A New Class of Conditional Tail Expectation Estimators

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

Henriques Rodrigues, L (Author) Other; Gomes, M (Author) Other; Figueiredo, Fernanda (Author) FEP; Caeiro, F (Author) Other

Conference proceedings International

Title: Springer Proceedings in Mathematics and Statistics

Pages: 295-308

26th Congress of the Portuguese Statistical Society, SPE 2023

Evora, 13 October 2021 through 16 October 2021

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-017-SKG

DOI: 10.1007/978-3-031-68949-9_22

Abstract (EN): Extreme value theory is a crucial tool in finance and risk management for evaluating the tail risk of a distribution. We shall focus on the conditional tail expectation (CTE) among various risk measures, as it is regarded as more informative than the value-at-risk at a level q, the upper (1¿q)-quantile of the loss function. We consider a Pareto tail for the right-tail function and work with heavy tailed models, i.e. models with a positive extreme value index (EVI), quite common in finance. The link between the estimation of both the EVI and the CTE allows for the utilization of the class of EVI estimators based on the power mean of the log-excesses in CTE estimation. To assess the behaviour of this class in finite samples, Monte Carlo simulation experiments will be conducted. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13