Publication: Outliers in Garch models and the estimation of risk measures
Loading...
Identifiers
Publication date
2010-01
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Export
Abstract
In this paper we focus on the impact of additive level outliers on the calculation of risk
measures, such as minimum capital risk requirements, and compare four alternatives of reducing
these measures' estimation biases. The first three proposals proceed by detecting and correcting
outliers before estimating these risk measures with the GARCH(1,1) model, while the fourth
procedure fits a Student’s t-distributed GARCH(1,1) model directly to the data. The former
group includes the proposal of Grané and Veiga (2010), a detection procedure based on
wavelets with hard- or soft-thresholding filtering, and the well known method of Franses and
Ghijsels (1999). The first results, based on Monte Carlo experiments, reveal that the presence of
outliers can bias severely the minimum capital risk requirement estimates calculated using the
GARCH(1,1) model. The message driven from the second results, both empirical and
simulations, is that outlier detection and filtering generate more accurate minimum capital risk
requirements than the fourth alternative. Moreover, the detection procedure based on wavelets
with hard-thresholding filtering gathers a very good performance in attenuating the effects of
outliers and generating accurate minimum capital risk requirements out-of-sample, even in
pretty volatile periods
Description
Keywords
Minimum capital risk requirements, Outliers, Wavelets