Tails of correlation mixtures of elliptical copulas
By H. Manner, Johan Segers. 2009
59 citations recorded by the source corpus.
Correlation mixtures of elliptical copulas arise when the correlation parameter is driven itself by a latent random process. For such copulas, both penultimate and asymptotic tail dependence are much larger than for ordinary elliptical copulas with the same unconditional correlation. Furthermore, for Gaussian and Student t-copulas, tail dependence at sub-asymptotic levels is generally larger than in the limit, which can have serious consequences for estimation and evaluation of extreme risk. Finally, although correlation mixtures of Gaussian copulas inherit the property of asymptotic independence, at the same time they fall in the newly defined category of near asymptotic dependence. The consequences of these findings for modeling are assessed by means of a simulation study and a case study involving financial time series.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/3c829673ae2e0755c3bc81a026f658c5fe14e8ca
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In Economics and Econometrics
- D. Oh
- Andrew J. Patton
- S. J. Koopman
- André Lucas
- Tim Bollerslev
- Kevin Sheppard
- Brendan K. Beare
- Gregor Kastner
In Finance
- D. Oh
- Andrew J. Patton
- S. J. Koopman
- André Lucas
- Tim Bollerslev
- Kevin Sheppard
- Brendan K. Beare
- Gregor Kastner
Citations in the corpus
- Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads (2018, background): “Some specifications, such as stochastic volatility models (see Shephard 2005) and related stochastic copula models (see Manner and Segers 2011; Hafner and Manner 2012) allow the varying parameters to evolve as a latent time series process.”
- Copula Methods for Forecasting Multivariate Time Series (2013, background): “Thus time-varying dependence may explain some (but not all) of the tail dependence estimated via the constant Student s t copula, see Manner and Segers (2011) on stochastic copulas and tail dependence.”
- A review of copula models for economic time series (2012, unknown): “inManner and Segers [108].”
- The Joint Distribution of Stock Returns is Not Elliptical (2010, unknown): “when p→ 1 is approximated by the following expansion in (rational) powers of (1−p): τ UU(p) = τ∗ (ν,ρ)+β(ν,ρ) · (1−p) 2 ν +O (1 −p)4ν (3.5) f This result was simultaneously found by Manner and Segers [31] (in a somewhat more general context). We still sketch our proof because it follows a different r”