The exchange rate on a daily basis is an indispensable factor in the foreign exchange market as well as in international trade. Many traders make a profit based on the pip in the foreign exchange market. Moreover, inflation and deflation of a currency against another currency is the root of making a profit in the foreign exchange market. Even in the international trade many individual traders and multi-national corporations always carefully observes the fluctuation of the exchange rate in order to determine the exchange rate efficiently and accurately.
Because the more accurate the forecasted exchange rate is, the higher the chance becomes to make a profit only by investing a little amount of money in the foreign exchange market. The exchange rate has also significant impact on the export, import, foreign direct investment etc.
This paper pursues the goal to explain how an individual or an organization can formulate future exchange rate of any currency in an efficient and time effective way. To meet this demand, this paper utilizes the help of panel data and a regression model. As a sample, this paper considers USD/BDT for a forecast. It must be noted that, with different panel data of different currencies, the method will remain same if anyone wants to forecast exchange rates of different currencies.
Table of Contents
1. INTRODUCTION
2. METHODOLOGY
2.1. Identifying Variables:
2.1.1. Dependent Variable:
2.1.2. Independent Variables:
2.2. Model:
2.3. Data and Sample:
2.4. Scope of the Study:
3. FINDINGS AND ANALYSIS
3.1. Regression Analysis:
4. FORECASTING EXCHANGE RATE
4.1. Regression equation:
4.2. Forecasting exchange rate for August 2015:
4.3. Comparison between Forecasted exchange rate and Actual exchange rate:
5. CONCLUSION
Objectives and Topics
This paper aims to provide an efficient and time-effective method for individuals and organizations to forecast future exchange rates by utilizing a panel data regression model, specifically applied to the USD/BDT currency pair.
- Application of regression analysis in currency forecasting.
- Identification of key macroeconomic variables influencing exchange rates.
- Assessment of the relationship between inflation, interest rates, per capita income, and GDP growth with exchange rate fluctuations.
- Testing model accuracy by comparing forecasted values against actual historical exchange rate data.
Excerpt from the book
2.1.2. Independent Variables:
Inflation rate: Inflation rate is a percentage which indicates the increase in the general level of prices for goods and services. In a very simple word, it means how fast a currency loses its value.
Bank Interest rate: A bank interest rate is the rate at which the central bank of a country approves the short term loan towards commercial banks.
Per Person Income: Per person income is also called per capita income. Per person income indicates the amount of money earned be per person in a certain or given area.
GDP growth rate: GDP is the abbreviation form of gross domestic product. GDP growth rate is a measurement of how fast an economy is growing. Or in another way, it is the percentage changes of gross domestic product from one period to another.
Summary of Chapters
1. INTRODUCTION: Provides the foundational context of exchange rates in international trade and outlines the purpose of the study using regression models.
2. METHODOLOGY: Defines the variables used in the study, specifies the regression model structure, and details the data collection process and scope limitations.
3. FINDINGS AND ANALYSIS: Presents the statistical results of the regression analysis and evaluates the relationships between the selected independent variables and the USD/BDT exchange rate.
4. FORECASTING EXCHANGE RATE: Demonstrates the practical application of the established regression equation to generate a specific exchange rate forecast for August 2015.
5. CONCLUSION: Reflects on the effectiveness of the panel data approach and acknowledges the potential for improved accuracy by incorporating additional variables and data.
Keywords
Forecasting exchange rate, Panel data, Regression model, USD/BDT, Inflation rate, Bank interest rate, Per person income, GDP growth rate, Statistical significance, Currency fluctuation, Foreign exchange market, International trade, Quantitative analysis, Predictive modeling
Frequently Asked Questions
What is the primary focus of this research paper?
The paper focuses on developing a time-effective method to forecast currency exchange rates, specifically the USD/BDT pair, using statistical regression models.
Which central topics are addressed in the study?
The study covers the mechanisms of exchange rate determination, the impact of macroeconomic indicators like inflation and GDP growth, and the application of quantitative methods in financial forecasting.
What is the core objective or research question?
The core objective is to explain how individuals or organizations can accurately formulate future exchange rates using a panel data approach based on historical macroeconomic variables.
Which scientific method is utilized in this work?
The author utilizes an Ordinary Least Squares (OLS) regression model to analyze the causal effects of independent variables on the dependent variable (exchange rate).
What does the main body of the work cover?
The main body covers the identification of variables, the construction of the regression model, the collection of 10 years of monthly historical data, and the comparison of model forecasts against actual market results.
Which keywords characterize the paper?
The paper is characterized by terms such as Forecasting exchange rate, Panel data, Regression model, and various macroeconomic indicators like Inflation rate and GDP growth rate.
Why was the USD/BDT pair chosen as a sample?
It was chosen as a representative sample for the study, though the author notes that the methodology remains identical for forecasting any other currency pair using relevant panel data.
How does the model account for the "inflation rate" variable?
The analysis incorporates inflation as an independent variable to observe its impact on the exchange rate, finding that in this specific regression, it held a negative relationship with the exchange rate.
What conclusion does the author draw regarding the model's accuracy?
The author concludes that while the current model provides a reasonable explanation for exchange rate variations (with a high R-square value), accuracy can be further improved by including more data and additional significant variables.
- Citar trabajo
- Sajjad Hossine Sharif (Autor), 2015, Forecasting the exchange rate of currencies. A panel data approach, Múnich, GRIN Verlag, https://www.grin.com/document/351362
