06 - Withdrawn - Common factors in large panels of option prices

We propose a new factor model for multivariate tensor-valued data suitable to describe the join dynamics of a cross-section of option prices of over 200 equities of the S&P 500 Index. The factors explain the common variation of all the options in the cross-section, and we model their dynamics in a standard time series context. In contrast, the factor loadings express the heterogeneous response to a common shock and are two-dimensional arrays (i.e., tensors). We propose an inference framework to test the significance of the loadings. Furthermore, we implement a tensor-counterpart version of the multivariate principal component model to deal with the high-parametrization of the factor loadings, which enables us to extract trading signals, which we use to design a dynamic trading strategy. Our results show that this strategy yields significantly higher profits than a mean-variance investment strategy, even when controlling transaction costs.

factor models

tensors

finance

multivariate pca 

Expert

Knowledge

Women in Statistics and Data Science 2022