Monitoring parameter change for time series models with application to location-Scale heteroscedastic models

Lee, Sangyeol; Kim, Chang Kyeom

doi:10.1080/00949655.2022.2086983

Cited by 4 publications

(5 citation statements)

References 51 publications

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“…In this study, we considered the monitoring process to detect a change in BINGARCH(1,1) and BINAR(1) models based on the previous work of Lee and Kim ( 2022 ). For this task, the inferential procedures for these two models were summarized a priori.…”

Section: Discussionmentioning

confidence: 99%

“…In this section, we construct a monitoring process using the closed-ended scheme in Lee and Kim ( 2022 ) for detecting a change in of Models ( 1 ) and ( 14 ). We illustrate the BINGARCH case more intensively as the BINAR case can be similarly analyzed.…”

Section: Monitoring Processmentioning

confidence: 99%

“…The condition of was used in ( 26 ), which was actually needed as does not hold in general. As the parameter estimation can hamper the performance of the monitoring process and cause some upward size distortions, following the suggestion of Lee and Kim ( 2022 ), we alternatively use for some , which gets closer to when n / l becomes smaller as assumed herein. There is no specific rule to choose an optimal theoretically.…”

Section: Monitoring Processmentioning

confidence: 99%

“…A suitable can be determined empirically through Monte Carlo experiments. This technique was demonstrated to be valid for GARCH-type models in various settings including the case of , refer to Lee and Kim ( 2022 ). Their simulation study provided a guidance of to be a number between 0.3 and 1.…”

Section: Monitoring Processmentioning

confidence: 99%

“…All these works handle not only the case of known parameters but also the situation that the parameter estimates are plugged into the monitoring processes, wherein the control limit is determined asymptotically on the basis of limit theorems. Lee and Kim ( 2022 ) recently generalized the method of Lee et al ( 2020 ) for a broader class of time series models, established its theoretical basis, and demonstrated its validity using empirical studies. In this research, we aim to extend their method to bivariate time series models of counts following the BINGARCH and BINAR models, and conduct an empirical study to demonstrate its validity in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring parameter change for bivariate time series models of counts

Lee

Kim

2023

J. Korean Stat. Soc.

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In this study, we consider an online monitoring procedure to detect a parameter change for bivariate time series of counts, following bivariate integer-valued generalized autoregressive heteroscedastic (BIGARCH) and autoregressive (BINAR) models. To handle this problem, we employ the cumulative sum (CUSUM) process constructed from the (standardized) residuals obtained from those models. To attain control limits, we develop limit theorems for the proposed monitoring process. A simulation study and real data analysis are conducted to affirm the validity of the proposed method.

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Section: Discussionmentioning

confidence: 99%

Section: Monitoring Processmentioning

confidence: 99%

Section: Monitoring Processmentioning

confidence: 99%

Section: Monitoring Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring parameter change for bivariate time series models of counts

Lee

Kim

2023

J. Korean Stat. Soc.

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Sequential monitoring for conditional quantiles of general conditional heteroscedastic time series models

Lee,

Kim

2024

Appl Stoch Models Bus & Ind

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In this study, we introduce an online monitoring procedure designed to sequentially detect change points in the conditional quantiles of location‐scale time series models. This statistical process control issue holds great significance in risk management, particularly in measuring the value‐at‐risk or expected shortfall of financial assets. Our approach employs suitable detectors, including cumulative sum statistics. We then define a stopping rule and determine control limits based on asymptotic theorems to signal an anomaly. To further evaluate the proposed methods, we conduct a comprehensive empirical study analyzing various aspects of our monitoring procedures when applied to location‐scale time series models. Additionally, we perform a real data analysis using the daily returns of the Korea Composite Stock Price Index (KOSPI) and EuroStoxx 50 indices to affirm the adequacy of the proposed monitoring procedures in real‐world applications.

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