Semiparametric Bayesian modelling of nonstationary joint extremes: How do big tech’s extreme losses behave?
受人工智能和大型科技股热潮启发，提出一种贝叶斯模型来追踪多个科技股联合极端损失随时间的变化，并判断它们是否渐近相关。
Abstract Motivated by the hype surrounding Artificial Intelligence (AI) and big tech stocks, we develop a model for tracking the dynamics of their combined extreme losses over time. Specifically, we propose a novel Bayesian model for inferring about the intensity of observations in the joint tail over time, and for assessing if two stochastic processes are asymptotically dependent. To model the intensity of observations exceeding a high threshold, we develop a Bayesian nonparametric approach that defines a prior on the space of what we define as Extremal Dependence Intensity functions. In addition, a parametric prior is set on the coefficient of tail dependence. An extensive battery of experiments on simulated data show that the proposed method are able to recover the true targets in a variety of scenarios. An application of the proposed methodology to a set of big tech stocks—known as FAANG (Meta’s Facebook, Apple, Amazon, Netflix and Alphabet’s Google)—sheds light on some interesting features on the dynamics of their combined losses over time.