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Quantitative Finance with Case Studies in Python : A Practical Guide to Investment Management, Trading and Financial Engineering by Chris Kelliher (2025, Hardcover)
Late Knight Books (147)
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About this product
Product Identifiers
PublisherCRC Press LLC
ISBN-101032868007
ISBN-139781032868004
eBay Product ID (ePID)15079882571
Product Key Features
Edition2
Book TitleQuantitative Finance with Case Studies in Python : A Practical Guide to Investment Management, Trading and Financial Engineering
Number of Pages792 Pages
LanguageEnglish
Publication Year2025
TopicFinance / General, Applied, Programming Languages / Python
GenreMathematics, Computers, Business & Economics
AuthorChris Kelliher
Book SeriesChapman and Hall/Crc Financial Mathematics Ser.
FormatHardcover
Dimensions
Item Length10 in
Item Width7 in
Additional Product Features
Intended AudienceTrade
LCCN2025-024655
Dewey Edition23/eng/20250909
Reviews"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
Dewey Decimal332.6
SynopsisQuantitative 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., 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, mortgage-backed securities and counterparty risk. In addition, there is an increased emphasis on trade ideas, as well as examples throughout based on recent market dynamics, including the post-Covid inflation shock. 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]
LC Classification NumberHG4515.2.K445 2025
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