Copulas: From theory to application in Finance

By J. Rank. 2006

145 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b826e030eba79a5dfe0e65b874761a9601a84594

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Citations in the corpus

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data (2014, unknown): “The Frank Copula exhibits no tail dependence, and the Gaussian Copula describes a similar dependence as the Frank Copula, but with slightly higher densities in the lower and upper tails (Venter, 2002; Schmidt, 2007).”
Copula Methods for Forecasting Multivariate Time Series (2013, background): “Applications of copulas in other derivatives markets include Rosenberg (2003), Bennett and Kennedy (2004), Cherubini, et al. (2004), van den Goorbergh, et al. (2005), Salmon and Schleicher (2006), Grégoire, et al. (2008), Taylor and Wang (2010), and Cherubini, et al. (2012).”
Bivariate frequency analysis of floods using a diffusion based kernel density estimator (2013, background): “…marginal distributions; (ii) the use of parameters of the marginal distributions of FRVs to model the dependence between the FRVs, though in reality it is advantageous to model the dependence structure independent of the marginal distributions [Schmidt and Stadtm€uller, 2006; Schmidt, 2007].”
From Dependence to Causation (2016, background): “Rank (2007) set pa and pb to be Normal distributions, kab = m −1 ab,i = n and µab,i = (P −1 a (ui), P −1 b (vi)) yielding the so-called nonparametric copula, equivalent to a Gaussian kernel density estimate on the transformed sample {(Φ−1(ui),Φ−1(vi))}ni=1, where Φ−1 is the Normal inverse…”