000			02217nam a22002297a 4500
005			20250521115739.0
008			250521b |||||||| |||| 00| 0 eng d
020			_a9798868809613
082			_a332.015195 _bAHL
100			_aAhlawat, Samit _923592
245			_aStatistical quantitative methods in finance: _bfrom theory to quantitative portfolio management
260			_bApress Media, LLC _aNew York _c2025
300			_axvi, 295 p.
365			_aEUR _b44.99
520			_aStatistical 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)
650			_aBayesian methods _924277
650			_aQuantitative finance _924029
650			_aMachine learning models _924278
650			_aPortfolio management
650			_aMathematical finance _924275
942			_cBK _2ddc
999			_c9995 _d9995