New Approach to Financial Time Series Forecasting - Quantum Minimization Regularizing BWGC and NGARCH Composite Model
- Bao Rong Chang 0, Hsiu Fen Tsai
- Corresponding Author
- Bao Rong Chang
0Dept. of CSIE, National Taitung University
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- https://doi.org/10.2991/jcis.2006.125How to use a DOI?
- BPNN-weighted GREY-C3LSP prediction, non-linear generalized autoregressive conditional heteroscedasticity, quantum minimization.
- 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.
- 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 - NaN/NaN DA - NaN/NaN 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 - ChangNaN/NaN ER -