Quantitative Finance with Case Studies in Python (Hardcover)
Language: English
Published by Taylor & Francis Ltd, 2025
Series: Book 70 of 71 - Chapman and Hall/CRC Financial Mathematics
- Hardcover
- New
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
AbeBooks seller since October 12, 2005
Condition: New
£ 160.01
Quantity: 1 available
Add to basketItem description from seller
Hardcover. 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 investorsIdeal textbook for first year graduate students in quantitative finance programs, such as those in masters programs in Mathematical Finance, Quant Finance or Financial EngineeringIncludes a perspective on the future of quant finance techniques, and in particular covers concepts of Machine Learning and Artificial IntelligenceFree-to-access repository with Python codes available at / 9781032014432 and on [CK1] 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
Seller Inventory # 9781032868004
- Title
- Quantitative Finance with Case Studies in Python (Hardcover)
- Author
- Chris Kelliher
- Publisher
- Taylor & Francis Ltd
- Publication year
- 2025
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1032868007
- ISBN 13
- 9781032868004
- Edition
- 2nd Edition
- Series
- Book 70 of 71: Chapman and Hall/CRC Financial Mathematics
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]
"Synopsis" may belong to another edition of this title.
About the Author
Chris Kelliher is a multi-asset portfolio manager and senior quantitative researcher with over 20 years of investment experience at asset management firms and hedge funds. In addition, Mr. Kelliher is an adjunct professor in the Master's in Mathematical Finance and Financial Technology program at Boston University’s Questrom School of Business where he has also held the role of Executive Director. In these roles, he has taught graduate-level courses on computational methods in finance, fixed income, credit risk and programming for quant finance. He is also the author of "Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering" and was named among the top 20 US Finance Professors in 2024 by Rebellion Research. Mr. Kelliher earned a BA in economics from Gordon College, where he graduated Cum Laude with departmental honours and an MS in mathematical finance from New York University’s Courant Institute.
"About the title" may belong to another edition of this title.
Grand Eagle Retail
Bensenville, IL, U.S.A.
AbeBooks seller since October 12, 2005
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