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Vine copulas have proven to be flexible dependence models, which are able to model tail dependence pattern as they occur in financial and insurance data. The models power is driven by the ability to construct a d-dimensional dependence model from a collection of bivariate models through appropriate conditioning. This pair copula construction allows to work with high dimensional data. We will discuss the basic construction principle and give a review of its applications to finance. We highlight current advances in the area of quantile regression and two part modeling in insurance. More information about papers and software can be found at vine-copula.org.
 Aas, K., Czado, C., Frigessi, A., & Bakken, H. (2009). “Pair-copula constructions of multiple dependence.” Insurance: Mathematics and Economics, 44(2), 182-198.
 Aas, K. (2016). “Pair-copula constructions for financial applications: A review.” Econometrics, 4(4), 43.
 Kraus, D., & Czado, C. (2017). “D-vine copula based quantile regression." Computational Statistics & Data Analysis, 110, 1-18.
 Yang, L., & Czado, C. (2019). “Two-Part D-Vine Copula Models for Longitudinal Insurance Claim Data.” Submitted.