Sparse Grid Quadrature in High Dimensions with Applications in Finance and Insurance [electronic resource] / by Markus Holtz.

By:

Holtz, Markus [author.]

Contributor(s):

SpringerLink (Online service)

Series: Lecture Notes in Computational Science and Engineering ; 77Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: VIII, 192 p. 32 illus. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783642160042

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

518 23

LOC classification:

QA71-90

Online resources:

Click here to access online

In: Springer eBooksSummary: 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.

Item type:

eBooks

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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.

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