Introduction

This book introduces in detail the core concepts and practical applications of optimization methods in the financial field. As a textbook designed specifically for computational financial problems, it provides an in-depth exploration of how to use the most advanced optimization theories, algorithms, and software to solve financial problems efficiently and accurately.

The book alternately explains the theoretical foundations and efficient solution methods of major optimization problem categories, and combines them with practical modeling problems in mathematical finance. The mathematical techniques covered in this book include, but are not limited to, linear programming, quadratic programming, mixed integer programming, stochastic programming, cone programming, and robust optimization.

In terms of financial applications, the book discusses in detail key topics such as volatility estimation, portfolio optimization (including classic mean-variance models and multi-period models), index fund construction, optimal trade execution, dynamic portfolio allocation taking into account transaction costs and taxes, asset liability management, option pricing, and risk management (such as value at risk).

The content of this book is derived from the Master of Financial Engineering course and provides a wealth of examples, exercises and case studies to help readers apply theoretical knowledge to practical financial problems. It is ideal for quants, algorithmic traders, financial engineers, data scientists, and students, academics, and practitioners with a background in mathematics, operations research, or financial engineering.

Main features

• Solid theoretical foundation
• Rich practical cases
• Clear operating instructions
• Suitable for practical applications

Suitable for the crowd

• Quantitative Analyst
• Algorithmic Trader
• Financial Engineer
• Data Scientist