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In this talk we introduce several goodness-of-fit(GOF)tests for regular vine(R-vine)copula models,a flexible class of multivariate copulas based on a pair-copula construction(PCC).In particular we investigate two new goodness-of-fit tests arising from the information matrix and specification test proposed by White(1982)and the information ratio test by Zhang et al.(2013).The test statistics are derived and their asymptotic distribution proven.Further 13 GOF tests are adapted from the bivariate case and compared in an extensive power study,which shows the superiority of the information matrix based tests.The bootstrapped simulation based tests show excellent performance with respect to size and power,while the asymptotic theory based tests are inaccurate in higher dimensions.The best performing GOF tests are applied to a portfolio of stock indices and their related volatility indices validating different R-vine specifications.