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).
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BookSeries: Lecture Notes in Computational Science and Engineering: 77Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: VIII, 192 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642160042.Subject(s): Mathematics | Finance | Computer science -- Mathematics | Mathematics | Computational Mathematics and Numerical Analysis | Quantitative FinanceDDC classification: 518 | 518 Online resources: Click here to access online
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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.
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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