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Semiparametric Bayesian modeling of nonstationary joint extremes: How do big tech's extreme losses behave?

Lookup NU author(s): Dr Vianey Palacios RamirezORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

Motivated by the hype surrounding 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 EDI (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—sheds light on some interesting features on the dynamics of their combined losses over time.

Publication metadata

Author(s): de Carvalho M, Palacios Ramirez KV

Publication type: Article

Publication status: Published

Journal: Journal of the Royal Statistical Society Series C:Applied Statistics

Year: 2025

Volume: 74

Issue: 2

Pages: 447-465

Print publication date: 01/03/2025

Online publication date: 26/12/2024

Acceptance date: 27/10/2024

Date deposited: 16/04/2025

ISSN (print): 0035-9254

ISSN (electronic): 1467-9876

Publisher: Oxford University Press

URL: https://doi.org/10.1093/jrsssc/qlae062

DOI: 10.1093/jrsssc/qlae062

Data Access Statement: Data are publicly available from Yahoo Finance

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