Parameter estimation for pair-copula constructions

By Ingrid Hobæk Haff. 2013

124 citations recorded by the source corpus.

We explore various estimators for the parameters of a pair-copula construction (PCC), among those the stepwise semiparametric (SSP) estimator, designed for this dependence structure. We present its asymptotic properties, as well as the estimation algorithm for the two most common types of PCCs. Compared to the considered alternatives, that is, maximum likelihood, inference functions for margins and semiparametric estimation, SSP is in general asymptotically less efficient. As we show in a few examples, this loss of efficiency may however be rather low. Furthermore, SSP is semiparametrically efficient for the Gaussian copula. More importantly, it is computationally tractable even in high dimensions, as opposed to its competitors. In any case, SSP may provide start values, required by the other estimators. It is also well suited for selecting the pair-copulae of a PCC for a given data set.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b5a1311399c3416f4ad264c7013a648113eacafb

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Citations in the corpus

Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy) (2017, unknown): “Parametric multivariate statistical models allow one to constrain the dependencies between the contributing variables of CEs, as well as their marginal distributions (e.g Hobæk Haff et al., 2015; Serinaldi, 2015; Aghakouchak et al., 2014; Saghaﬁan and Mehdikhani, 2013; Serinaldi et al., 2009; Shiau ”
Pair‐copula constructions for non‐Gaussian DAG models (2012, methodology): “approximating certain conditional cdfs by non-parametric estimates as suggested by Hobæk Haff (2010). Including this idea in the optimisation routines used in Section 5 cut down computation time by the factor 50. One way of dealing with the second problem above may be to consider sequential instead ”
Maximum likelihood estimation of mixed C-vines with application to exchange rates (2012, background): “They are asymptotically normal under regularity conditions as recently shown by Haff (2010).”
Selecting and estimating regular vine copulae and application to financial returns (2012, methodology): “These so-called sequential estimates can be used as starting values for determining the maximum likelihood estimates (see also Hobæk Haﬀ (2011) for more details on the asymptotic behavior of these estimates).”
Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine (2013, background): “More details on the estimation of vine copulas can found in Aas et al. (2009), Hobæk Haﬀ (2013) and Czado et al. (2012).”
Selection of Vine Copulas (2013, background): “For more details see [1] as well as [32] who investigates the asymptotic properties of this sequential approach.”
Risk management with high-dimensional vine copulas: An analysis of the Euro Stoxx 50 (2013, methodology): “The treewise selection and estimation procedure described here gives sequential estimates of pair copula parameters, which are quite quickly obtained and can be used as starting values for a full maximum likelihood estimation (see Aas et al. (2009) and Hobæk Haff (2013)).”
Copula-Based Regression Estimation and Inference (2013, background): “Hobæk Haff et al. (2010) discussed the conditions under which such a simplification is possible and found that this is not a severe restriction in many situations. For more about vines see the recent book by Kurowicka and Joe (2010). The problem of selecting an appropriate simplified decomposition a”