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

long-range dependence sign in to follow this
equity markets sign in to follow this
GARCH sign in to follow this
long memory sign in to follow this
Hurst exponent sign in to follow this
fractional Brownian motion 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
Market Risk Prediction under Long Memory: When VaR is Higher than Expected
Year Of Publication: 2010
Month Of Publication: January
Pages: 49
Download Count: 7
View Count: 1537
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 8-14-2010
Publisher: Administrator
Summary
This paper addresses financial risk prediction for equity markets under long range dependence. We account for long memory in multi-period value-at-risk forecasts via a scaling based modification of the GARCH(1,1) forecast. Our results show that traditional value-at-risk forecasting techniques underestimate market risk while our approach yields superior results for forecast horizons of as little as ten or more trading days. This outperformance is only in part due to higher levels of our risk forecasts: Even after controlling for the unconditional VaR levels of both approaches by equalizing unconditional VaR forecasts, our approach delivers results that are not dominated by the standard approach.
This document is published in Operations Research Proceedings 2010, Part 4, 123-128
http://dx.doi.org/10.1007/978-3-642-20009-0_20
This document is published in Journal of Risk Finance (volume 15, number 1),
Author(s)
Wagner, Niklas Sign in to follow this author
Kinateder, Harald 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