Data Science for Financial Econometrics

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ISBN 10: 3030488551 / ISBN 13: 9783030488550

Editore: Springer International Publishing, 2021

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Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.

Codice articolo 38226623/1

Dati bibliografici

• Titolo: Data Science for Financial Econometrics
• Editore: Springer International Publishing
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 3030488551
• ISBN 13: 9783030488550
• Rilegatura: Brossura
• Condizione: Hervorragend
• Cataloghi dei venditori:
  • Bücher

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Riassunto

<p>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, i.e., 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.&nbsp;&nbsp;</p><p></p>

Dalla quarta di copertina

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, i.e., 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.