Family of Flexible Multivariate Distributions with Applications in Empirical Finance |
| B. Mazur, M. Pipień
Cracow University of Economics, Rakowicka 27, PL 31517 Kraków |
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| We develop a class of parametric distributions that are capable of accounting for non-standard empirical properties that are evident in some financial time series. We aim at creating a parametric framework that allows for serious divergences from the multivariate Gaussian case both in terms of properties of marginal distributions and in terms of the dependence pattern. We are particularly interested in obtaining a multivariate construct that allows for considerable degree of heterogeneity in marginal properties of its components (like tail thickness and asymmetry). Moreover, we consider non-standard dependence patterns that go beyond a linear correlation-type relationship while maintaining simplicity, obtained by introducing rotations. We make use of marginal distributions that belong to generalized asymmetric Student-t class analysed in [A. Harvey, R.J. Lange, J. Time Ser. Anal. 38, 175 (2017)], allowing not only for skewness but also for asymmetric tail thickness. We illustrate flexibility of the resulting bivariate distribution and investigate its empirical performance examining unconditional properties of bivariate daily financial series representing dynamics of stock price indices and the related FUTURES contracts, as well as analysing unconditional co-dependence between daily returns on DAX and FTSE indices. |
DOI:10.12693/APhysPolA.138.65 topics: Bayesian inference, generalized asymmetric t-distribution, skewness, orthogonal matrices, rotations |