Quantitative Finance with Case Studies in Python
A Practical Guide to Investment Management, Trading and Financial Engineering
Series: Chapman and Hall/CRC Financial Mathematics Series;
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Product details:
- Edition number 2
- Publisher Chapman and Hall
- Date of Publication 7 November 2025
- ISBN 9781032868004
- Binding Hardback
- No. of pages766 pages
- Size 254x178 mm
- Language English 700
Categories
Short description:
Quantitative Finance with Case Studies in Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management.
MoreLong description:
Quantitative Finance with Case Studies in Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors.
This new edition includes brand new material on data science and AI concepts, including large language models, as well as updated content to reflect the transition from Libor to SOFR to bring the text right up to date. It also includes expanded material on inflation and mortgage-backed securitie, more trade ideas embedded in each chapter and also via a dedicated chapter analyzing a set of derivatives trades. There are additional examples throughout based on recent market dynamics, including the post-Covid inflation shock and its impact on risk parity strategies.
Overall, the new edition is designed to be even more of a practical tool than the first edition, and more firmly rooted in real-world data, applications, and examples.
Features
· Useful as both a teaching resource and as a practical tool for professional investors
· Ideal textbook for first year graduate students in quantitative finance programs, such as those in master’s programs in Mathematical Finance, Quant Finance or Financial Engineering
· Includes a perspective on the future of quant finance techniques, and in particular covers concepts of Machine Learning and Artificial Intelligence
· Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432 and on https://github.com/lingyixu/Quant-Finance-With-Python-Code.[CK1]
"This ambitious book is a practical guide for aspirant quants, on both the buyside and the sellside [. . .] The author is both a lecturer and practitioner in the field. This is evident from the accessible style of writing, comprehensive examples and the way the topics are built up. The content is generally well balanced between theory and practice. There is a broad range of finance topics covered. From swaption and currency triangles to CDO mechanics to feature explainability in machine learning, few books in this space are as comprehensive."
—Mark Greenwood, Quantitative Finance
MoreTable of Contents:
Foreword Contributors Acknowledgments Section I Foundations of Quant Modeling Chapter 1 Setting the Stage: Quant Landscape Chapter 2 Setting the Stage: Landscape of Financial Instruments Chapter 3 Theoretical Underpinnings of Quant Modeling: Modeling the Risk Neutral Measure Chapter 4 Theoretical Underpinnings of Quant Modeling: Modeling the Physical Measure Section II Fundamentals of Coding and Data Analysis Chapter 5 Python Programming Environment Chapter 6 Programming Concepts in Python Chapter 7 Working with Financial Datasets Chapter 8 Data Science Techniques in Finance Chapter 9 Model Validation Section III Options Modeling Chapter 10 Stochastic Models Chapter 11 Options Pricing Techniques for European Options Chapter 12 Options Pricing Techniques for Exotic Options Chapter 13 Greeks and Options Trading Chapter 14 Extraction of Risk Neutral Densities Section IV Quant Modeling in Different Markets Chapter 15 Interest Rate Markets Chapter 16 Credit Markets Chapter 17 Foreign Exchange Markets Chapter 18 Equity & Commodity Market Section V Portfolio Construction & Risk Management Chapter 19 Portfolio Construction & Optimization Techniques Chapter 20 Modeling Expected Returns and Covariance Matrices Chapter 21 Risk Management Chapter 22 Quantitative Trading Models Chapter 23 Artificial Intelligence: Incorporating Machine Learning Techniques Chapter 24 Artificial Intelligence: Incorporating Deep Learning, Large Language Models and Working with Unstructured Data Bibliography Index
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