Document Search
Add To My Bookshelf Sign in or Register Save And Annotate
Keywords:

kernel estimation sign in to follow this
quantile estimate sign in to follow this
GARCH sign in to follow this
CAViaR sign in to follow this
nonparametric sign in to follow this
quantile regression sign in to follow this
Categories:

VaR Methods sign in to follow this
--Evaluation/Comparison sign in to follow this
Half-Life:
Impact:
Discuss This Paper
Sign in to follow this page
Recent Comments
  more
Nonparametric Estimate for Conditional Quantiles of Time Series: An application for VaR
Company: Center for Applied Statistics and Economics, Humboldt Universität zu Berlin
Month Of Publication: January
Pages: 37
Download Count: 1
View Count: 1017
Comment Num: 0
Language: English
Source: thesis
Who Can Read: Free
Date: 12-27-2012
Publisher: Administrator
Summary
This paper investigates a nonparametric approach for estimating conditional quantiles of time series for dependent data. The considered estimate is obtained by inverting a kernel estimate of the conditional distribution function. We implement the technique on four simulated samples with light and heavy-tailed distributions and on real financial data, by calculating VaR using the nonparametric procedure. The good performance of the estimator is illustrated with backtesting.
Author(s)
Balcau, Ioana Sign in to follow this author
This document's citation network:
Similar Documents:
Close window
Sign up in one step, no personal information required. Already a Member?



Email:
Repeat Email:
User Name:
Password:
Confirm Password:

Sign Up


Welcome to GloriaMundi!
Thanks for singning up



continue or edit your profile