtained from TURKSTAT for the period between 2005 and 2023 are trained with Artificial Neural Networks (ANN) and Support Vector Machine (SVM) algorithms. The second model, which includes additional economic parameters such as inflation, exchange rate, and labor force data, is modeled with the XGBoost algorithm in addition to ANN and SVM models. The forecasting performance of both models is evaluated using various performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings of the study show how successful artificial intelligence methods are in forecasting economic developments and that these methods can be used in macroeconomic studies. They also highlight the effects of economic parameters such as exchange rates, inflation, and labor force on unemployment and reveal the potential of these methods to support economic decisions. As a result, this study shows that modeling and forecasting different parameter values during periods of economic uncertainty are possible with artificial intelligence technology.

Comparison of Nonparametric Regression Nadara - Watson Estimator Kernel Function And Local Polynomial Regression In Predicting USD Against IDR

Introduction: Macroeconomic problems such as inflation and exchange rates are often highlighted as benchmarks for achieving economic progress. The stability of both must be monitored by the government in order to control the inflation rate and exchange rate. This instability is a phenomenon of fluctuation, namely the phenomenon of the rise and fall of the exchange rate of a currency based on demand and supply. Given the large impact of exchange rate fluctuations on the economy, the prediction of the wage exchange rate against the US dollar is considered necessary because it is useful to anticipate and minimize bad possibilities that arise. Method: Methods that can be used to analyze fluctuating currency exchange rate data are nonparametric regression, Nadaraya-Watson estimator, Gaussian kernel function, and Local Polynomial Regression. Results and Discussion: The results of a nonparametric regression comparison between the Nadaraya-Watson estimator, Gaussian kernel function, and local polynomial regression were obtained by MAPE of 2.508% and 0.179%, respectively. This shows that the best model uses the local polynomial regression method and predicted USD exchange rate data against IDR using the best model, namely Local polynomial Regression where the MAPE value is less than 10%, which means the prediction rate is very good. Conclusion: The nonparametric regression method of the Nadaraya-Watrson estimator, Gaussian kernel function, and local polynomial regression shows that the best model uses the local polynomial regression method.

THE IMPACT OF EXCHANGE RATE VOLATILITY AND INFLATION ON THE NIGERIAN ECONOMY

In African countries, exchange rate volatility, inflation, and economic growth relationships remain debatable, especially in Nigeria, because of the historical homogeneity of the monetary macroeconomic variables among the member countries. Therefore, the purpose of this paper is to examine the relationship among exchange rate volatility, inflation, and economic growth in Nigeria from 1985 to 2022. The autoregressive distributed lag (ARDL) estimation technique of analysis was employed in achieving the objective of the study. We employed GARCH (1, 1) to obtain the volatility of the exchange rate data. Results indicated that exchange rate volatility and inflation adversely influenced the growth of the Nigerian economy. Further analysis reveals a negative impact when the interaction of exchange rate volatility and inflation is tested on growth. Also, the result revealed that gross capital formation promotes the growth of the Nigerian economy. This result confirms the positive evidence ascertained in the literature on the investment growth nexus. It is therefore recommended that policies that focus on improving local currency should be the top priority of the government. Also, policies that would reduce overdependence on foreign raw materials for production should be encouraged.

PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLAR SINGAPURA, BAHT, DAN PESO MENGGUNAKAN METODE GSTAR

The Generalized Space-Time Autoregressive (GSTAR) model is an extension of the Space-Time Autoregressive (STAR) model. The difference between the two models lies in the parameter assumptions. In the STAR model, the parameters are assumed to be independent of location, so this model is only suitable for data with homogeneous locations. Meanwhile in the GSTAR model, the parameters are assumed to change for each different location. This research aims to develop the best model for forecasting the Rupiah exchange rate against the Singapore Dollar, Thai Baht, and Philippine Peso. The appropriate model used for the Rupiah exchange rate data is the GSTAR(51)I(1) model. The weights used in this study are uniform location weights and inverse distance. The modeling results show that the best model is the model with inverse distance weighting, which has an MSE value of 371.8907 with MAPE values for each of the Rupiah exchange rate data against the Singapore Dollar, Thai Baht, and Philippine Peso of 0.3154214%, 0.8369436%, and 0.6237245%, respectively.

Measuring persistent global economic factors with output, commodity price, and commodity currency data

AbstractIn this study, we use monthly G7 industrial production data, commodity price index data, and commodity currency exchange rate data in a dynamic factor model to examine the global economic factors useful for commodity price prediction. We differentiate between the dynamic factors by specifying a persistent factor and a non‐persistent factor, both as a single global factor using all data and as factors for each category of data. The in‐sample predictive performances of the three persistent factors together are better than the non‐persistent factors and the single global factors. Out‐of‐sample outcomes based on forecast combinations also support the presence of predictive information in the persistent factors for overall commodity prices and for most sub‐categories of commodity price indexes relative to their means. The gains in forecast accuracy are heterogeneous, ranging from 5% to 7% in the 1‐ to 6‐month horizon for overall commodity prices to a high of around 20% for fertilizers in the 12‐month horizon in the recent sample. We further show that the information in the persistent factors, especially in the commodity currency exchange rate‐based persistent factor, can be integrated with other global measures to further improve the predictive performances of the global measures.

A study of the dynamic relationship between the RMB exchange rate, foreign exchange reserves and monetary policy

In today's globalized economic environment, the relationship between China's RMB exchange rate, foreign exchange reserves and monetary policy has become more and more complex, and is crucial to the stability and sustainable development of the economy. The interactions among these three have a direct impact on China's macroeconomic situation, so it has become a very urgent task to study this relationship in depth. This study combines theoretical and empirical research to deeply analyze the dynamic relationship among these three, aiming to provide practical guidance for the formulation of China's monetary policy.This paper selects the time series data of RMB exchange rate, foreign exchange reserves and money supply from 2003 to 2023 to study the relationship between RMB exchange rate, foreign exchange reserves and monetary policy. This paper will deeply explore the impact of the government's market intervention on the RMB exchange rate under different exchange rate regimes, especially under the fixed exchange rate regime and the floating exchange rate regime. This paper will delve into the transmission mechanism of monetary policy, especially the flexibility and adaptability of monetary policy under the volatility of global financial markets and changes in economic cycles. In terms of empirical research, this paper will use the VAR model and the GARCH model for time series analysis to verify the applicability of the theoretical mechanism in practical situations and provide lessons for the formulation of future monetary policy.

Prediction of Changes in the Exchange Rate between the USD and the CNY and Analysis of Influencing Factors

As one of the important indicators of the global financial market, the fluctuation of the US dollar to Chinese yuan exchange rate not only directly affects the economic development of China and the United States, but also has important significance for analyzing the global economic situation. Analyzing the factors influencing the exchange rate between the United States dollar (USD) and the Chinese yuan (CNY) is one of the key research topics today. Researchers have found that exchange rates are influenced by factors such as national policies and the global political situation. However, there is still a certain gap in the comprehensive and objective analysis of these influencing factors. Therefore, the research topic of this article is to predict the trend of the exchange rate between the USD and the CNY and analyze the influencing factors. This study collected exchange rate data from January 2000 to November 2023 and used the ARIMA model to predict exchange rate changes. This study explores the trend of exchange rate changes by visualizing these data. Research has found that significant changes in exchange rates over the past two decades have also occurred during times of global instability and policy adjustments. The economic development level of China and the United States also directly affects the changes in exchange rates. In addition, the model used in this study also predicted the exchange rate between the USD and the CNY in 2024, providing a scientific direction for predicting the economic development trend in 2024.

Sentiment-Driven Exchange Rate Forecasting: Integrating Twitter Analysis with Economic Indicators

Abstract This study focuses on predicting the USD/TL exchange rate by integrating sentiment analysis from Twitter with traditional economic indicators. With the dynamic nature of global finance, accurate exchange rate forecasting is crucial for financial planning and risk management. While economic indicators have traditionally been used for this purpose, the increasing influence of public sentiment, particularly on digital platforms like Twitter, has prompted the exploration of sentiment analysis as a complementary tool. Our research aims to evaluate the effectiveness of combining sentiment analysis with economic indicators in predicting the USD/TL exchange rate. We employ machine learning techniques, including LSTM Neural Network, xgboost, and RNN, to analyze Twitter data containing keywords related to the Turkish economy alongside TL/USD exchange rate data. Our findings demonstrate that integrating sentiment analysis from Twitter enhances the predictive accuracy of exchange rate movements. This study contributes to the evolving landscape of financial forecasting by highlighting the significance of sentiment analysis in exchange rate prediction and providing insights into its potential applications in financial decision-making processes. JEL classification numbers: C53, F31, E60. Keywords: Twitter narratives, LSTM, XGBoost, RNN, USD/TL FX rate, Narrative economics.

Application of deep learning algorithms in predicting the exchange rate of Chinese yuan against the US dollar

This paper mainly studies the feasibility of using deep learning algorithms to predict the exchange rate between the Chinese yuan and the US dollar. Firstly, this study chooses the Long Short Term Memory Network (LSTM) as the main algorithm, which is a deep learning model suitable for processing time series data. Then, the study collects a large amount of data, including historical exchange rate data, macroeconomic indicators, and political events that may affect exchange rates. These data provide sufficient information for the model to learn and predict exchange rate fluctuations. The study found that the deep learning model can accurately predict the volatility of the RMB US dollar exchange rate by contrasting the calculated results of the pattern with effective exchange rate record, and its predicted results have high stability. Deep learning has great potential in the field of exchange rate forecasting, providing more accurate predictive information for financial transactions.