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Introduction:

Foreign exchange (FX) volatility forecasting is of great importance in the financial markets as it helps investors and policymakers make informed decisions regarding risk management and investment strategies. Volatility forecasting models have been widely studied and utilized in the literature, but there is still room for improvement in terms of accuracy and efficiency. This thesis aims to contribute to the existing body of knowledge by proposing a novel FX volatility forecasting model that leverages the latest advancements in machine learning and econometric techniques.

Chapter One: 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 Two: Literature Review
2.1 Overview of FX Market
2.2 Importance of FX Volatility Forecasting
2.3 Traditional Volatility Forecasting Models
2.4 Machine Learning Approaches in Volatility Forecasting
2.5 Econometric Techniques in Volatility Forecasting
2.6 Evaluation Metrics for Volatility Forecasting Models
2.7 Recent Advances in FX Volatility Forecasting
2.8 Challenges in FX Volatility Forecasting
2.9 Empirical Studies on FX Volatility Forecasting Models
2.10 Gaps in Existing Literature

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Model Specification
3.4 Variable Selection
3.5 Model Estimation
3.6 Model Evaluation
3.7 Robustness Checks
3.8 Data Analysis Techniques

Chapter Four: Discussion of Findings
4.1 Descriptive Analysis of Data
4.2 Model Performance Comparison
4.3 Interpretation of Results
4.4 Sensitivity Analysis
4.5 Implications for Investors and Policymakers
4.6 Policy Recommendations
4.7 Future Research Directions

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to Literature
5.3 Practical Implications
5.4 Limitations of Study
5.5 Recommendations for Future Research
5.6 Conclusion

Thesis Overview:

FX volatility forecasting models play a crucial role in the financial markets by providing valuable insights for investors and policymakers to manage risks and optimize investment strategies. This thesis aims to propose a novel FX volatility forecasting model that leverages the latest advancements in machine learning and econometric techniques to enhance accuracy and efficiency. The study begins with an introduction that highlights the background, problem statement, objectives, limitations, scope, significance, structure, and definition of terms. A comprehensive literature review is presented in Chapter Two, discussing the importance of FX volatility forecasting, traditional models, machine learning approaches, evaluation metrics, recent advances, challenges, empirical studies, and gaps in existing literature. Chapter Three outlines the research methodology, including research design, data collection, model specification, variable selection, model estimation, model evaluation, robustness checks, and data analysis techniques. Chapter Four presents a detailed discussion of findings, including descriptive analysis, model performance comparison, interpretation of results, sensitivity analysis, implications for investors and policymakers, policy recommendations, and future research directions. The thesis concludes in Chapter Five with a summary of findings, contributions to literature, practical implications, limitations of the study, recommendations for future research, and a conclusion. Through this comprehensive analysis, the thesis aims to advance the understanding of FX volatility forecasting models and provide valuable insights for practitioners and academics in the field.

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