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Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression

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  • Chang Kyeom Kim
  • Min Hyeok Yoon
  • Sangyeol Lee

Abstract

This study proposes a control chart that monitors conditionally heteroscedastic time series by integrating the Huber support vector regression (HSVR) and the one-class classification (OCC) method. For this task, we consider the model that incorporates nonlinearity to the generalized autoregressive conditionally heteroscedastic (GARCH) time series, named HSVR-GARCH, to robustly estimate the conditional volatility when the structure of time series is not specified with parameters. Using the squared residuals, we construct the OCC-based control chart that does not require any posterior modifications of residuals unlike previous studies. Monte Carlo simulations reveal that deploying squared residuals from the HSVR-GARCH model to control charts can be immensely beneficial when the underlying model becomes more complicated and contaminated with noises. Moreover, a real data analysis with the Nasdaq composite index and Korea Composite Stock Price Index (KOSPI) datasets further disclose the validity of using the bootstrap method in constructing control charts.

Suggested Citation

  • Chang Kyeom Kim & Min Hyeok Yoon & Sangyeol Lee, 2024. "Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-31, February.
    • Handle: RePEc:plo:pone00:0299120
    DOI: 10.1371/journal.pone.0299120
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    1. Alwan, Layth C & Roberts, Harry V, 1988. "Time-Series Modeling for Statistical Process Control," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(1), pages 87-95, January.
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