In this work we present a new method for Quickest Change Detection (QCD) and Quickest Hub Discovery (QHD) in correlation structures that geometrically represents solutions from asymptotic Random Matrix theory (RMT) consistently, and allows to incorporate new distance metrics for both tests. We...

We develop estimation methods for Bayesian models where the covariance matrix has a block structure. A block covariance matrix partitions the data into groups or blocks; variances and covariances are equal within blocks while covariances are equal across blocks. We derive the posterior and...

We investigate the performance of non-linear return prediction models in the high complexity regime, i.e., when the number of model parameters exceeds the number of observations. We document a "virtue of complexity" in all asset classes that we study (US equities, international equities, bonds,...

The generalized asymmetric Laplace (GAL) distribution, also known as the variance/mean-gamma model, is a popular flexible class of distributions that can account for peakedness, skewness, and heavier than normal tails, often observed in financial or other empirical data. We consider extensions...

There is considerable literature on matrix-variate gamma distributions, also known as Wishart distributions, which are driven by a shape parameter with values in the (Gindikin) set {i/2, i = 1, . . . , k−1}∪((k−1)/2, ∞). We provide an extension of this class to the case where the shape...

Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex...