Sparse Grid Quadrature in High Dimensions with Applications in Finance and Insurance: Lecture Notes in Computational Science and Engineering, cartea 77
Autor Markus Holtzen Limba Engleză Paperback – dec 2012
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Paperback (1) | 376.16 lei 6-8 săpt. | |
Springer Berlin, Heidelberg – dec 2012 | 376.16 lei 6-8 săpt. | |
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Springer Berlin, Heidelberg – 25 oct 2010 | 375.60 lei 6-8 săpt. |
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Specificații
ISBN-13: 9783642265631
ISBN-10: 3642265634
Pagini: 200
Ilustrații: VIII, 192 p. 32 illus.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.29 kg
Ediția:2011
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Computational Science and Engineering
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642265634
Pagini: 200
Ilustrații: VIII, 192 p. 32 illus.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.29 kg
Ediția:2011
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Lecture Notes in Computational Science and Engineering
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchNotă biografică
• Scientific employee at the Institute for Numerical Simulation at the University of Bonn (July 2004 - January 2009)• Involved in several teaching activities and research projects in the area of computational finance partly in close cooperation with financial institutions• Since January 2009 at head office of Baloise Group working on the introduction of stochastic models for life insurance portfolios
Textul de pe ultima copertă
This book deals with the numerical analysis and efficient numerical treatment of high-dimensional integrals using sparse grids and other dimension-wise integration techniques with applications to finance and insurance. The book focuses on providing insights into the interplay between coordinate transformations, effective dimensions and the convergence behaviour of sparse grid methods. The techniques, derivations and algorithms are illustrated by many examples, figures and code segments. Numerical experiments with applications from finance and insurance show that the approaches presented in this book can be faster and more accurate than (quasi-) Monte Carlo methods, even for integrands with hundreds of dimensions.
Caracteristici
Includes supplementary material: sn.pub/extras