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

HsinKai Wang, Meihui Guo, Sangyeol Lee and Cheng-Han Chua

PLOS ONE, 2022, vol. 17, issue 12, 1-16

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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0278816

DOI: 10.1371/journal.pone.0278816

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