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Introduction

Exchange rate forecasting plays a crucial role in the decision-making process of various stakeholders including governments, businesses, investors, and policymakers. With the increasing globalization of markets, accurate exchange rate forecasting is essential for managing risk, making investment decisions, and crafting effective monetary policies. There are various models and techniques used for exchange rate forecasting, each with its strengths and limitations. This study aims to compare and evaluate different exchange rate forecasting models to determine their effectiveness and applicability in different scenarios.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Exchange Rate Forecasting
2.2 Traditional Time Series Models
2.3 Economic Fundamentals Models
2.4 Machine Learning Models
2.5 Hybrid Models
2.6 Comparative Studies
2.7 Accuracy Metrics
2.8 Factors Influencing Exchange Rate Forecasting
2.9 Challenges in Exchange Rate Forecasting
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Model Selection
3.4 Variable Selection
3.5 Model Evaluation Criteria
3.6 Empirical Analysis
3.7 Data Analysis Techniques
3.8 Limitations of the Methodology

Chapter 4: Discussion of Findings
4.1 Comparison of Exchange Rate Forecasting Models
4.2 Empirical Results
4.3 Model Performance Evaluation
4.4 Sensitivity Analysis
4.5 Robustness Checks
4.6 Interpretation of Results
4.7 Implications for Stakeholders
4.8 Recommendations for Future Research

Chapter 5: Conclusion
5.1 Summary of Findings
5.2 Contributions to Existing Literature
5.3 Practical Implications
5.4 Limitations of the Study
5.5 Future Research Directions
5.6 Conclusion

Thesis Overview

Exchange rate forecasting is a critical aspect of financial decision-making in today’s globalized economy. This thesis focuses on comparing different exchange rate forecasting models to identify their strengths, weaknesses, and applicability in various contexts. The study begins with an introduction to the importance of exchange rate forecasting and a discussion on the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis.

The literature review in Chapter 2 provides a comprehensive overview of traditional time series models, economic fundamentals models, machine learning models, and hybrid models used in exchange rate forecasting. It examines comparative studies, accuracy metrics, factors influencing exchange rate forecasting, and challenges faced in the process.

Chapter 3 outlines the research methodology, including research design, data collection, model selection, variable selection, model evaluation criteria, empirical analysis, and data analysis techniques. It also discusses the limitations of the methodology used in the study.

In Chapter 4, the discussion of findings focuses on comparing exchange rate forecasting models, presenting empirical results, evaluating model performance, conducting sensitivity analysis and robustness checks, interpreting results, and providing implications and recommendations for stakeholders.

The conclusion in Chapter 5 summarizes the findings of the study, highlights contributions to existing literature, outlines practical implications, identifies limitations of the research, suggests future research directions, and presents a concluding remark on exchange rate forecasting models comparison.

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