Is pass‑through of the exchange rate to restaurant and hotel prices asymmetric in the US?Role of monetary policy uncertainty

This study examines the exchange rate pass-through to the United States(US)restaurant and hotel prices by incorporating the effect of monetary policy uncertainty over the period 2001:M12 to 2019:M01.Using the nonlinear autoregressive distributed lag(NARDL)model,empirical evidence indicates asymmetric pass-through of exchange rate and monetary policy uncertainty.Moreover,a stronger pass-through effect is observed during depreciation and a negative shock in monetary policy uncertainty,corroborating asymmetric pass-through predictions.Our results further show that a positive shock in energy prices leads to an increase in restaurant and hotel prices.Furthermore,asymmetric causality indicates that a positive shock in the exchange rate causes a positive shock to restaurant and hotel prices.We found feedback causal effects between positive and negative shocks in monetary policy uncertainty and positive and negative shocks in the exchange rate.Additionally,we detected a one-way asymmetric causality,flowing from a positive(negative)shock to a positive(negative)shock in energy prices.Therefore,these findings provide insights for policymakers to achieve low and stable prices in the US restaurant and hotel industry through sound monetary policy formulations.Highlights.The drivers of restaurant and hotel business in tourism destinations are examined.There is asymmetric pass-through of exchange rate and monetary policy uncertainty.A stronger pass-through is observed during appreciation and a negative shock to monetary policy uncertainty.There is asymmetric causality from positive shock in exchange rate to postive shock in restaurant and hotel prices.

RMB Exchange Rate,Overseas Education,and High-Quality Economic Growth

China is transitioning its industrial structure from labor-and resource-intensive industries that previously contributed significantly to the country’s GDP growth to technology-intensive industries emphasizing a highly-skilled workforce and sustainability to achieve high-quality economic growth.This paper examines the impact of the RMB exchange rate on high-quality economic growth through theoretical modeling and empirical analysis and discusses the variable of overseas education to explore the mechanism of how the RMB exchange rate and overseas education jointly impact high-quality economic growth.The research sample includes the National Bureau of Statistics data on education from 1995 to 2015,the Bank for International Settlements(BIS)data on the RMB exchange rate,and the added value of China’s high-quality economic growth estimated based on the national economy data.An empirical analysis of theoretical expectations was conducted,finding that RMB appreciation could make a positive contribution to China’s high-quality economic growth;RMB exchange rate fluctuations would impact the relative cost of overseas education and overseas returnees could have a positive impact on domestic resource utilization efficiency and domestic capacity to make sci-tech innovations,thereby injecting vitality to high-quality economic growth.This study focuses on both the RMB exchange rate and the population studying abroad,providing additional observation dimensions to existing research.

Trend Analysis of Exchange Rate of the Ghana Cedi against the US Dollar Using Time Series

Exchange rate volatility or its stability is a key determinant of the state of a country’s economy. The Ghana cedi’s performance against the US dollar in recent times has been the worse in the past decade. This has resulted in high inflation, high cost of living and high cost of production in Ghana. Despite the recent economic recovery growth, the cedi continues to strife in high rate of exchange against the dollar. This study examines and models a trend, and makes predictions of future rates of the cedis against the US dollar. Methodology: The study used a 13-year data of exchange rates of Ghana cedi and the US dollar spanning from 2010 to mid-2023 from the Bank of Ghana’s economic data on exchange rates, Ghana Stock Exchange and the World Bank. The ARIMA and SARIMA models were used to model the trends and for forecasting, taking into the consideration the asymmetric and seasonal effect of the data. Results: The outputs show that, the Ghana cedi will continue to rise but steadily against the US dollar for the remaining months of 2023 except in December, and continue to decline afterwards through into 2024. Conclusion: The cedi continues to weaken in value and the strength of its purchasing power. A weaker currency depicts a “junk” economy which affects its foreign investment. As the US dollar continues to rise, the Government and policy makers must implement effective policies to stabilize its rise against the cedi. Export of commodities must increase in addition to import restrictions to balance trade deficit and to strengthen the Ghana Cedi.

An Artificial Neural Network Model to Forecast Exchange Rates

For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto-Based. The objective of the research is to predict the trend of the ex-change rate Euro/USD up to three days ahead of last data available. The variable of output of the ANN designed is then the daily exchange rate Euro/Dollar and the frequency of data collection of variables of input and the output is daily. By the analysis of the data it is possible to conclude that the ANN model developed can largely predict the trend to three days of exchange rate Euro/USD.

The Most Sensitive Exchange Rates for Tin Based on the Major Commodity Production Countries

This paper examines the most sensitive exchange rates for tin price based on China and Indonesia that these are the world’s first and second largest tin producers.The export data from these countries have shared over 75 per cent of global tin supply that relates significantly with the Indonesian exchange rate based on the Canonical Correlation Analysis(CCA).Furthermore,the future tin prices are forecasted using the weighted least squares(WLS)model.This model is selected since it takes into account the non-normally distribution and heteroscedasticity of the original data.Overall,this result suggests that the Indonesian exchange rate is superior in predicting the future tin price rather than the Chinese exchange rate while China is the largest tin producer in the world.This is caused that the Chinese exchange rate cannot appreciate to other currency baskets.

Testing for Nonlinearity in Five Emerging Markets' Exchange Rates

Unlike the Brock,Dechert,and Scheinkman(BDS)test,the Surrogate Data method is not only able to provide a nonlinearity test for exchange rates,but also distinguish whether the nonlinear features come from the economic system itself.Our analysis gives some evidence on the presence of nonlinear dependency of five emerging markets' exchange rates by using BDS test.Furthermore,we present evidence in existence of intrinsic and deterministic nonlinearity in the five exchange rates using the Surrogate Data method,comparing the original data and surrogate data and rejecting the null hypothesis in confidence coefficient 95%.The results provide the basis for the analyses of the chaotic properties of exchange rate time series and their future nonlinear forecasting.

Causes of the exchange rate with the U.S. trade deficit

With debate that the U.S. trade deficit is due to overestimate exchange rate of RMB, the paper pointed out that exchange rate is not the main factor for the formation of balance of payments on trade. U.S. trade deficit is largely due to the current international monetary system, international division of labor and the trade structure determined by it, low savings rates and other factors.

Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models:a comparison based on normal and Student's t-error distribution

Background:Modeling exchange rate volatility has remained crucially important because of its diverse implications.This study aimed to address the issue of error distribution assumption in modeling and forecasting exchange rate volatility between the Bangladeshi taka(BDT)and the US dollar($).Methods:Using daily exchange rates for 7 years(January 1,2008,to April 30,2015),this study attempted to model dynamics following generalized autoregressive conditional heteroscedastic(GARCH),asymmetric power ARCH(APARCH),exponential generalized autoregressive conditional heteroscedstic(EGARCH),threshold generalized autoregressive conditional heteroscedstic(TGARCH),and integrated generalized autoregressive conditional heteroscedstic(IGARCH)processes under both normal and Student’s t-distribution assumptions for errors.Results and Conclusions:It was found that,in contrast with the normal distribution,the application of Student’s t-distribution for errors helped the models satisfy the diagnostic tests and show improved forecasting accuracy.With such error distribution for out-of-sample volatility forecasting,AR(2)–GARCH(1,1)is considered the best.

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words.The dataset was collected in raw form,and was subjected to natural language processing by way of data preprocessing.Response variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in it.To better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space.Clusters that were obtained using a sampling approach were then used for data optimization.Five machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized dataset.The results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

Impacts of Exchange Rate Volatility and FDI on Technical Efficiency—A Case Study of Vietnamese Agricultural Sector

The objective of this research is to examine impacts of exchange rate volatility and FDI on efficiency of the Vietnamese agricultural sector at the provincial level for the period 1998-2011. Due to the characteristic of high uncertainty in agricultural production, the chance-constrained programming model would be used to estimate efficiency of the agricultural production sector. In order to study impacts of exchange rate volatility and FDI, we employ the two-stage model. In the first stage, we use the chance-constrained programming model to measure technical efficiency and ARIMA model to quantify exchange rate volatility. In the second stage, we use the fixed effect model to evaluate impacts of exchange rate volatility and FDI on efficiency of agricultural production in poor and rich provinces. The estimated results show that fluctuation in exchange rate volatility would reduce efficiency in agricultural production but FDI has an insignificant impact on the efficient production in Vietnam agricultural sector.

The Influence of Bond Market Opening in Trade Field on RMB Internationalization Foreign Exchange Rate

In order to verify that the open bond market is the most direct choice to promote the internationalization of RMB,the empirical analysis method is used to analyze the impact of bond liberalization on the internationalization of RMB foreign exchange rate.Firstly,the data source processing method of empirical analysis is introduced.Then the dynamic panel data regression model and panel threshold model are built to verify the influence factors of RMB internationalization foreign exchange rate and the influence of debt market opening on RMB foreign exchange rate,respectively.The results show that the opening of the creditor's rights market has a significant positive promoting effect on the internationalization of RMB foreign exchange rate,and the development of the domestic creditor's rights market has a significant promoting effect on the internationalization of RMB foreign exchange rate.The study has a certain role in promoting the status of China's currency in the international community.

Influential factors on the exchange rate of dissolved inorganic nutrients at the sediment-water interface in Jiaozhou Bay, China

Incubation experiments are carried out to study the exchange rates of dissolved inorganic nutrients including silicate, phosphate, ammonium, nitrite, and nitrate (vSiO3-Si, vPO4-P, vNH4-N, vNO2-N and vNO3-N) at the sediment-water interface in the Jiaozhou Bay. Major factors influencing the exchange rates are discussed in detail, which include the dissolved inorganic nutrient concentrations in porewater (Cpw), water and clay contents, and grain size of the sediments (CH2O, Cclay and GSsed). The results may provide insight into the dynamics of nutrient transport and the environmental capacity of nutrients in Jiaozhou Bay, and should be beneficial to solving the problems caused by excessive nutrient input this area.

Discussion of Exchange Rate Fluctcation Using IS-LM Model

This paper analyses the essential cause of exchange market fluctuation by using IS－LM model: excessive investment causes goods overstocking in goods market, which brings about supply exceeding demand:financial market loses equilibrium as well, resulting imbalance of money demand and money supply, which causes interest rate to drop. This Paper gives the relationship between interest rate and exchange rate: as a result of interest rate dropping, exchange rate is in great turbulence,whose fluctuation range relates to some coefficients.

Influence of magnetic field sweep rate on the hysteresis loops of Ni0.8Fe0.2/Fe0.5Mn0.5 exchange bias bilayer

The influence of the magnetic field sweep rate on the hysteresis loops of exchange bias Ni0.8Fe0.2/Fe0.5Mn0.5 bilayers has been investigated with a vibrating sample magnetometer. It was found that the sweep rate of 13.6 kA/4πms is high enough to bring about obvious changes in the hysteresis loops of the exchange bias bilayer. High sweep rate in the magnetization reversal stage enlarges the coercivity of the sample, while high sweep rate in the saturation state reduces the coercivity. The above phenomena were attributed to magnetic viscosity in the ferromagnetic layer enhanced by the interface exchange interaction and domain magnetization reversals assisted by thermal fluctuation in the antiferromagnetic layer respectively.

Using fuzzy neural networks for RMB/USD real exchange rate forecasting

In order to aim at improving the forecasting performance of the RMB/USD exchange rate, this paper proposes a new architecture of fuzzy neural networks based on fuzzy logic, and the method of point differential, which guarantees not only the direction of weight correction, but also the needed precision for the BP algorithm. In applying genetic algorithms for optimal performance, this approach, in the forecasting of the RMB/USD real exchange rate from 1994 to 2000, obviously outperforms typical BP Neural Networks and exhibits a higher capacity in regard to nonlinear, time-variablility, and illegibility of the exchange rate.

Effects of sulfur dioxide on growth， gas exchange rate and leaf sulfur content of massion pine seedlings

The effects of sulfur dioxide(SO_2) on the dry weight growth gas exchange rate and leaf sulfur content of massion pine(Pinus massoniana Lamb. )were investigated. The results obtained in this study show that the dry weight growth and net photosynthetic rate of masson pine seedlings are reduced by exposure to SO_2 at ≥100 ppb. From these results,one of the main causes in the dieback of masson pine forest reported in Chongqing,China may be relatively high concentrations of atmospheric SO_2 in the relevant area.

Numerical Computing for a Class of Free Multipoint Boundary Value Problem of O.D.E in the Intervention of Exchange Rate

In this paper by means of generalized shooting method and homotopy technique a numerical method was given for computing free multipoint boundary value problem proposed in the intervention of exchange rate by Cadenillas and Femaado Zapatero. A numerical example was given for illustrating the validity of this method.

Subprime Mortgage Crisis and the Exchange Rate Channel: Evidence from Six Countries of the Zone Euro-Med

This article proposes to assess the impact of the subprime mortgage crisis on the exchange rate channel in 6 countries of the Euro-Med zone. To do this, our analysis is based on three evaluation methods that allow us to conclude that the exchange rate channel is better evaluated by the non-linear approach of the countries studied. Thus, the depreciation of the exchange rate adopted by the countries of the Med zone did worse macroeconomic stability and economic convergence.