Data Science for Financial Econometrics /

This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the e...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Ngoc Thach, Nguyen (Editor), Kreinovich, Vladik (Editor), Trung, Nguyen Duc (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:1st ed. 2021.
Series:Studies in Computational Intelligence, 898
Subjects:
Online Access:Connect to the full text of this electronic book
Description
Summary:This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, id est, on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. .
Physical Description:1 online resource (X, 633 pages 91 illustrations, 71 illustrations in color.)
ISBN:9783030488536
ISSN:1860-9503 ;
DOI:10.1007/978-3-030-48853-6