Statistical quantitative methods in finance: from theory to quantitative portfolio management
Ahlawat, Samit
Statistical quantitative methods in finance: from theory to quantitative portfolio management - New York Apress Media, LLC 2025 - xvi, 295 p.
Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.
This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models.
By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges.
(https://link.springer.com/book/10.1007/979-8-8688-0962-0)
9798868809613
Bayesian methods
Quantitative finance
Machine learning models
Portfolio management
Mathematical finance
332.015195 / AHL
Statistical quantitative methods in finance: from theory to quantitative portfolio management - New York Apress Media, LLC 2025 - xvi, 295 p.
Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.
This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models.
By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges.
(https://link.springer.com/book/10.1007/979-8-8688-0962-0)
9798868809613
Bayesian methods
Quantitative finance
Machine learning models
Portfolio management
Mathematical finance
332.015195 / AHL