Statistical quantitative methods in finance: (Record no. 9995)

MARC details
000 -LEADER
fixed length control field 02217nam a22002297a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250521115739.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250521b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9798868809613
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 332.015195
Item number AHL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ahlawat, Samit
245 ## - TITLE STATEMENT
Title Statistical quantitative methods in finance:
Remainder of title from theory to quantitative portfolio management
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Apress Media, LLC
Place of publication, distribution, etc. New York
Date of publication, distribution, etc. 2025
300 ## - PHYSICAL DESCRIPTION
Extent xvi, 295 p.
365 ## - TRADE PRICE
Price type code EUR
Price amount 44.99
520 ## - SUMMARY, ETC.
Summary, etc. Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance.<br/><br/>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.<br/><br/>By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges.<br/><br/>(https://link.springer.com/book/10.1007/979-8-8688-0962-0)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Bayesian methods
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Quantitative finance
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning models
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Portfolio management
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical finance
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Bill No Bill Date Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Accession Number Date last seen Copy number Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Finance & Accounting 1192809 13-05-2025 Indian Institute of Management LRC Indian Institute of Management LRC General Stacks 05/20/2025 Atlantic Publishers & Distributors 2857.09   332.015195 AHL 008736 05/20/2025 1 4395.52 05/20/2025 Book

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