Sparse Grid Quadrature in High Dimensions with Applications in Finance and Insurance [electronic resource] / by Markus Holtz.
Tipo de material:
TextoSeries Lecture Notes in Computational Science and Engineering, 77Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: VIII, 192 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642160042Trabajos contenidos: SpringerLink (Online service)Tema(s): Mathematics | Finance | Computer science -- Mathematics | Mathematics | Computational Mathematics and Numerical Analysis | Quantitative FinanceFormatos físicos adicionales: Sin títuloClasificación CDD: 518 | 518 Clasificación LoC:QA71-90Recursos en línea: de clik aquí para ver el libro electrónico Springer eBooksResumen: 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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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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