• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.41-47
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• 2010

In this paper, we show the performance evaluation of outlier detection methods based on the GARCH model. We first introduce GARCH model and the methods of outlier detection in the GARCH model. The results of small simulation and the real KOSPI data show the out-performance of the outlier detection method over the traditional method in the GARCH model.

• The Korean Journal of Financial Management
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• v.26 no.4
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• pp.191-234
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• 2009

This study examined on the volatility feedback hypothesis through the use of threshold GARCH-in-Mean (GJR-GARCH-M) model developed by Glosten, Jaganathan, and Runkle (1993) in the stock markets of 14 emerging countries during the period of January, 1996 to May, 2009. On this study, I found successful evidences which can support the volatility feedback hypothesis through the following three estimation procedures. First, I found relatively strong positive relationship between the expected market risk premiums and their conditional standard deviations from the GARCH-M model in the basis of daily return on each representative stock market index, which is appropriate to investors' risk-averse preferences. Second, I can also identify the significant asymmetric time-varying volatility originated from the investors' differentiated reactions toward the unexpected market shocks by applying the GJR-GARCH-M model and further find the lasting positive risk aversion coefficient estimators. Third, I derived the negative signs of the regression coefficient of unpredicted volatility on the stock market return by re-applying the GJR-GARCH-M model after I controlled the positive effect of predicted volatility through including the conditional standard deviations from the previous GARCH-M model estimation as an independent explanatory variable in the re-applied new GJR-GARCH-M model. With these consecutive results, the volatility feedback effect was successfully tested to be effective also in the various emerging stock markets, although the leverage hypothesis turned out to be insufficient to be applied to another source of explaining the negative relationship between the unexpected volatility and the ex-post stock market return in the emerging countries in general.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.683-692
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• 2010

This short article is concerned with a categorical time series obtained after clipping a heteroscedastic GARCH process. Estimation methods are discussed for the model parameters appearing both in the original process and in the resulting binary time series from a clipping (cf. Zhen and Basawa, 2009). Assuming AR-GARCH model for heteroscedastic time series, three data sets from Korean stock market are analyzed and illustrated with applications to calculating certain probabilities associated with the AR-GARCH process.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.831-839
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• 2010

Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.821-831
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• 2011

It has been relatively incomplete in the field of financial time series to adapt asymmetric features to multivar ate GARCH processes (McAleer et al., 2009). Retaining constant conditional correlation(CCC) structure, this article pursues to introduce asymmetric GARCH modelling in analysing multivariate volatilities in time series in a practical point of view. Multivariate Korean financial time series are analyzed in detail to compar our theory with conventional methodologies including GARCH and EGARCH.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.19-26
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• 2019

The paper aims to measure stock price volatility on Ho Chi Minh stock exchange (HSX). We apply symmetric models (GARCH, GARCH-M) and asymmetry (EGARCH and TGARCH) to measure stock price volatility on HSX. We used time series data including the daily closed price of VN-Index during 1/03/2001-1/03/2019 with 4375 observations. The results show that GARCH (1,1) and EGARCH (1,1) models are the most suitable models to measure both symmetry and asymmetry volatility level of VN-Index. The study also provides evidence for the existence of asymmetric effects (leverage) through the parameters of TGARCH model (1,1), showing that positive shocks have a significant effect on the conditional variance (volatility). This result implies that the volatility of stock returns has a big impact on future market movements under the impact of shocks, while asymmetric volatility increase market risk, thus increase the attractiveness of the stock market. The research results are useful reference information to help investors in forecasting the expected profit rate of the HSX, and also the risks along with market fluctuations in order to take appropriate adjust to the portfolios. From this study's results, we can see risk prediction models such as GARCH can be better used in risk forecasting especially.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.415-422
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• 2012

In this paper, we first the compare the performance of Holt-Winters, FSARIMA, AR-GARCH and Seasonal AR-GARCH models with in the short term based data. The results of the compared data show that the Holt-Winters model outperformed other models in terms of forecasting accuracy.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.995-1005
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• 2009

Conditional correlation between financial time series plays an important role in risk management, asset allocation and portfolio selection and therefore diverse efforts for modeling conditional correlations in multivariate-GARCH processes have been made in last two decades. In particular, CCC (cf. Bollerslev, 1990) and DCC(dynamic conditional correlation, cf. Engle, 2002) models have been commonly used since they are relatively parsimonious in the number of parameters involved. This article is concerned with DCC modeling for multivariate GARCH processes in comparison with CCC specification. Various multivariate financial time series are analysed to illustrate possible advantages of DCC over CCC modeling.

• The Korean Journal of Financial Management
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• v.10 no.2
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• pp.263-301
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• 1993

최근의 연구결과에 의하면 분산이 시간에 따라 변화하여 이분산적(異分散的)이며, 시계열상관(時系列相關)이 존재하는 것으로 나타나고 있다. 일정(一定)한 분산을 가정하여 주식수익률(株式收益率)의 움직임을 설명하는 기존의 모형들은 주식수익률(株式收益率)을 예측하는데 편의(偏倚)(bias)를 가지게 되며, 또한 투자자(投資者)들에게 정확한 위험측정(危險測定)의 수단을 제공하지 못하고 있다. 따라서 본 연구는 우리나라 주식수익률(株式收益率)의 분산이 시간에 따라 변화하는지를 살펴보기 위해 종합주가지수(綜合株價指數) 및 규모별(規模別) 지수(指數)를 사용하여 ARCH 및 GARCH 모형을 추정하였다. 또한 기대수익률(期待收益率)과 조건부(條件附) 분산(分散)사이의 다기간(多期間)(intertemporal) 관계를 ARCH-M 및 GARCH-M 모형을 사용하여 추정하였다. 추정결과는 우리나라 주식시장에도 유의적인 ARCH 및 GARCH 효과, 즉 주식수익률이 매우 이분산적(異分散的)인 것으로 나타났다. 그리고 기대수익률(期待收益率)과 조건부(條件附) 분산(分散)사이의 관계에서 ARCH-M 모형과 GARCH-M 모형의 추정결과가 다르게 나타났으나 전체적으로 유의하지 않는 것으로 나타났다. 이러한 본 연구결과로 조건부(條件附) 분산모형(分散模型)을 통하여 기대수익률(期待收益率) 및 분산(分散)의 움직임을 더욱 잘 파악할 수 있을 것으로 생각되며, 따라서 주식수익률(株式收益率) 및 분산(分散)의 예측에 더 좋은 도구로 활용될 수 있을 것으로 생각된다.