Testing Extreme Value Copulas to Estimate the Quantile

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Using Extreme Value Theory to Estimate Value-at-Risk

Martin Odening and Jan Hinrichs Abstract: This article examines problems that may occur when conventional Value-at-Risk (VaR) estimators are used to quantify market risks in an agricultural context. For example, standard VaR methods, such as variance-covariance method or historical simulation, can fail when the return distribution is fat tailed. This problem is aggravated when long-term VaR for...

متن کامل

Modelling the Dependence of Parametric Bivariate Extreme Value Copulas

In this study, we consider the situation where contraints are made on the domains of two random variables whose joint copula is an extreme value model. We introduce a new measure which characterize these conditional dependence. We proved that every bivariate extreme value copulas is totally characterized by a conditional dependence function. Every twodimensional distribution is also shown to be...

متن کامل

Predicting extreme value at risk: Nonparametric quantile regression with refinements from extreme value theory

A framework is introduced allowing to apply nonparametric quantile regression to Value at Risk (VaR) prediction at any probability level of interest. A monotonized double kernel local linear estimator is used to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, nonparametric quantile regression is combined with extreme value theory. The ab...

متن کامل

Rank - Based Inference for Bivariate Extreme - Value Copulas

Consider a continuous random pair (X, Y) whose dependence is characterized by an extreme-value copula with Pickands dependence function A. When the marginal distributions of X and Y are known, several consistent estimators of A are available. Most of them are variants of the estimators due to Pickands [Bull. In this paper, rank-based versions of these esti-mators are proposed for the more commo...

متن کامل

Inference for Extremal Conditional Quantile Models (extreme Value Inference for Quantile Regression)

Quantile regression is a basic tool for estimation of conditional quantiles of a response variable given a vector of regressors. It can be used to measure the effect of covariates not only in the center of a distribution, but also in the upper and lower tails. Quantile regression applied to the tails, or simply extremal quantile regression is of interest in numerous economic and financial appli...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: SSRN Electronic Journal

سال: 2013

ISSN: 1556-5068

DOI: 10.2139/ssrn.2361163