• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.487-497
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• 2007

As is pointed out by Gourieroux (1997), the volatility effects in financial time series vary according to the signs of the return rates and therefore asymmetric Threshold-GARCH (TGARCH, henceforth) processes are natural extensions of the standard GARCH toward asymmetric volatility modeling. For preliminary detection of asymmetry in volatility, we suggest graphs of squared-log-returns for various financial time series including KOSPI, KOSDAQ and won-Euro exchange rate. Next, asymmetric TGARCH(1,1) model fits are provided in comparisons with standard GARCH(1.1) models.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• 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.34 no.5
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• pp.823-835
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• 2021

Identification of factors influencing regional population is critical for establishing government's population policies as well as for improving residents' social, economic and cultural well-being in the region. In this study we analysed the data from 2019 Population Housing Survey in Korea to identify the factors affecting the population size in each of the three regions: Seoul, metropolitan cities, and provincial regions. We applied a Bayesian quantile regression to account for asymmetry and heteroscedasticity of data. The analysis results showed that the effects of factors vary greatly between the three regions of Seoul, metropolitan cities, and provincial regions as well as between sub regions within the same region. These results suggest that population-related variables have very heterogeneous characteristics from region to region and therefore it is important to establish customized population policies that suit regional characteristics rather than uniform population policies that apply to every region.