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Introduction

Risk factor models in asset pricing have become a crucial tool in understanding the relationship between risk and return in financial markets. These models, such as the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model, seek to explain the expected returns of assets based on their exposure to different systematic risk factors. As financial markets continue to evolve and become increasingly complex, the need for more sophisticated risk factor models has become apparent. This thesis aims to explore the existing risk factor models in asset pricing and propose enhancements to better capture the risk-return dynamics in today’s markets.

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 risk factor models in asset pricing
2.2 Capital Asset Pricing Model (CAPM)
2.3 Fama-French three-factor model
2.4 Arbitrage Pricing Theory (APT)
2.5 Multi-factor models
2.6 Recent developments in risk factor models
2.7 Empirical studies on risk factor models
2.8 Criticisms of existing models
2.9 Alternative risk factor models
2.10 Current research gaps

Chapter 3: Research Methodology

3.1 Research philosophy
3.2 Research approach
3.3 Data collection methods
3.4 Sample selection criteria
3.5 Model specification
3.6 Empirical analysis techniques
3.7 Validity and reliability of data
3.8 Ethical considerations

Chapter 4: Discussion of Findings

4.1 Analysis of existing risk factor models
4.2 Comparison of model performance
4.3 Identification of weaknesses in current models
4.4 Proposed enhancements to risk factor models
4.5 Empirical results and implications
4.6 Robustness checks and sensitivity analysis
4.7 Limitations of the study
4.8 Recommendations for future research

Chapter 5: Conclusion and Summary

In conclusion, this thesis provides a comprehensive review of risk factor models in asset pricing and proposes improvements to enhance their effectiveness in capturing the risk-return relationship. By addressing the limitations of existing models and incorporating new factors, such as liquidity and macroeconomic variables, we can better understand and predict asset pricing dynamics in today’s complex financial markets. The findings of this study have implications for investors, asset managers, and policymakers seeking to make informed decisions in an uncertain and rapidly changing investment landscape.

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