• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1037-1044
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• 2008

In this paper, we show the performance of the power transformation GARCH(PGARCH) model to analyze the internet traffic data. The long memory property which is the typical characteristic of internet traffic data can be explained by the PGARCH model rather than the linear GARCH model. Small simulation and the analysis of the real internet traffic show the out-performance of the PARCH MODEL over the linear GARCH one.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.713-722
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• 2020

Contrasted with the standard symmetric GARCH models, we consider a broad class of threshold-asymmetric models to analyse financial time series exhibiting asymmetric volatility. By further introducing power transformations, we add more flexibilities to the asymmetric class, thereby leading to power transformed and asymmetric volatility models. In particular, the paper is concerned with the nonstationary volatilities in which conditions for integrated volatility and explosive volatility are separately discussed. Dow Jones Industrial Average is analysed for illustration.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.755-764
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• 2022

This paper is concerned with power transformations in estimating GARCH volatility. To handle a semi-parametric case for which the exact likelihood is not known, quasi-likelihood (QL) rather than maximum-likelihood method is investigated to best estimate GARCH via maximizing the information criteria. A power transformation is introduced in the innovation generating QL estimating functions and then optimum power is selected by maximizing the profile information. A combination of two different power transformations is also studied in order to increase the parameter estimation efficiency. Nine domestic stock prices data are analyzed to order to illustrate the main idea of the paper. The data span includes Covid-19 pandemic period in which financial time series are really volatile.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.475-483
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• 2009

In this paper we introduced a class of nonlinear time series models to analyse KOSPI data. We introduce the Generalized Power-Transformation TGARCH (GPT-TGARCH) model and the model includes Zakoian (1993) and Li and Li (1996) models as the special cases. We showed the effectiveness and efficiency of the new model based on KOSPI data.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.271-281
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• 2005

Zokian(1993) and Li and Li(1996) developed TARCH(Threshold ARCH) model, considering the asymmetries in volatility. The models are based on Engle(1982)'s ARCH model and Bollerslev(1986)'s GARCH model. However, two TARCH models can be expressed a common model through Box Cox Power transformation, which was used by Higgins and Bera(1992) for developing NARCH(nonlinear ARCH) model. This article shows the PTARCH(Power transformation TARCH) model is necessary in some condition, and it checks the fact that PTARCH model has better performance comparing estimates and RMSE(Root Mean Square Error) with those of Zakoian's TARCH model and Li and Li's TARCH model. PTARCH model would give contribution in asymmetric study as well as heteroscedastic study.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.49-61
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• 2010

The time series dependencies of Financial volatility are frequently measured by the autocorrelation function of power-transformed absolute returns. It is known as the Taylor property that the autocorrelations of the absolute returns are larger than those of the squared returns. Hass (2009) developed a simple method for detecting the Taylor property in absolute-value-GAROH(1,1) (AVGAROH(1,1)) model. In this article, we fitted AVGAROH(1,1) model for various Korean financial time series and observed the Taylor property.