Efficient and Accurate Evaluation Methods for Concordance Measures via Functional Tensor Characterizations of Copulas

Antonio Dalessandro () and Gareth W. Peters ()
Additional contact information
Antonio Dalessandro: University College London
Gareth W. Peters: Heriot-Watt University

Methodology and Computing in Applied Probability, 2020, vol. 22, issue 3, 1089-1124

Abstract: Abstract There is now an increasingly large number of proposed concordance measures available to capture, measure and quantify different notions of dependence in stochastic processes. However, evaluation of concordance measures to quantify such types of dependence for different copula models can be challenging. In this work, we propose a class of new methods that involves a highly accurate and computationally efficient procedure to evaluate concordance measures for a given copula, applicable even when sampling from the copula is not easily achieved. In addition, this then allows us to reconstruct maps of concordance measures locally in all regions of the state space for any range of copula parameters. We believe this technique will be a valuable tool for practitioners to understand better the behaviour of copula models and associated concordance measures expressed in terms of these copula models.

Keywords: Concordance measures; Copula functions; Copula infinitesimal generators; Martingale problem; Multidimensional semimartingales decomposition approximations; Semimartingales decomposition; Tensor algebra; 47N30; 60B15; 46N30; 62G32; 62H86 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11009-019-09752-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:22:y:2020:i:3:d:10.1007_s11009-019-09752-2

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-019-09752-2

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().