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Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-

Publication: Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing. Published Nov 2, 2021.

Research & Papers
arxiv.orgPublished Nov 2, 2021
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Overview

Publication: Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing. Published Nov 2, 2021.

Publication information

Publication title
Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing
Authors
Christian Bayer, Chiheb Ben Hammouda, Raúl Tempone
Published
Nov 2, 2021
Last publication revision
Jun 29, 2022
Subject areas
q-fin.CP, cs.CC, cs.CE, math.NA, q-fin.PR
DOI
10.1080/14697688.2022.2135455 ↗
Publication source
arxiv.org · abs/2111.01874v2 ↗

Source: arXiv API · observed Oct 11, 2026.

Source links & provenance

https://arxiv.org/abs/2111.01874 ↗

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Screenshot unavailableResource information and source links remain available. DeepIntoStreams/TSFM_Finance — reviewed source website screenshot
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DeepIntoStreams/TSFM_Finance

github.com

the official implementation of the paper “Re(visiting) Time Series Foundation Models in Finance”.

GitHub stars
31
Language
Jupyter Notebook
Code license
Apache-2.0
foundation-modeltime-seriesresearch-lab
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