Modeling International Financial Returns with a Multivariate Regime Switching Copula

By Lorán Chollete, Andréas Heinen, Alfonso Valdesogo. 2009

345 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ed3535a072b56c3f5f6ed9640c447c776bedfb9e

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Citations in the corpus

Selecting and estimating regular vine copulae and application to financial returns (2012, methodology): “While D-vine based models are started to be used in many applications (Fischer et al. (2009), Min and Czado (2010), Chollete et al. (2009), Hofmann and Czado (2010), Mendes et al. (2010), Salinas-Guti´errez et al. (2010), Erdorf et al. (2011), Mercier and Frison (2009), Smith et al. (2010)),…”
Regime Switching Vine Copula Models for Global Equity and Volatility Indices (2016, unknown): “The general idea of such setups based on Markov chains was introduced by Hamilton (1989) and has recently been linked with copula models as well (cf. Chollete et al. (2009), da Silva Filhoa et al. (2012), Wang et al. (2013) or Stöber and Czado (2014)).”
About the number of vines and regular vines on n nodes (2010, background): “More recently they are being applied in statistical analysis of multivariate data sets [1], [3], [2] and [5].”
Pair-Copula Constructions for Financial Applications: A Review (2016, background): “The work in [45] estimates a regime-switching model for the dependence of the stock indices of the G5 and of four Latin American countries.”
Selection strategies for regular vine copulae (2013, methodology): “They have been extended to include time varying copula parameters ([Almeida et al., 2012]), regime switching copulae ([Chollete et al., 2008, Stöber and Czado, 2013]), factor copula models ([Krupskii and Joe, 2012]), cross serial dependence ([Brechmann and Czado, 2012a]) and multivariate option…”
Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine (2013, methodology): “The Vuong and the Clarke tests are suitable to compare two non-nested models. Both are likelihood ratio based and related to the common Kullback-Leibler information criterion, which measures the distance between two statistical models. In the following let c1 and c2 be two competing bivariate copula”
Parameter estimation for pair-copula constructions (2013, background): “[6], Heinen and Valdesogo [19], Schirmacher and Schirmacher [35], Czado and Min [9], Kolbjørnsen and Stien [29], Czado et al.”