Identification of Risk Extreme Values in a Time Series and Analysis with an Autoregressive Method - Application for Climate Risk Events

TL;DR

This paper explores identifying extreme values in time series data, especially climate risks, using autoregressive models to analyze trends and lagged correlations without focusing on basic risk management concepts.

Contribution

It introduces a method for analyzing extreme values in time series with autoregression, emphasizing its application to climate risk events and lagged correlations.

Findings

Autoregression effectively models extreme values in climate data.

Climate risk events often exhibit lagged correlations.

The method is adaptable to various time series analyses.

Abstract

In this article there is no intention to repeat basic concepts about risk management, but we will try to define why often is usefull the time series analysis during the assessment of risks, and how is possible to compute a significative analysis using regression and autoregression. After some basic concepts about trend analysis, will be introduced some methods to identify peaks. This is often usefull when there is no need to use the full time series, because sometimes is more practical to focus only on the extremes. With a correct time series without not-anomalous data, the extremes time series are treated with a simply autoregression model. This drives to know if the time series has a correlation between periods, and how many periods could be considered lagged among them. We think that climate events frequently are lagged because the climate show a clear increasing tendency, and that climate risks are potentially increasing during the time. There will be no specific conclusion related with risk management, because the proposed solution with autoregression can be adapted to any time series analysis.