Conditional quantile change test for time series based on support vector regression

Kim, Chang Kyeom; Lee, Sangyeol

doi:10.1080/03610918.2021.1992436

Cited by 1 publication

(1 citation statement)

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“…However, as GARCH-type time series inherently suffers from instability as observations can become wildly explosive, monitoring methods that accommodate variants of parametricor SVR-GARCH models are often inefficient for constructing a reliable test. For instance, [29] fitted an AR(p) model to the obtained residuals and truncated excessively large residuals to prevent them from undermining the Type I error rate of the test. This phenomenon arises primarily because standard SVRs still can be susceptible to the outliers lying in the training dataset [30], which generally makes the residuals behave in a more correlated manner, while also rendering some of them to become excessively larger than the others.…”

Section: Introductionmentioning

confidence: 99%

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

Kim,

Yoon,

Lee

2024

PLoS ONE

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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.

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Section: Introductionmentioning

confidence: 99%