Risk assessment using suprema data

TL;DR

This paper introduces a stochastic modeling approach using supremum data to estimate temperature dynamics and risk measures like heat wave probability, with theoretical validation and practical application on real weather data.

Contribution

It develops a method to estimate Ornstein-Uhlenbeck process parameters from supremum temperature data and provides theoretical guarantees for the estimation process.

Findings

Parameters estimated accurately on simulated data

Method successfully applied to real temperature data

Effective in assessing heat wave risks

Abstract

This paper proposes a stochastic approach to model temperature dynamic and study related risk measures. The dynamic of temperatures can be modelled by a mean-reverting process such as an Ornstein-Uhlenbeck one. In this study, we estimate the parameters of this process thanks to daily observed suprema of temperatures, which are the only data gathered by some weather stations. The expression of the cumulative distribution function of the supremum is obtained thanks to the law of the hitting time. The parameters are estimated by a least square method quantiles based on this function. Theoretical results, including mixing property and consistency of model parameters estimation, are provided. The parameters estimation is assessed on simulated data and performed on real ones. Numerical illustrations are given for both data. This estimation will allow us to estimate risk measures, such as the probability of heat wave and the mean duration of an heat wave.