Maximum likelihood estimation of mixed C-vines with application to exchange rates
From MaRDI portal
Publication:4970956
DOI10.1177/1471082X1101200302OpenAlexW2320148535MaRDI QIDQ4970956
Claudia Czado, Aleksey Min, Ulf Schepsmeier
Publication date: 8 October 2020
Published in: Statistical Modelling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1177/1471082x1101200302
Related Items
A multivariate volatility vine copula model, Regime switches in the dependence structure of multidimensional financial data, Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables, The general tail dependence function in the Marshall-Olkin and other parametric copula models with an application to financial time series, The shifting dependence dynamics between the G7 stock markets, A mixed C-vine copula model for hedging price and volumetric risk in wind power trading, Structure learning in Bayesian networks using regular vines, On the quantification and efficient propagation of imprecise probabilities with copula dependence, A corrected Clarke test for model selection and beyond, A new class of copulas involved geometric distribution: estimation and applications, Unnamed Item, Analysis of long-term natural gas contracts with vine copulas in optimization portfolio problems, A closed-form universal trivariate pair-copula, Multivariate dependent interval finite element analysis via convex hull pair constructions and the extended transformation method, Copula directed acyclic graphs, Model selection in sparse high-dimensional vine copula models with an application to portfolio risk, Selecting and estimating regular vine copulae and application to financial returns, Estimating standard errors in regular vine copula models, Conditional copula simulation for systemic risk stress testing, pyvine: the Python package for regular vine copula modeling, sampling and testing, Modelling mortality dependence: an application of dynamic vine copula, Counterdiagonal/nonpositive tail dependence in vine copula constructions: application to portfolio management, Selection of Vine Copulas, Vine copula graphical models in the construction of biological networks, A goodness-of-fit test for regular vine copula models, A hierarchical copula-based world-wide valuation of sovereign risk, Efficient information based goodness-of-fit tests for vine copula models with fixed margins: a comprehensive review, Spatial composite likelihood inference using local C-vines, Conditional quantiles and tail dependence, Nonparametric estimation of general multivariate tail dependence and applications to financial time series
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Pair-copula constructions of multiple dependence
- Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
- Goodness-of-fit tests for copulas: A review and a power study
- Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification
- An introduction to copulas.
- On the simplified pair-copula construction -- simply useful or too simplistic?
- Estimating the dimension of a model
- Elliptical copulas: Applicability and limitations.
- Generalized autoregressive conditional heteroscedasticity
- Vines -- a new graphical model for dependent random variables.
- Vine copulas with asymmetric tail dependence and applications to financial return data
- Parameter estimation for pair-copula constructions
- Probability density decomposition for conditionally dependent random variables modeled by vines
- Asymptotic efficiency of the two-stage estimation method for copula-based models
- Statistical Inference Procedures for Bivariate Archimedean Copulas
- Ordinal Measures of Association
- Uncertainty Analysis with High Dimensional Dependence Modelling
- Goodness-of-fit Procedures for Copula Models Based on the Probability Integral Transformation
- On a measure of lack of fit in time series models
- A semiparametric estimation procedure of dependence parameters in multivariate families of distributions
- Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence
- A NEW MEASURE OF RANK CORRELATION
- A new look at the statistical model identification
