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Introduction:

Neural networks have gained popularity in recent years for their ability to model complex relationships in data and make predictions with high accuracy. In the field of finance, neural networks have been used for various applications such as stock price prediction, risk assessment, and portfolio optimization. One area where neural networks have shown promise is in options pricing, where they can help traders and investors make more informed decisions about buying and selling options contracts.

This thesis aims to explore the use of neural networks for options pricing and evaluate their effectiveness compared to traditional pricing models. The study will consider various factors that can affect options pricing, such as market volatility, interest rates, and underlying asset price movements. By developing and testing neural network models, this research seeks to provide insights into the potential benefits and limitations of using neural networks for options pricing.

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 Historical Overview of Options Pricing
2.2 Traditional Models for Options Pricing
2.3 Neural Networks in Finance
2.4 Applications of Neural Networks in Options Pricing
2.5 Comparison of Neural Networks and Traditional Pricing Models
2.6 Factors Affecting Options Pricing
2.7 Empirical Studies on Neural Networks for Options Pricing
2.8 Challenges and Limitations in Using Neural Networks for Options Pricing
2.9 Future Directions for Research in Neural Networks for Options Pricing
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Preprocessing of Data
3.4 Neural Network Architecture
3.5 Model Training and Validation
3.6 Evaluation of Model Performance
3.7 Sensitivity Analysis
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Neural Network Models for Options Pricing
4.2 Comparison with Traditional Pricing Models
4.3 Impact of Different Factors on Options Pricing
4.4 Interpretation of Results
4.5 Implications for Traders and Investors
4.6 Recommendations for Future Research

Chapter 5: Conclusion
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Limitations of the Study
5.5 Suggestions for Further Research
5.6 Conclusion

Thesis Overview:

Neural networks have shown promise in options pricing, offering a flexible and scalable approach to modeling the complex relationships involved in pricing options contracts. This thesis aims to investigate the effectiveness of neural networks in options pricing compared to traditional models.

The literature review provides a comprehensive overview of options pricing, traditional models, neural networks in finance, and previous studies on neural networks for options pricing. The research methodology outlines the data collection, preprocessing, neural network architecture, model training, and evaluation process.

The findings from the study will be discussed in detail in chapter four, including the analysis of neural network models, comparison with traditional models, and the impact of different factors on options pricing. The conclusion will summarize the key findings, contributions to the field, practical implications, limitations, and suggestions for further research in neural networks for options pricing.

Overall, this thesis aims to contribute to the growing body of research on neural networks in finance and provide insights into the potential benefits and limitations of using neural networks for options pricing.

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