Title: Fast inference for high-dimensional one-factor copula models with additional Gaussian factors
Authors: Alex Verhoijsen - University of Melbourne (Australia) [presenting]
Pavel Krupskiy - University of Melbourne (Australia)
Abstract: Gaussian factor models allow the statistician to capture multivariate dependence between variables. However, they are computationally cumbersome in high dimensions and are not able to capture multivariate skewness in the data. We propose a copula model that allows for arbitrary margins, and multivariate skewness in the data by including a non-Gaussian factor whose dependence structure is the result of a one-factor copula model. Estimation is carried out using a two-step procedure: margins are modelled separately and transformed to the normal scale, after which the dependence structure is estimated. An estimation procedure is developed that allows for fast estimation of the model parameters in a high-dimensional setting. Theoretical results of the model with up to three Gaussian factors are proven, and simulation results confirm the results for increasing sample size and dimensionality. Finally, the model is applied to a selection of stocks of the SP500, demonstrating that the model can capture cross-sectional skewness in the stock market data.