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Forecasting and change point test for nonlinear heteroscedastic time series based on support vector regression

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  • HsinKai Wang
  • Meihui Guo
  • Sangyeol Lee
  • Cheng-Han Chua

Abstract

SVR-ARMA-GARCH models provide flexible model fitting and good predictive powers for nonlinear heteroscedastic time series datasets. In this study, we explore the change point detection problem in the SVR-ARMA-GARCH model using the residual-based CUSUM test. For this task, we propose an alternating recursive estimation (ARE) method to improve the estimation accuracy of residuals. Moreover, we suggest using a new testing method with a time-varying control limit that significantly improves the detection power of the CUSUM test. Our numerical analysis exhibits the merits of the proposed methods in SVR-ARMA-GARCH models. A real data example is also conducted using BDI data for illustration, which also confirms the validity of our methods.

Suggested Citation

  • HsinKai Wang & Meihui Guo & Sangyeol Lee & Cheng-Han Chua, 2022. "Forecasting and change point test for nonlinear heteroscedastic time series based on support vector regression," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0278816
    DOI: 10.1371/journal.pone.0278816
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    References listed on IDEAS

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    1. Ferger, Dietmar, 2018. "On the supremum of a Brownian bridge standardized by its maximizing point with applications to statistics," Statistics & Probability Letters, Elsevier, vol. 134(C), pages 63-69.
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