Beyond Correlation: Extreme Co-movements Between Financial Assets

By Assaf J. Zeevi, R. Mashal. 2002

258 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/329da7d9b7e13926c6c4ff503434a9dc29955e16

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Citations in the corpus

Pair-copula constructions of multiple dependence (2009, methodology): “Our proposal extends the method of maximum pseudo-likelihood originally proposed for copulae by Oakes (1994), and later shown to be asymptotically normal and consistent both by Genest et al.”
Dynamic Copula Modelling for Value at Risk (2006, result): “This evidence is consistent with other results reported in Marshal and Zeevi (2002) and Chen, Fan, and Patton (2004) that found the normal copula to be a plausible choice when dealing with bivariate portfolios, while this is not the case for larger collections of financial assets.”
Copula-based multivariate GARCH model with uncorrelated dependent errors (2009, methodology): “However, these models have been estimated under the conditional multivariate normality assumption, while this assumption has been rejected in much of the empirical findings — Fama and French (1993), Richardson and Smith (1993), Longin and Solnik (2001),Mashal and Zeevi (2002), among many others.”
Application of copulas for derivation of drought severity–duration–frequency curves (2012, methodology): “The parameter θ can be obtained by maximizing this rank-based pseudo log-likelihood function numerically, ̂ ¼ argmax lnLU θð Þ½ (17) For Student’s t copula, a two-step estimation procedure is employed (Mashal and Zeevi, 2002).”
Probabilistic assessment of flood risks using trivariate copulas (2012, methodology): “9, the correlation matrix of Student’s t copula is estimated and then a numerical search technique is employed for estimating b θ (Mashal and Zeevi 2002).”
Dependence structure between the equity market and the foreign exchange market–A copula approach (2010, background): “The tail dependence coe cients can be written as l = 0; r = 2 21= : (14) Clayton copula The Clayton copula is de ned as: C (u; v) = (u + v 1)1= for > 0 (15) where is the associate parameter.”
Modelling the dependence structure of financial assets : A survey of four copulas (2004, unknown): “According to Mashal and Zeevi (2002), this procedure is computationally intensive and suffers from numerical stability problems arising from the inversion of close to singular matrices.”
Copula goodness-of-fit testing: an overview and power comparison (2009, methodology): “We follow Mashal and Zeevi (2002) and Demarta and McNeil (2005), who propose a two-stage approach in which the scale matrix is first estimated by inversion of Kendall's tau, and then the pseudo-likelihood function is maximized with respect to the degree-of-freedom ν, using the estimate of the scale…”
The Joint Distribution of Stock Returns is Not Elliptical (2010, unknown): “There are however many situations of practical interest where the (linear) correlation matrix is inadequate or insufficient [1,30,33].”
KENDALL'S TAU FOR ELLIPTICAL DISTRIBUTIONS ∗ (2003, background): “returns; see [ 9 ] for practical applications of this approach.”
The t Copula and Related Copulas (2005, methodology): “5 Generalizations of t Copula Via Mixture Constructions The t copula has been found in empirical studies, such as those of Mashal & Zeevi (2002) and Breymann et al. (2003), to be a better model than the Gauss copula for the dependence structure of multivariate financial returns, which often seem to…”
Dependence patterns across financial markets: a mixed copula approach (2006, background): “Among the empirical studies, Longin and Solnik (2001) uses a Gumbel copula to estimate the extreme correlations across international equity markets; Mashal and Zeevi (2002) estimates the degree of freedom in a t-copula and use it to test the traditional Gaussian assumption in financial markets.”