9th Joint International Conference on Information Sciences (JCIS-06)

New Approach to Financial Time Series Forecasting - Quantum Minimization Regularizing BWGC and NGARCH Composite Model

Authors
Bao Rong Chang 0, Hsiu Fen Tsai
Corresponding Author
Bao Rong Chang
0Dept. of CSIE, National Taitung University
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.125How to use a DOI?
Keywords
BPNN-weighted GREY-C3LSP prediction, non-linear generalized autoregressive conditional heteroscedasticity, quantum minimization.
Abstract
A hybrid BPNN-weighted GREY-C3LSP prediction (BWGC) is used for resolving the overshooting phenomenon significantly; however, it may lose the localization once volatility clustering occurs. Thus, we propose a compensation to deal with the time-varying variance in the residual errors, that is, incorporating a non-linear generalized autoregressive conditional heteroscedasticity (NGARCH) into BWGC, and quantum minimization (QM) is employed to regularize the smoothing coefficients for both BWGC and NGARCH to effectively improve model’s robustness as well as to highly balance the generalization and the localization.
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
DOI
https://doi.org/10.2991/jcis.2006.125How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Bao Rong Chang
AU  - Hsiu Fen Tsai
PY  - 2006/10
DA  - 2006/10
TI  - New Approach to Financial Time Series Forecasting - Quantum Minimization Regularizing BWGC and NGARCH Composite Model
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/jcis.2006.125
DO  - https://doi.org/10.2991/jcis.2006.125
ID  - Chang2006/10
ER  -