Optimal Investment

 
 
Springer (Verlag)
  • erschienen am 9. Januar 2013
 
  • Buch
  • |
  • Softcover
  • |
  • 168 Seiten
978-3-642-35201-0 (ISBN)
 
Readers of this book will learn how to solve a wide range of optimal investment problems arising in finance and economics. Starting from the fundamental Merton problem, many variants are presented and solved, often using numerical techniques that the book also covers. The final chapter assesses the relevance of many of the models in common use when applied to data.
Paperback
2013
  • Englisch
  • Heidelberg
  • |
  • Deutschland
Springer Berlin
  • Für Beruf und Forschung
  • |
  • Professional/practitioner
  • 41 s/w Abbildungen, 3 farbige Abbildungen
  • |
  • 3 Illustrations, color; 41 Illustrations, black and white; X, 156 p. 44 illus., 3 illus. in color.
  • Höhe: 235 mm
  • |
  • Breite: 155 mm
  • |
  • Dicke: 9 mm
  • 265 gr
978-3-642-35201-0 (9783642352010)
10.1007/978-3-642-35202-7
weitere Ausgaben werden ermittelt
Preface.- The Merton Problem.- Variations.- Numerical Solution.- How Well Does It Work.- Index.- References.
From the book reviews:

"This short book would be an excellent supplementary text for a course in quantitative finance or useful to researchers or practitioners looking for an overview of one of the foundations of modern quantitative finance." (IEEE Control Systems Magazine, October, 2013)

"This book first focuses on the classical Merton problems and presents a range of techniques for solving optimal investment/consumption problems. ... I really enjoyed reading this book. ... it would be very helpful to students and researchers who are interested in financial engineering, corporate finance and asset pricing, and it would be worth keeping the book on their shelves." (Zhaojun Yang, Mathematical Reviews, September, 2013)
Readers of this book will learn how to solve a wide range of optimal investment problems arising in finance and economics.
Starting from the fundamental Merton problem, many variants are presented and solved, often using numerical techniques
that the book also covers. The final chapter assesses the relevance of many of the models in common use when applied to data.
Dewey Decimal Classfication (DDC)

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