Copula‐based analysis of multivariate dependence patterns between dimensions of poverty in Europe

2020 ◽  
Author(s):  
César García‐Gómez ◽  
Ana Pérez ◽  
Mercedes Prieto‐Alaiz
Keyword(s):  
Multivariate Dependence

Antithetic Variates, Multivariate Dependence and Simulation of Stochastic Systems

1985 ◽  
Vol 31 (1) ◽  
pp. 66-77 ◽  
Author(s):  
Reuven Y. Rubinstein ◽  
Gennady Samorodnitsky ◽  
Moshe Shaked
Keyword(s):  
Stochastic Systems ◽  
Antithetic Variates ◽  
Multivariate Dependence

Applications of Artificial Neural Networks in Economics and Finance

2018 ◽  
pp. 6631-6641
Author(s):  
Iva Mihaylova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Trends ◽  
Dependence Analysis ◽  
Future Trends ◽  
Powerful Technique ◽  
Analytic Tool ◽  
The Future ◽  
Multivariate Dependence ◽  
Artificial Neural

Artificial neural Networks (ANNs) are a powerful technique for multivariate dependence analysis. Originally inspired by neuroscience, ANNs are becoming an increasingly attractive analytic tool for applications in the area of economics and finance due to the flexible solutions they offer. The purpose of this article is to present such important applications with an emphasis on recent research trends. The contributions are grouped as follows: ANNs (1) for prediction, (2) for classification and (3) for modelling. The chapter concludes with the future trends in the ANNs research in economics and finance.

ON A MULTIVARIATE GENERALIZED POLYA PROCESS WITHOUT REGULARITY PROPERTY

2019 ◽  
Vol 34 (4) ◽  
pp. 484-506
Author(s):  
Ji Hwan Cha ◽  
F.G. Badía
Keyword(s):  
Regularity Property ◽  
Dependence Structure ◽  
Counting Processes ◽  
Multiple Events ◽  
New Class ◽  
Different Types ◽  
Polya Process ◽  
Multivariate Dependence ◽  
Pólya Process ◽  
Stochastic Properties

Most of the multivariate counting processes studied in the literature are regular processes, which implies, ignoring the types of the events, the non-occurrence of multiple events. However, in practice, several different types of events may occur simultaneously. In this paper, a new class of multivariate counting processes which allow simultaneous occurrences of multiple types of events is suggested and its stochastic properties are studied. For the modeling of such kind of process, we rely on the tool of superposition of seed counting processes. It will be shown that the stochastic properties of the proposed class of multivariate counting processes are explicitly expressed. Furthermore, the marginal processes are also explicitly obtained. We analyze the multivariate dependence structure of the proposed class of counting processes.

Models for construction of multivariate dependence – a comparison study

2009 ◽  
Vol 15 (7-8) ◽  
pp. 639-659 ◽  
Author(s):  
Kjersti Aas ◽  
Daniel Berg
Keyword(s):  
Comparison Study ◽  
Multivariate Dependence

scholarly journals Long memory dynamics for multivariate dependence under heavy tails

2014 ◽  
Vol 29 ◽  
pp. 187-206 ◽  
Author(s):  
Paweł Janus ◽  
Siem Jan Koopman ◽  
André Lucas
Keyword(s):  
Long Memory ◽  
Heavy Tails ◽  
Multivariate Dependence

scholarly journals A multivariate dependence measure for aggregating risks

2014 ◽  
Vol 263 ◽  
pp. 78-87 ◽  
Author(s):  
Jan Dhaene ◽  
Daniël Linders ◽  
Wim Schoutens ◽  
David Vyncke
Keyword(s):  
Dependence Measure ◽  
Multivariate Dependence

scholarly journals Multivariate dependence concepts through copulas

2015 ◽  
Vol 65 ◽  
pp. 24-33 ◽  
Author(s):  
Zheng Wei ◽  
Tonghui Wang ◽  
Phuong Anh Nguyen
Keyword(s):  
Dependence Concepts ◽  
Multivariate Dependence
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