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

The educational backwardness of Nigeria has been attributed to exchange rate depreciation. The objective of this study is to ascertain the nature and extent to which exchange rate is related to educational development in Nigeria from 1986 to 2022 using autoregressive distributed lag (ARDL) bounds test approach. The theoretical framework of the study is the dependency theory. Literacy rate is a proxy of educational development and the data of literacy rate was obtained from macrotrends.net. The data of exchange rate and the control variables were obtained from World Development Indicators. The domestic credit to private sector by banks, exchange rate, GDP per capita growth and lending interest rate had no relationship with educational development in Nigeria in both the short run and long run. Exchange rate does not matter to educational development in Nigeria. Exchange rate policy cannot be used to improve educational development in Nigeria. The dependency theory cannot explain educational development in Nigeria.

This paper studied the impact of inflation rates and US Dollar exchange rates in Indonesian stock market return volatility in the period of 2002-2012. Daily data of stock market return, inflation rates and US Dollar exchange rates were used to estimate the daily volatility of the stock return. The data of stock market price, inflation rates and US Dollar exchange rates were taken from Yahoo Finance, Indonesia statistics bureau and central bank of Indonesia, respectively. The result showed that both inflation rates and US Dollar exchange rates did not have a significant impact on the Indonesian stock market return volatility. Inflation rates contributed 0.03% change in daily stock market return volatility whereas a 1% appreciation of Rupiah contributed to a 0.0000001% change in daily stock market return volatility. This research finding was different from the result in Nigeria where Yaya’s & Shittu’s (2010) research had the same variables. Inflation rates and US Dollar exchange rates were to examine the impact on Nigeria stock market index return volatility.

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.

Chapter 20b - Estimating the growth or depreciation on exchange rates using sentiment analysis method from social media comments

Aiming at the problem of low accuracy of exchange rate forecast in foreign exchange market data analysis, an exchange rate prediction model based on Lasso-IGWO-BP was proposed. Firstly, Lasso regression model is used as feature screening method of exchange rate data. Then, BP neural network is taken as the basic prediction method, and IGWO algorithm is introduced to optimise the parameters of BP neural network. Finally, the extracted features are input into the IGWO-BP model for classification prediction, thus achieving better exchange rate prediction effect. Experimental results show that compared with the Attention-LSTM, PSO-ELM and FWA-GA prediction models, the Lasso-IGWO-BP model constructed in this paper has significantly improved prediction accuracy. Therefore, exchange rate prediction model constructed can provide more reliable exchange rate data for foreign exchange market, which has certain feasibility.

The assessment of models of financial market behaviour requires evaluation tools. When complexity hinders a direct estimation approach, e.g., for agent based microsimulation models, simulation based estimators might provide an alternative. In order to apply such techniques, an objective function is required, which should be based on robust statistics of the time series under consideration. Based on the identification of robust statistics of foreign exchange rate time series in previous research, an objective function is derived. This function takes into account stylized facts about the unconditional distribution of exchange rate returns and properties of the conditional distribution, in particular, autoregressive conditional heteroscedasticity and long memory. A bootstrap procedure is used to obtain an estimate of the variance-covariance matrix of the different moments included in the objective function, which is used as a base for the weighting matrix. Finally, the properties of the objective function are analyzed for two different agent based models of the foreign exchange market, a simple GARCH-model and a stochastic volatility model using the DM/US-$ exchange rate as a benchmark. It is also discussed how the results might be used for inference purposes.

Impact of Per Capita Income on Exchange Rate with Regression Tool

This study aims to predict the exchange rat based on radial basis function neural network (RBFNN). Concerning the selected sample point, it only responds to the inputs of neighboring samples and hence has better approximation performance and overall optimization than other forwarding networks, in addition to being simple in structure and fast in training speed. RBFNN in empirical risk minimization methods has been studied, their adaptability to exchange rate prediction has been explored, and these methods have been verified by exchange rate data. The contributions are proposing an exchange rate prediction algorithm based on RBFNN and more accurately predicting short-term and long-term exchange rate variation trends.

Simple Tests for Models of Dependence Between Multiple Financial Time Series, with Applications to U.S. Equity Returns and Exchange Rates

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain in high-dimensional data handling and parameter optimization. This study mitigates these constraints by introducing an innovative enhanced prediction framework that integrates the optimal complete ensemble empirical mode decomposition with adaptive noise (OCEEMDAN) method and a strategically optimized combination weight prediction model. The grey wolf optimizer (GWO) is employed to autonomously modify the noise parameters of OCEEMDAN, while the zebra optimization algorithm (ZOA) dynamically fine-tunes the weights of predictive models—Bi-LSTM, GRU, and FNN. The proposed methodology exhibits enhanced prediction accuracy and robustness through simulation experiments on exchange rate data (EUR/USD, GBP/USD, and USD/JPY). This research improves the precision of exchange rate forecasts and introduces an innovative approach to enhancing model efficacy in volatile financial markets.

We study the Glosten–Milgrom model and estimate the proportion of informed traders or speculators using bid–ask spread and price range. The GM model is generalized in terms of a key parameter $$ \theta $$ —the probability of making a correct decision by an agent. Informed traders have $$ \theta = 1 $$ , and uninformed traders have $$ \theta = 1/2 $$ in the GM model. Speculators are defined to be agents with $$ 1/2 < \bar{\theta } < 1 $$ . We show that bid–ask spread can be generated when speculators and uninformed traders are in the market—the presence of informed traders is unnecessary. We estimate the proportion of informed traders or speculators using the spread-to-range ratio as a proxy, which entails a new estimation method. Using three exchange rate data, we obtain the conditional mean of the proportion of informed traders and speculators over a seven-year period. Speculators can achieve probability $$ \bar{\theta } > 1/2 $$ using simple trading rules within short trading horizons and net of transaction cost.

Associating the pattern in text data with the pattern with time series data is a novel task. In this paper, an approach that utilizes the features of the time series data and domain knowledge is proposed and used to identify the patterns for exchange rate modeling. A set of rules to identify the patterns are firstly specified using domain knowledge. The text data are then associated with the exchange rate data and pre-classified according to the trend of the time series. The rules are further refined by the characteristics of the pre-classified data. Classification solely based on time series data requires precise and timely data, which are difficult to obtain from financial market reports. On the other hand, domain knowledge is often very expensive to be acquired and often has a modest inter-rater reliability. The proposed method combines both methods, leading to a “grey box” approach that can handle the data with some time delay and overcome these drawbacks.

Each country has different holiday policies, but the number of holidays in Indonesia is quite large, which impacts uncertainty for investors when buying or selling shares. These events can cause market anomalies or irregular market conditions and produce abnormal returns at certain times, known as the holiday effect. This study uses a quantitative descriptive method with an event study approach, data collection is carried out using documentation and literature methods. The data used are secondary data in the form of the Jakarta Composite Index (JCI), the LQ45 Index, and the Jakarta Islamic Index (JII) from the official website of the Indonesia Stock Exchange (IDX). Exchange rate data is taken from the official website of Bank Indonesia. The population of this study is every company listed on the IDX, while the data used are JCI, LQ45, and JII data 6 days before and 6 days after the Eid al-Fitr holiday and regular trading days from 2011-2025. The results of the study show that there is no significant difference in the JCI, LQ45 Index, or JII before and after the Eid al-Fitr holiday, so there is no holiday effect. These results indicate that all three indices reflect a market that tends to be efficient and stable in responding to seasonal events. Furthermore, the Rupiah exchange rate had a negative but significant effect on the Jakarta Composite Index (JCI). The Rupiah exchange rate had a negative but insignificant effect on the JII before and after the Eid al-Fitr holiday. The Rupiah exchange rate had a positive but insignificant effect on the LQ45 Index before and after the Eid al-Fitr holiday.

&lt;em&gt;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.&lt;/em&gt;

This study analyses the impact of exchange rate and oil prices on the yield of sovereign bond and sukuk for Malaysian capital market. This study aims to ascertain the effect of weakening Malaysian Ringgit and declining of crude oil price on the fixed income investors in the emerging capital market. This study utilises daily time series data of Malaysian exchange rate, oil price and the yield of Malaysian sovereign bond and sukuk from year 2006 until 2015. The findings show that the weakening of exchange rate and oil prices contribute different impacts in the short and long run. In the short run, the exchange rate and oil prices does not have a direct relation with the yield of sovereign bond and sukuk. However, in the long run, the result reveals that there is a significant relationship between exchange rate and oil prices on the yield of sovereign bond and sukuk. It is evident that only a unidirectional causality relation is present between exchange rate and oil price towards selected yield of Malaysian sovereign bond and sukuk. This study provides numerical and empirical insights on issues relating to capital market that supports public authorities and private institutions on their decision and policymaking process.