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Introduction:

Extreme value theory (EVT) is a branch of statistics that focuses on the modeling and analysis of extreme events, or tail events, which are rare occurrences with high impact. In the context of financial risk management, EVT plays a crucial role in providing more accurate estimates of extreme events, such as market crashes or large losses, which are often not adequately captured by traditional risk management techniques. By understanding the extreme values of financial data, firms can better assess and mitigate their exposure to potential risks.

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 financial risk management
2.2 Traditional risk management techniques
2.3 Introduction to Extreme value theory
2.4 Applications of EVT in finance
2.5 EVT models for risk assessment
2.6 Criticisms and limitations of EVT
2.7 Recent developments in EVT
2.8 Empirical studies using EVT in financial risk management
2.9 Comparison of EVT with other risk management techniques
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Sampling techniques
3.4 EVT models selection
3.5 Data analysis techniques
3.6 Model validation methods
3.7 Ethical considerations
3.8 Limitations of research methodology

Chapter 4: Discussion of Findings
4.1 Descriptive analysis of financial data
4.2 EVT models estimation
4.3 Comparison of EVT models
4.4 Interpretation of results
4.5 Sensitivity analysis
4.6 Implications for risk management practices
4.7 Recommendations for future research
4.8 Practical implications for financial institutions

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the literature
5.3 Implications for practice
5.4 Limitations of study
5.5 Future research directions
5.6 Conclusion

Thesis Overview:

Extreme value theory (EVT) has gained significant importance in financial risk management due to its ability to provide accurate estimates of extreme events, which are critical for mitigating potential risks in the financial market. This thesis aims to explore the application of EVT in financial risk management by investigating the modeling and analysis of extreme values in financial data.

Chapter 1 provides an introduction to the study, presenting the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and key definitions. Chapter 2 reviews the existing literature on financial risk management, traditional risk management techniques, EVT, applications of EVT in finance, criticisms, recent developments, empirical studies, and comparisons with other techniques.

Chapter 3 outlines the research methodology, detailing the research design, data collection methods, sampling techniques, EVT model selection, data analysis, validation methods, and ethical considerations. Chapter 4 presents a discussion of findings, including descriptive analysis, EVT model estimation, comparisons, results interpretation, sensitivity analysis, implications for risk management, recommendations, and practical implications.

Chapter 5 concludes the thesis, summarizing the findings, discussing contributions to the literature, implications for practice, limitations, future research directions, and overall conclusions. Through this comprehensive analysis, this thesis aims to contribute to the growing body of knowledge on the application of EVT in financial risk management and provide valuable insights for practitioners in the field.

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