Modelling non-stationarity in asymptotically independent extremes

TL;DR

This paper introduces a semi-parametric framework for modeling non-stationary extremal dependence in environmental data, especially capturing trends in asymptotically independent extremes for improved risk assessment.

Contribution

It presents a novel method for modeling non-stationary extremal dependence that works with asymptotic independence, addressing a gap in existing approaches.

Findings

Detected significant dependence trends in climate data.

Enabled estimation of future bivariate risk measures.

Applicable to data with complex non-stationary dependence structures.

Abstract

In many practical applications, evaluating the joint impact of combinations of environmental variables is important for risk management and structural design analysis. When such variables are considered simultaneously, non-stationarity can exist within both the marginal distributions and dependence structure, resulting in complex data structures. In the context of extremes, few methods have been proposed for modelling trends in extremal dependence, even though capturing this feature is important for quantifying joint impact. Moreover, most proposed techniques are only applicable to data structures exhibiting asymptotic dependence. Motivated by observed dependence trends of data from the UK Climate Projections, we propose a novel semi-parametric modelling framework for bivariate extremal dependence structures. This framework allows us to capture a wide variety of dependence trends for data exhibiting asymptotic independence. When applied to the climate projection dataset, our model detects significant dependence trends in observations and, in combination with models for marginal non-stationarity, can be used to produce estimates of bivariate risk measures at future time points.