Copula Theory and Its Applications

478 citations recorded by the source corpus.

Subfields: Finance

How researchers see this paper

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Connectionist multivariate density-estimation and its application to speech synthesis (2016, methodology): “Jaworski et al. (2010) review the copula approach to multivariate modelling.”
From Dependence to Causation (2016, background): “, 2001), spatial statistics, civil engineering, and random vector generation (Jaworski et al., 2010).”
Basis-Adaptive Sparse Polynomial Chaos Expansion for Probabilistic Power Flow (2017, unknown): “However, the Copula theory enables a more general and flexible modelling of the dependence structure [28].”
The shifting dependence dynamics between the G7 stock markets (2018, unknown): “Since the seminal work of Sklar (1959), copula theory has rapidly grown to cover not only symmetric but also asymmetric copula families in both bivariate and multivariate settings (Jaworski et al. 2010).”
On the return period and design in a multivariate framework (2011, background): “G. Salvadori et al.: Return period and design For a thorough theoretical introduction to copulas see Joe (1997); Nelsen (2006); for a practical approach see Salvadori et al. (2007); Jaworski et al. (2010).”
Statistics and Quantitative Risk Management for Banking and Insurance (2014, background): “Summaries of the main results partly including historical overviews can be found in the textbooks of Schweizer and Sklar (1983, Chapter 6), Nelsen (1999), Cherubini et al. (2004), or Jaworski et al. (2010).”