Transformed-Linear Models for Time Series Extremes

Abstract

To capture the dependence in the upper tail of a time series, we develop non-negative regularly varying time series models that are constructed similarly to classical non-extreme ARMA models. Rather than fully characterizing tail dependence of the time series, we define the concept of weak tail stationarity which allows us to describe a regularly varying time series via a measure of pairwise extremal dependencies, the tail pairwise dependence function (TPDF). We state consistency requirements among the finite-dimensional collections of the elements of a regularly varying time series and show that the TPDF's value does not depend on the dimension of the random vector being considered. So that our models take non-negative values, we use transformed-linear operations. We show existence and stationarity of these models, and develop their properties such as the model TPDFs. We fit models to hourly windspeed and daily fire weather index data, and we find that the fitted transformed-linear models produce better estimates of upper tail quantities than a traditional ARMA model, classical linear regularly varying models, a max-ARMA model, and a Markov model.