New forecasting scheme using quantum minimization to regularize a composite of prediction and its nonlinear heteroscedasticity

Bao Rong Chang, Hsiu Fen Tsai, Chung Ping Young

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

A hybrid BPNN-weighted GREY-C3LSP prediction (BWGC) is performed very well for resolving the overshoot phenomenon significantly, but it definitely deteriorates the predictive accuracy once the volatility clustering occurs. Thus, we propose a new scheme to incorporate non-linear generalized autoregressive conditional heteroscedasticity (NGARCH) into BWGC prediction, which is optimally regularized by quantum minimization (QM), in such a way that the composite model, BWGC/NGARCH, can overcome the problem of volatility clustering substantially. In comparison with forecasting performance of several notable prediction methods, the proposed one can get the best predictive accuracy in two typical experiments.

Original languageEnglish
Pages (from-to)1251-1262
Number of pages12
JournalInternational Journal of Innovative Computing, Information and Control
Volume3
Issue number5
Publication statusPublished - 2007 Oct

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

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