Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286812 
Year of Publication: 
2021
Citation: 
[Journal:] Computational Management Science [ISSN:] 1619-6988 [Volume:] 19 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021 [Pages:] 269-293
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
The nested distance builds on the Wasserstein distance to quantify the difference of stochastic processes, including also the evolution of information modelled by filtrations. The Sinkhorn divergence is a relaxation of the Wasserstein distance, which can be computed considerably faster. For this reason we employ the Sinkhorn divergence and take advantage of the related (fixed point) iteration algorithm. Furthermore, we investigate the transition of the entropy throughout the stages of the stochastic process and provide an entropy-regularized nested distance formulation, including a characterization of its dual. Numerical experiments affirm the computational advantage and supremacy.
Subjects: 
Nested distance
Optimal transport
Sinkhorn divergence
Entropy
JEL: 
C08
C15
G07
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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