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

GARCH sign in to follow this
nonparametric sign in to follow this
quantile regression sign in to follow this
high-frequency sign in to follow this
intra-day 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
Hybrid Functional Autoregressive Modelling of Non-parametric Density for Improved Value-at-Risk Analysis
Year Of Publication: 2011
Month Of Publication: July
Pages: 45
Download Count: 1
View Count: 1161
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 1-1-2013
Publisher: Administrator
Summary
Functional Auto-regressive modelling of the non-parametric density function is proposed to improve Value-at-Risk (VaR) analysis by taking into account the relative advantages of parametric and non-parametric models in a hybrid manner. In particular, this approach enables us to use the intraday information for forecasting the time-varying daily return density function. Monte Carlo simulation study and empirical evaluations of VaR, based on thirty components of the Dow Jones Industrial Average and their equal weighted portfolio, clearly demonstrate that the overall performance of the proposed hybrid model is superior to those of both the parametric and the non-parametric models.
Author(s)
Zhang, Jacky Qi Sign in to follow this author
Cai, Charlie X. Sign in to follow this author
Kim, Minjoo Sign in to follow this author
Shin, Yongcheol Sign in to follow this author
This document's citation network:
Similar Documents:
Documents cited in this work:
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