An Extensive Comparison of Some Well‐Established Value at Risk Methods. (23rd July 2020)

• Record Type: Journal Article
• Authors: Calmon, Wilson; Ferioli, Eduardo; Lettieri, Davi; Soares, Johann; Pizzinga, Adrian
• Is Part Of: International statistical review. Volume 89:Number 1(2021)
• Page Start: 148
• Page End: 166
• Publication Date: 2020-07-23
• Subjects: backtest -- CAViaR -- extreme value theory -- filtered historical simulation -- higher order moments; Statistics -- Periodicals; Statistics -- Bibliography -- Periodicals; Statistics -- Bibliography; Statistics -- Periodicals; Statistique; Statistique -- Périodiques; Statistique -- Bibliographie -- Périodiques; Statistique -- Étude et enseignement -- Périodiques; Statistique -- Étude et enseignement -- Bibliographie -- Périodiques; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 519.2
• DOI: 10.1111/insr.12393
• Languages: English
• ISSNs: 0306-7734

Abstract

Summary: In the last two decades, several methods for estimating Value at Risk have been proposed in the literature. Four of the most successful approaches are conditional autoregressive Value at Risk, extreme value theory, filtered historical simulation and time‐varying higher order conditional moments. In this paper, we compare their performances under both an empirical investigation using 80 assets and a large Monte Carlo simulation. From our analysis, we conclude that most of the methods seem not to imply huge numerical difficulties and, according to usual backtests and performance measurements, extreme value theory presents the best results most of the times, followed by filtered historical simulation.