An Application of Extreme Value Theory for Measuring Financial Risk

Authors:
Manfred Gilli;Evis Këllezi
Affiliations:
Department of Econometrics, University of Geneva and FAME, Geneva, Switzerland;Mirabaud & Cie, Boulevard du Thééâtre 3, Geneva, Switzerland 1204
Venue:
Computational Economics
Year:
2006

Citing 4
Cited 7

Parameter and quantile estimation for the generalized pareto distribution

Technometrics
Modelling extremal events: for insurance and finance

Modelling extremal events: for insurance and finance
Statistical analysis of extreme values

Statistical analysis of extreme values
Using a bootstrap method to choose the sample fraction in tail index estimation

Journal of Multivariate Analysis

A Comparative Empirical Study on the Margin Setting of Stock Index Futures Calendar Spread Trading

Advanced Web and NetworkTechnologies, and Applications
Modelling the financial risk associated with U.S. movie box office earnings

Mathematics and Computers in Simulation
Volatility Modeling by Asymmetrical Quadratic Effect with Diminishing Marginal Impact

Computational Economics
Optimal task assignment in multithreaded processors: a statistical approach

ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
Insured access: an approach to ad-hoc information sharing for virtual organizations

Proceedings of the third ACM conference on Data and application security and privacy
Kernel Partitioning of Streaming Applications: A Statistical Approach to an NP-complete Problem

MICRO-45 Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture
Original article: Extreme market risk and extreme value theory

Mathematics and Computers in Simulation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Assessing the probability of rare and extreme events is an important issue in the risk management of financial portfolios. Extreme value theory provides the solid fundamentals needed for the statistical modelling of such events and the computation of extreme risk measures. The focus of the paper is on the use of extreme value theory to compute tail risk measures and the related confidence intervals, applying it to several major stock market indices.