Output details
11 - Computer Science and Informatics
Goldsmiths' College
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Article title
Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation
Type
D - Journal article
Title of journal
Expert Systems with Applications
Article number
-
Volume number
40
Issue number
6
First page of article
2233
ISSN of journal
09574174
Year of publication
2013
Number of additional authors
2
Additional information
<13> This is published in the most important journal in expert systems, with an H-index of 73 (SCImago) it reports the first research to design a proper model for variance estimation in financial time series, and derives reliable algorithm for its estimation. The experiments with our low order MT(2)-GARCH(1,1) show that it yields results with improved statistical characteristics and economic performance compared to linear and nonlinear heavy-tail GARCH, as well as normal mixture GARCH. We demonstrate that our model leads to reliable Value-at-Risk performance in short and long
trading positions across different confidence levels.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
0
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
