quantitative finance case studies python de chris kelliher (26 résultats)

Etat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

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Etat : New. 2025. 2nd Edition. hardcover. . . . . .

Etat : New. 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 Mathem.

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Hardcover. Etat : Brand New. 2nd edition. 792 pages. 10.00x7.00x10.00 inches. In Stock.

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Hardcover. Etat : new. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Etat : new. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Etat : Brand New. 2nd edition. 792 pages. 10.00x7.00x10.00 inches. In Stock. This item is printed on demand.

Buch. Etat : Neu. Quantitative Finance with Case Studies in Python | A Practical Guide to Investment Management, Trading and Financial Engineering | Chris Kelliher | Buch | Einband - fest (Hardcover) | Englisch | 2025 | Chapman and Hall/CRC | EAN 9781032868004 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Hardcover. Etat : new. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.