Benedikt Wilbertz
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Benedikt Wilbertz has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Harald Luschgy
FB IV, Mathematics University of Trier Trier Germany
2 shared publications Gilles Pagès
Laboratoire de Probabilités et Modèles Aléatoires, UMR 7599, Université Paris 6, Case Courrier 188, 4 place Jussieu, 75252 Paris Cedex 05, France
1 shared publication- Gilles Pagès
Laboratoire de Probabilités et Modèles Aléatoires, UMR 7599, Université Paris 6, case 188, 4, pl. Jussieu, F-75252 Paris Cedex 5, France
1 shared publication - Gilles Pagès
Sorbonne Université and Université Paris Cité LPSM
1 shared publication
Latest Publications
- Open Access
- 10 May 2010
- Published by EDP Sciences in ESAIM: Probability and Statistics
- Vol. 14, 93-116
Asymptotically optimal quantization schemes for Gaussian processes on Hilbert spaces
We describe quantization designs which lead to asymptotically and order optimal functional quantizers for Gaussian processes in a Hilbert space setting. Regular variation of the eigenvalues of the covariance operator plays a crucial role to achieve these rates. For the development of a constructive quantization scheme we rely on the knowledge of the eigenvectors of the covariance operator in order to transform the problem into a finite dimensional quantization problem of normal distributions.
- 10 May 2010
- Published by EDP Sciences in ESAIM: Probability and Statistics
- Vol. 14, 93-116
Open AccessAsymptotically optimal quantization schemes for Gaussian processes on Hilbert spaces
We describe quantization designs which lead to asymptotically and order optimal functional quantizers for Gaussian processes in a Hilbert space setting. Regular variation of the eigenvalues of the covariance operator plays a crucial role to achieve these rates. For the development of a constructive quantization scheme we rely on the knowledge of the eigenvectors of the covariance operator in order to transform the problem into a finite dimensional quantization problem of normal distributions.
- Open Access
How to speed up the quantization tree algorithm with an application to swing options
In this paper, we suggest several improvements to the numerical implementation of the quantization method for stochastic control problems in order to get fast and accurate premium estimations. This technique is applied to derivative pricing in energy markets. Several ways of modeling energy derivatives are described and finally numerical examples including parallel execution on multi-processor devices are presented to illustrate the accuracy of these methods and their execution times.
Open AccessHow to speed up the quantization tree algorithm with an application to swing options
In this paper, we suggest several improvements to the numerical implementation of the quantization method for stochastic control problems in order to get fast and accurate premium estimations. This technique is applied to derivative pricing in energy markets. Several ways of modeling energy derivatives are described and finally numerical examples including parallel execution on multi-processor devices are presented to illustrate the accuracy of these methods and their execution times.
- Open Access
Asymptotically optimal quantization schemes for Gaussian processes
We describe quantization designs which lead to asymptotically and order optimal functional quantizers. Regular variation of the eigenvalues of the covariance operator plays a crucial role to achieve these rates. For the development of a constructive quantization scheme we rely on the knowledge of the eigenvectors of the covariance operator in order to transform the problem into a finite dimensional quantization problem of normal distributions. Furthermore we derive a high-resolution formula for the $L^2$-quantization errors of Riemann-Liouville processes.
Open AccessAsymptotically optimal quantization schemes for Gaussian processes
We describe quantization designs which lead to asymptotically and order optimal functional quantizers. Regular variation of the eigenvalues of the covariance operator plays a crucial role to achieve these rates. For the development of a constructive quantization scheme we rely on the knowledge of the eigenvectors of the covariance operator in order to transform the problem into a finite dimensional quantization problem of normal distributions. Furthermore we derive a high-resolution formula for the $L^2$-quantization errors of Riemann-Liouville processes.