• Bax, K., Sahin, Ö., Czado, C., and Paterlini, S. (2023)
  ESG, Risk, and (tail) dependence
  International Review of Financial Analysis
  [link]
• Czado, C., Bax, K., Sahin, Ö., Nagler, T., Min, A., and Paterlini, S. (2023)
  Vine copula based dependence modeling in sustainable finance
  The Journal of Finance and Data Science
  [link]
• Sahin, Ö. and Czado, C. (2022)
  High-dimensional sparse vine copula regression with application to genomic prediction
  [preprint]
• Sommer, E., Bax, K., and Czado, C. (2022)
  Vine Copula based portfolio level conditional risk measure forecasting
  [preprint]
• Ye, F., Ding, J., Chen, K. and Xi, X. (2022)
  Investigation of Corticomuscular Functional Coupling during Hand Movements Using Vine Copula
  Brain Sciences
  [link]
• Alnasser, H. and Czado, C. (2022)
  An Application of D-vine Regression for the Identification of Risky Flights in Runway Overrun
  [preprint]
• Tepegjozova, M. and Czado, C. (2022)
  Bivariate vine copula based quantile regression
  [preprint]

• Kreuzer, A., Dalla Valle, L., and Czado, C. (2022)
  A Bayesian Non-linear State Space Copula Model to Predict Air Pollution in Beijing
  Journal of the Royal Statistical Society: Series C (Applied Statistics)
  [link]

• Sahin, Ö. and Czado, C. (2022)
  Vine copula mixture models and clustering for non-Gaussian data
  Econometrics and Statistics
  [preprint | link ]

• Tepegjozova, M., Zhou, J., Claeskens, G. and Czado, C. (2022)
  Nonparametric C- and D-vine based quantile regression
  Dependence Modeling
  [preprint | link ]
• Nagler, T., Krüger, D. and Min, A. (2022)
  Stationary vine copula models for multivariate time series
  Journal of Econometrics
  [preprint, link]
• Yang, L. and Czado, C. (2022)
  Two‐Part D‐Vine Copula Models for Longitudinal Insurance Claim Data
  Scandinavian Journal of Statistics
  [link]
• Czado, C. and Nagler, T. (2022)
  Vine Copula Based Modeling
  Annual Review of Statistics and Its Application, 9, 453-477
  [link ]
• Czado, C. and Scharl, S. (2021)
  Analysis of an interventional protein experiment using a vine copula based structural equation model
  [preprint]
• Kielmann, J., Manner, H. and Min, A. (2021)
  Stock market returns and oil price shocks: A CoVaR analysis based on dynamic vine copula models
  [link]
• Coblenz, M. (2021)
  MATVines: A vine copula package for MATLAB
  [link]
• Deng, Y. and Chaganty, N.R. (2021)
  Pair-copula Models for Analyzing Family Data.
  J Stat Theory Pract 15, 13.
  [link][access]
• Chang, B. and Joe, H. (2020)
  Copula diagnostics for asymmetries and conditional dependence.
  Journal of Applied Statistics, 47(9), 1587-1616.
  [link] 
• Zhu, K., Kurowicka, D. and Nane, G. F. (2020)
  Common sampling orders of regular vines with application to model selection
  Computational Statistics & Data Analysis
  [link]

2023

• Bax, K., Sahin, Ö., Czado, C., and Paterlini, S. (2023)
  ESG, Risk, and (tail) dependence
  International Review of Financial Analysis
  [link]
• Czado, C., Bax, K., Sahin, Ö., Nagler, T., Min, A., and Paterlini, S. (2023)
  Vine copula based dependence modeling in sustainable finance
  The Journal of Finance and Data Science
  [link]

2022

• Sahin, Ö. and Czado, C. (2022)
  High-dimensional sparse vine copula regression with application to genomic prediction
  [preprint]

• Sommer, E., Bax, K., and Czado, C. (2022)
  Vine Copula based portfolio level conditional risk measure forecasting
  [preprint]

• Ye, F., Ding, J., Chen, K. and Xi, X. (2022)
  Investigation of Corticomuscular Functional Coupling during Hand Movements Using Vine Copula
  Brain Sciences
  [link]

• Alnasser, H. and Czado, C. (2022)
  An Application of D-vine Regression for the Identification of Risky Flights in Runway Overrun
  [preprint]

• Tepegjozova, M. and Czado, C. (2022)
  Bivariate vine copula based quantile regression
  [preprint]

• Kreuzer, A., Dalla Valle, L., and Czado, C. (2022)
  A Bayesian Non-linear State Space Copula Model to Predict Air Pollution in Beijing
  Journal of the Royal Statistical Society: Series C (Applied Statistics)
  [link]

• Sahin, Ö. and Czado, C. (2022)
  Vine copula mixture models and clustering for non-Gaussian data
  Econometics and Statistics 
  [preprint | link]

• Tepegjozova, M., Zhou, J., Claeskens, G. and Czado, C. (2022)
  Nonparametric C- and D-vine based quantile regression
  Dependence Modeling
  [preprint | link ]

• Nagler, T., Krüger, D. and Min, A. (2022)
  Stationary vine copula models for multivariate time series
  Journal of Econometrics
  [preprint, link]

• Yang, L. and Czado, C. (2022)
  Two‐Part D‐Vine Copula Models for Longitudinal Insurance Claim Data
  Scandinavian Journal of Statistics
  [link]

• Czado, C. and Nagler, T. (2022)
  Vine Copula Based Modeling
  Annual Review of Statistics and Its Application, 9, 453-477
  [link ]

2021

• Czado, C. and Scharl, S. (2021)
  Analysis of an interventional protein experiment using a vine copula based structural equation model
  [preprint]
• Sahin, Ö., Bax, K., Paterlini, S. and Czado, C. (2021)
  ESGM: ESG scores and the Missing pillar
  [Preprint]
• Kielmann, J., Manner, H. and Min, A. (2021)
  Stock market returns and oil price shocks: A CoVaR analysis based on dynamic vine copula models
  [link]
• Bax, K., Sahin, Ö., Czado, C. and Paterlini, S. (2021)
  ESG, Risk, and (tail) dependence
  [preprint]
• Coblenz, M. (2021)
  MATVines: A vine copula package for MATLAB
  [link]
• Deng, Y. and Chaganty, N.R. (2021)
  Pair-copula Models for Analyzing Family Data.
  J Stat Theory Pract 15, 13.
  [link][access]

2020

• Chang, B. and Joe, H. (2020)
  Copula diagnostics for asymmetries and conditional dependence.
  Journal of Applied Statistics, 47(9), 1587-1616.
  [link] 
• Zhu, K., Kurowicka, D. and Nane, G. F. (2020)
  Common sampling orders of regular vines with application to model selection
  Computational Statistics & Data Analysis
  [link]

2019

• Chang, B., Pan, S. and Joe, H. (2019)
  Vine copula structure learning via Monte Carlo tree search
  Proceedings of Machine Learning Research
  [link]
• Kreuzer, A. and Czado, C. (2019)
  Bayesian inference for dynamic vine copulas in higher dimensions
  [preprint]
• Kreuzer, A. and Czado, C. (2019)
  Efficient Bayesian inference for univariate and multivariate non linear state space models with univariate autoregressive state equation
  [preprint]
• Kreuzer, A., Dalla Valle, L. and Czado, C. (2019)
  Bayesian Multivariate Nonlinear State Space Copula Models
  [preprint]
• Torre, E., Marelli, S., Embrechts, P. and Sudret, B. (2019)
  A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas
  Probabilistic Engineering Mechanics
  [link]

2018

• Nagler, T., Bumann, C., Czado, C. (2018)
  Model selection in sparse high-dimensional vine copula models with application to portfolio risk.
  [preprint]
• Vatter, T. and Nagler, T. (2018)
  Generalized additive models for pair-copula constructions.
  Journal of Computational and Graphical Statistics, to appear
  [preprint]
• Kreuzer A. and Czado C. (2018)
  Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo
  [preprint]
• Barthel, N., Czado, C., and Okhrin, Y. (2018)
  A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series
  [preprint]
• Stübinger, J., Mangold, B., Krauss, C. (2018)
  Statistical arbitrage with vine copulas
  Quantitative Finance (published online) [link] [preprint]
• Wilson, K. J. (2018)
  Specification of Informative Prior Distributions for Multinomial Models Using Vine Copulas
  Bayesian Analysis [link]
• Barthel, N., Geerdens. C., Czado, C., and Janssen, P. (2018)
  Dependence modeling for recurrent event times subject to right-censoring with D-vine copulas
  [preprint]

2017

• Barthel, N., Geerdens, C., Killiches, M., Janssen, P. and Czado, C. (2017)
  Vine copula based likelihood estimation of dependence patterns in multivariate event time data.
  Computational Statistics & Data Analysis, doi.org/10.1016/j.csda.2017.07.010.
  [link]
• Schamberger, B., Gruber, L. and C. Czado (2017)
  Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting
  Econometrics 2017, 5, 21; doi:10.3390/econometrics5020021
  [pdf]
• Kurz, M. and Spanhel, F. (2017)
  Testing the simplifying assumption in high-dimensional vine copulas
  submitted
  [preprint]
• Müller, D. and Czado, C. (2017)
  Dependence Modeling in Ultra High Dimensions with Vine Copulas and the Graphical Lasso
  submitted for publication
  [preprint]
• Kraus, D. and Czado, C. (2017)
  Growing simplified vine copula trees: improving Dißmann's algorithm
  submitted for publication
  [preprint]
• Killiches, M., Kraus, D. and Czado, C. (2017)
  Model distances for vine copulas in high dimensions.
  Statistics and Computing (doi:10.1007/s11222-017-9733-y)
  [preprint]
• Müller, D. and Czado, C. (2017)
  Selection of Sparse Vine Copulas in High Dimensions with the Lasso
  submitted for publication
  [preprint]
• Panagiotelis, A., Czado, C. Joe, H. and Stöber, J. (2017)
  Model selection for discrete regular vine copulas.
  Computational Statistics & Data Analysis, Volume 106, Pages 138–152
  [preprint]
• Christian Schellhase, Fabian Spanhel (2017)
  Estimating Non-Simplified Vine Copulas Using Penalized Splines.
  Statistics & Computing (doi: 10.1007/s11222-017-9737-7)
  [preprint]
• Müller, D. and Czado, C. (2017)
  Representing sparse Gaussian DAGs as sparse R-vines allowing for non-Gaussian dependence
  to appear in the Journal of Computational and Graphical Statistics (doi:10.1080/10618600.2017.1366911)
  [link]
• Fink, H., Y. Klimova, C. Czado J. and Stöber (2017)
  Regime switching vine copula models for global equity and volatility indices
  Econometrics 2017, 5, 3; doi:10.3390/econometrics5010003
  [link][preprint]
• Pereira, G., Veiga, A., Erhardt, T. M., and C. Czado (2017)
  A periodic spatial vine copula model for multi-site streamflow simulation.
  Electric Power Systems Research, 152, 9-17
  [link]
• Ivanov, E., Min, A. and F. Ramsauer (2017)
  Copula-Based Factor Models for Multivariate Asset Returns
  Econometrics 2017, 5, 20; doi:10.3390/econometrics5020020
  [pdf]
• M. Fischer, D. Kraus, M. Pfeuffer and C. Czado (2017)
  Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression
  Risks 2017, 5(3), 38.
  [preprint]

2016

• Vatter, T. and Nagler, T. (2016)
  Generalized additive models for pair-copula constructions
  Journal of Computational and Graphical Statistics, to appear
  [preprint]
• Killiches, M., Kraus, D. and Czado, C. (2016)
  Examination and visualization of the simplifying assumption for vine copulas in three dimensions
  Australian and New Zealand Journal of Statistics
  [preprint]
• Killiches, M., Kraus, D. and Czado, C. (2016)
  Using model distances to investigate the simplifying assumption, goodness-of-fit and truncation levels for vine copulas.
  submitted for publication
  [preprint]
• Prokhorov, A., Schepsmeier, U. and Zhu, Y. (2016)
  Generalized Information Matrix Tests for Copulas.
  under review in Journal of Multivariate Analysis
• Schepsmeier, U. (2016)
  A goodness-of-fit test for regular vine copula models.
  Econometric Reviews (2016): 1-22.
  [pdf]
• Nagler T., Schellhase, C. and Czado, C. (2016)
  Nonparametric estimation of simplified vine copula models: comparison of methods
  Submitted
  [preprint]
• Nagler, T. and Czado, C. (2016)
  Evading the curse of dimensionality in multivariate kernel density estimation with simplified vines.
  Journal of Multivariate Analysis 151, 69-89 (doi:10.1016/j.jmva.2016.07.003)
  [preprint]
• Almeida, C., C. Czado and H. Manner (2016)
  Modeling high-dimensional time-varying dependence using dynamic D-vine models
  Applied Stochastic Models in Business and Industry 2016.
  [link], [preprint]
• Aas, K. (2016)
  Pair-Copula Constructions for Financial Applications: A Review.
  Econometrics 2016, 4, 43.
  [link]

2015

• Sriboonchitta, S., Kosheleva, O. and Nguyen, H. T. (2015)
  Why Are Vine Copulas So Successful in Econometrics?
  [pdf]
• Maya, R. A. L., Gomez-Gonzales, J. E. and Velandia, L. F. M. (2015)
  Latin American Exchange Rate Dependencies: A Regular Vine Copula Approach
  Contemporary Economic Policy, Volume 33, Issue 3, 535–549.
  [link]
• Cooke, R. M., Kurowicka, D., & Wilson, K. (2015)
  Sampling, Conditionalizing, Counting, Merging, Searching Regular Vines
  Journal of Multivariate Analysis 138, 4-18.
  [link]
• Spanhel, F. and Kurz, M. (2015)
  Simplified vine copula models: Approximations based on the simplifying assumption
  [preprint]
• Schepsmeier, U. (2015)
  Efficient information based goodness-of-fit tests for vine copula models with fixed margins.
  Journal of Multivariate Analysis 138, 34-52.
  (formerly: Efficient goodness-of-fit tests in multi-dimensional vine copula models)
  [pdf]
• Brechmann, E. and Joe, H. (2015)
  Truncation of vine copulas using fit indices.
  Journal of Multivariate Analysis 138, 19-33.
• Gruber, L. and Czado, C. (2015)
  Sequential Bayesian Model Selection of Regular Vine Copulas.
  Bayesian Anal. Volume 10, Number 4 (2015), 937-963.
  [preprint]
• Stöber, J., Hong, H., Czado, C., Ghosh, P (2015)
  Comorbidity of chronic diseases in the elderly: longitudinal patterns identified by a copula design for mixed responses.
  Computational Statistics & Data Analysis, Volume 88, Pages 28–39.
  [preprint]
• Hobaek Haff, I. and Segers, J. (2015)
  Nonparametric estimation of pair-copula constructions with the empirical pair-copula.
  Computational Statistics & Data Analysis, Volume 84, Pages 1–13.
  [pdf]
• Bauer, A. and C. Czado (2015)
  Pair-copula Bayesian networks
  Journal of Computational and Graphical Statistics
  [link]
• Jaworski, P. (2015)
  Univariate conditioning of vine copulas.
  Journal of Multivariate Analysis, 138, 89-103.
  [link]
• Smith, M. and Vahey, S. (2015)
  Asymmetric Density Forcasting of U.S. Macroeconomic Variables using a Gaussian Copula Model of Cross-Sectional and Serial Dependence.
  [preprint]
• Erhardt, T. M. and Czado, C. (2015)
  Standardized drought indices: A novel uni- and multivariate approach
  [preprint]
• Erhardt, T. M., Czado, C. and Schepsmeier, U. (2015)
  Spatial composite likelihood inference using local C-vines.
  Journal of Multivariate Analysis 138, 74-88
  [preprint][link]
• Erhardt, T. M., Czado, C. and Schepsmeier, U. (2015)
  R-vine Models for Spatial Time Series with an Application to Daily Mean Temperature.
  Biometrics 71, 323-332
  [preprint][link]
• Killiches, M. and Czado, C. (2015)
  Block-Maxima of Vines.
  To appear in Extreme Value Modelling and Risk Analysis: Methods and Applications. Eds. D. Dey and J. Yan. Chapman & Hall/CRC Press.
  [preprint]
• Krupskii, P., & Joe, H. (2015).
  Structured factor copula models: Theory, inference and computation.
  Journal of Multivariate Analysis 138, 53-73.
  [link]

2014

• E.C. Brechmann and H. Joe (2014).
  Parsimonious Parameterization of Correlation Matrices Using Truncated Vines and Factor Analysis.
  Computational Statistics & Data Analysis, Volume 77, Pages 233–251. 
  [preprint] [link]
• Schepsmeier, U. and J. Stöber (2014)
  Derivatives and Fisher information of bivariate copulas.
  Statistical Papers, 55(2), 525-542.
  online first: http://link.springer.com/article/10.1007/s00362-013-0498-x.
  [pdf]
  Web supplement: Derivatives and Fisher Information of bivariate copulas.
  [pdf]
• Stöber, J. and C. Czado (2014),
  Detecting regime switches in the dependence structure of high dimensional financial data.
  Computational Statistics & Data Analysis, 76, 672-686.
  [link]
• Min, A. and Czado, C. (2014),
  SCOMDY models based on pair-copula constructions with application to exchange rates.
  Computational Statistics and Data Analysis, 76, 523-535.
  [link]
• Smith, M. (2014)
  Copula Modelling of Dependence in Multivariate Time Series.
  International Journal of Forecasting, Volume 31, Issue 3, Pages 815–833
  [link] [preprint]
• Brechmann, E.C. and Czado, C. (2014),
  COPAR - Multivariate Time Series Modeling Using the COPula AutoRegressive Model.
  Applied Stochastic Models in Business and Industry, Volume 31, Issue 4, Pages 495–514.
  [link]
• Gräler, B. (2014)
  Modelling Skewed Spatial Random Fields through the Spatial Vine Copula.
  Spatial Statistics 10, 87 - 102.
  [link]
• E.C. Brechmann, C. Czado and S. Paterlini (2014),
  Flexible Dependence Modeling of Operational Risk Losses and Its Impact on Total Capital Requirements.
  Journal of Banking & Finance, 40, 271-285.
  [pdf][link]
• E.C. Brechmann (2014)
  Hierarchical Kendall Copulas: Properties and Inference.
  Canadian Journal of Statistics, 42(1), 78-108.
  [link]

2013

• E.C. Brechmann, C. Czado and S. Paterlini (2013),
  Modeling Dependence of Operational Loss Frequencies.
  Journal of Operational Risk, 8(4), 105-126.
  [link]
• Brechmann, E.C., K. Hendrich and C. Czado (2013)
  Conditional copula simulation for systemic risk stress testing.
  Insurance: Mathematics and Economics, 53, 722-732
  [pdf][link]
• Brechmann, E.C. and C. Czado (2013),
  Risk Management with High-Dimensional Vine Copulas: An Analysis of the Euro Stoxx 50.
  Statistics & Risk Modeling, 30(4), 307-342.
  [pdf]
• Krupskii, P. and Joe, H. (2013).
  Factor copula models for multivariate data,
  Journal of Multivariate Analysis, 120, 85-101.
  [pdf]
• Krämer, N, Brechmann, E.C., Silvestrini, D. and Czado, C. (2013),
  Total loss estimation using copula-based regression models
  Insurance: Mathematics and Economics, 53, 829-839
  [preprint][link]
• Nikoloulopoulos, A. and Joe, H. (2013).
  Factor copula models for item response data.
  Psychometrika, 1-25.
  [link]
• E.C. Brechmann (2013)
  Sampling from Hierarchical Kendall Copulas.
  Journal de la Société Française de Statistique, 154(1), 192-209.
  [link]
• Stöber, J.,  H. Joe and C. Czado (2013),
  Simplified Pair Copula Constructions - Limitations and Extensions
  Journal of Multivariate Analysis, 119, 101-118
  [pdf]
• Lopez-Paz D, Hernandez-Lobato JM and Ghahramani Z (2013)
  Gaussian Process Vine Copulas for Multivariate Dependence
  In: JMLR W&CP 28(2): Proceedings of The 30th International Conference on Machine Learning, (Ed) S Dasgupta and D McAllester, 30th International Conference on Machine Learning (ICML 2013), JMLR, 10-18.
  [pdf]
• Dißmann, J., Brechmann, E.C., Czado, C., and Kurowicka, D.  (2013)
  Selecting and estimating regular vine copulae and application to financial returns.
  Computational Statistics and Data Analysis, 59, 52-69
  [pdf]
• Brechmann, E.C., Czado, C. and Aas, K. (2012)
  Truncated regular vines in high dimensions with applications to financial data.
  Canadian Journal of Statistics, 40 (1), 68-85.
  [pdf]
• Czado, C., Jeske, S., & Hofmann, M. (2013).
  Selection strategies for regular vine copulae.
  Journal de la Société Française de Statistique, 154(1), 174-191.
  [pdf]
• Stöber, J. and U. Schepsmeier (2013)
  Estimating standard errors in regular vine copula models
  Computational Statistics, 28 (6), 2679-2707
  [link]
• E.C. Brechmann and U. Schepsmeier (2013),
  Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine.
  Journal of Statistical Software, 52(3), 1-27.
  [pdf]
• Haff, I. H. (2013).
  Parameter estimation for pair copula construction.
  Bernoulli Journal, 19, 462-491 
  [pdf]
• Kauermann, G. and Schellhase, C. (2013)
  Flexible pair-copula estimation in D-vines with Penalized Splines.
  Statistics and Computing, Volume 24(6), Pages 1081-1100 (doi: 10.1007/s11222-013-9421-5)
  [link]
• Schellhase, C. and Kauermann, G. (2013)
  Flexible pair-copula estimation in R-vines for portfolio optimization.
  working paper
• Schmidl, D., Czado, C., Hug, S., and Theis, F. J. (2013)
  A vine-copula based adaptive MCMC sampler for efficient inference of dynamical systems.
  Bayesian Analysis, 8(1), 1-22

2012

• Acar, E., Genest, C. and Nešlehová, J. (2012)
  Beyond simplified pair-copula constructions
  Journal of Multivariate Analysis, 110, 74-90.
  [link]
• Czado, C., Schepsmeier, U., Min, A. (2012)
  Maximum likelihood estimation of mixed C-vines with application to exchange rates
  Statistical Modelling, 12 (3), 229-255
  [pdf]
• Panagiotelis, A., Czado, C. and Joe, H. (2012)
  Pair copula constructions for multivariate discrete data. 
  J Amer Stat Assoc, 107:499, 1063-1072
  [link] [pdf]
• Bauer, A., Czado, C. and Klein, T. (2012)
  Pair-copula constructions for non-Gaussian DAG models.
  Canadian Journal of Statistics, 40 (1), 86-109
  [pdf]
• Nikoloulopoulos, A.K., Joe, H., and Li, H.  (2012)
  Vine copulas with asymmetric tail dependence and applications to financial return data.
  Computational Statistics and Data Analysis, 56 (11), 3659-3673
  [pdf]
• Gräler, B. and Pebesma, E. (2012)
  Modelling Dependence in Space and Time with Vine Copulas.
  Presentation at: Geostats 2012, Oslo, Norway, 11-15 June 2012
  [link]
• Erhardt, V. and Czado, C. (2012)
  Modeling dependent yearly claim totals including zero claims in private health insurance.
  Scandinavian Actuarial Journal, 2, 106-129
  [link]
• Czado, C., Gärtner, F. and Min, A. (2011)
  Analysis of Australian electricity loads using joint Bayesian inference of D-Vines with autoregressive margins
  (Handbook on Vines, Editors: Dorota Kurowicka and Harry Joe, World Scientific)

2011

• Czado, C. and Min, A. (2011),
  Bayesian Inference for D-vines: Estimation and Model Selection
  Handbook on Vines, Editors: Dorota Kurowicka and Harry Joe, World Scientific.
• Gräler, B. and Pebesma, E. (2011)
  The pair-copula construction for spatial data: a new approach to model spatial dependency.
  Procedia Environmental Sciences 7, 206-211.
  [pdf]

2010

• Haff, I. H., K. Aas, and A. Frigessi (2010).
  On the simplified pair-copula construction – simply useful or too simplistic?
  Journal of Multivariate Analysis 101(5), 1296–1310.
  [pdf]
• Min, A. and C. Czado (2010).
  Bayesian model selection for multivariate copulas using pair-copula constructions.
  Journal of Financial Econometrics 8 (4), 511–546.
  [pdf]
• Hofmann, M. and Czado, C. (2010),
  Assessing the VaR of a portfolio using D-vine copula based multivariate GARCH models
  Submitted for publication.
  [pdf]
• Min, A. and C. Czado (2010).
  Bayesian model selection for D-vine pair-copula constructions.
  Canadian Journal of Statistics 39 (2), 239–258.
  [pdf]
• Smith, M., A. Min, C. Almeida, and C. Czado (2010).
  Modeling longitudinal data using a pair-copula construction decomposition of serial dependence.
  Journal of the American Statistical Association 105, 1467–1479.
  [pdf]
• Joe, H., Li, H. and Nikoloulopoulos, A.K.  (2010)
  Tail dependence functions and vine copulas.
  Journal of Multivariate Analysis, 101, 252-270.
  [pdf]