The monitoring test for the stability of regression models with nonstationary regressors

Lee, Sangyeol; Park, Siyun

doi:10.1016/j.econlet.2009.08.013

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Further, Berkes, Gombay, Horvarh, and Kokoszaka (2004) considered the monitoring procedure based on quasi-likelihood scores in generalized autoregressive conditional heteroskedasticity (GARCH) models. Recently, Lee et al (submitted for publication) considered the monitoring process problem on the autocorrelation function (ACF) of linear processes and GARCH parameters, and Lee and Park (2009) studied the monitoring process problem for time series models with nonstationary regressors.…”

Section: Introductionmentioning

confidence: 99%

Monitoring parameter changes for random coefficient autoregressive models

Lee

2010

Journal of the Korean Statistical Society

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Section: Introductionmentioning

confidence: 99%

Monitoring parameter changes for random coefficient autoregressive models

Lee

2010

Journal of the Korean Statistical Society

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Test for parameter changes in generalized random coefficient autoregressive model

2014

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In this paper, we study the problem of testing for parameter changes in generalized random coefficient autoregressive model (GRCA). The testing method is based on the monitoring scheme proposed by Na et al. (Stat. Methods Appl. 20:171-199, 2011), and the test statistic relies on the conditional least-squares estimator of an unknown parameter. Furthermore, under mild conditions, we obtain the asymptotic property of the test statistic. Some simulation studies are also conducted to investigate the finite sample performances of the proposed test. MSC: Primary 62M10; secondary 91B62

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Research on Change Point Detection during Periods of Sharp Fluctuations in Stock Prices–Based on Bayes Method β-ARCH Models

Tian,

Wang,

Qin

et al. 2024

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In periods of dramatic stock price volatility, the identification of change points in stock price time series is important for analyzing the structural changes in financial market data, as well as for risk prevention and control in the financial market. As their residuals follow a generalized error distribution, the problem of estimating the change point parameters of the β-ARCH model is solved by combining the Kalman filtering method and the Bayes method innovatively, and we give a method for parameter estimation of the Bayes factors for the occurrences of change points, the expected values of the change point positions, and the variance of the change points. By detecting the change points of the price of eight stocks with a high number of limit up and limit down changes occurring in the observation period, the following conclusions are obtained: (1) Change point detection using the β-ARCH model based on the Bayes method is effective. (2) For different values of β, this research study finds that based on the classical ARCH model (i.e., β=1) of the change point parameter, the results are relatively optimal. (3) The accuracy of change point detection can be improved by correcting stock short-term effects by using the Kalman filtering method.

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The monitoring test for the stability of regression models with nonstationary regressors

Cited by 3 publications

References 12 publications

Monitoring parameter changes for random coefficient autoregressive models

Monitoring parameter changes for random coefficient autoregressive models

Test for parameter changes in generalized random coefficient autoregressive model

Research on Change Point Detection during Periods of Sharp Fluctuations in Stock Prices–Based on Bayes Method β-ARCH Models

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