• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.605-614
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• 2009

TGARCH models characterized by asymmetric volatilities have been useful for analyzing various time series in financial econometrics. We are concerned with persistent volatility in the TGARCH context. Park et al. (2009) introduced I-TGARCH process exhibiting a certain persistency in volatility. This article applies I-TGARCH model to various financial time series in Korea and it is obtained that I-TGARCH provides a better fit than competing models.

• The Korean Journal of Financial Studies
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• v.14 no.1
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• pp.119-158
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• 2008

본 연구에서는 복합금융그룹의 부실위험을 그룹전체기반 측도로 측정하는 방법론을 비교하고 국내 복합금융그룹의 자료를 이용하여 실증분석한다. Joint Forum(2001a) 방법은 연결기준을 사용하여 그룹내 자본의 중복요소들을 상계한 후 필요자본 대비 자기자본비율을 구한다. 신BIS 규제자본 방법은 Vasicek(1987)의 점근적 단일위험 모형을 가정하여 자산의 전체기반 위험을 측정하고 연결기준을 사용하여 자본의 중복계상을 배제하여 측정한다. 개별 경제적 자본 방법은 개별 경제적 위험을 수준별로 합산하여 전체기반 경제적 자본을 빌딩블록 방식으로 합산한다. 경제적 자본 방법은 위험 측정시 겪게 되는 극단적 손실 문제와 결합분포의 비대칭성을 반영할 수 있는 방법을 측정시 포함시킬 수 있다. 국내 복합금융그룹의 자료를 이용하여 실증분석을 한 결과, 첫째, 개별 재무지표에서 복합금융그룹 소속회사들의 ROA, ROA 변동성 그리고 총자산 대비 자기자본비율이 우량한 것으로 나타났다. 특히 가장 비중이 큰 은행산업에서 위 개별 재무지표는 복합금융그룹 소속회사에서 우량하게 나타난다. 둘째, 그룹전체기반 위험자본 측도로서 필요자본 대비 자기자본 비율과 연결기준 BIS비율을 살펴본 결과 은행계열 금융그룹의 부실위험이 낮은 것으로 판단된다. 전체적으로 국내 복합금융그룹의 부실위험은 높지 않은 것으로 판단된다. 이상의 결과를 바탕으로 복합금융그룹에 대한 리스크상시감시방안에의 시사점을 살펴보면, 첫째, 복합금융그룹 소속 금융회사에 대한 리스크 평가시 그룹전체기반 부실위험평가를 반영하여 이를 측정할 필요가 있다. 둘째, 권역별로 통일된 리스크감시를 위해 권역별 자기자본규제의 형평성을 제고할 필요가 있다.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.53-60
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• 2007

Conditionally heteroscedastic time series models such as GARCH processes have frequently provided useful approximations to the real aspects of financial time series. It is not uncommon that financial time series exhibits near non-stationary, say, integrated phenomenon. For stationary GARCH processes, a shock to the current conditional variance will be exponentially converging to zero and thus asymptotically negligible for the future conditional variance. However, for the case of integrated process, the effect will remain for a long time, i.e., we have a persistent effect of a current shock on the future observations. We are here concerned with providing empirical evidences of persistent GARCH(1,1) for various fifteen domestic financial time series including KOSPI, KOSDAQ and won-dollar exchange rate. To this end, kurtosis and Integrated-GARCH(1,1) fits are reported for each data.

• 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.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1219-1228
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• 2014

Constant conditional correlation (CCC) is frequently employed for parsimony in the field of multivariate GARCH time series. An extended-CCC (ECCC) model is further developed in order to allow interactions between multivariate volatilities. The paper introduces both CCC model and ECCC model to the domestic financial time series. The CCC and ECCC models are fitted and then compared with each other through various multivatiate time series.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1163-1170
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• 2015

Various computational methods for obtaining volatilities for financial time series are reviewed and compared with each other. We reviewed model based GARCH approach as well as the data based method which can essentially be regarded as a smoothing technique applied to the squared data. The method for high frequency data is focused to obtain the realized volatility. A hybrid method is suggested by combining the model based GARCH and the historical volatility which is a data based method. Korea stock prices are analysed to illustrate various computational methods for volatilities.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.505-512
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• 2016

The paper is concerned with high frequency financial time series. A weighted hybrid volatility is suggested to compute daily volatilities based on high frequency data. Various realized volatility (RV) computations are reviewed and the weights are chosen by minimizing the differences between the hybrid volatility and the realized volatility. A high frequency time series of KOSPI200 index is illustrated via QLIKE and Theil-U statistics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.86-98
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• 2014

This study examined a long-standing issue with its perverse results in the Korean capital markets, such as any variant financial profiles over time, affecting capital structure for the firms belonging to the chaebols. It may be of interest to identify these components from the perspectives of international investors and domestic policy makers to implement their contingent strategies on the target leverage, since the U.S. financial turmoils in the late 2000s. Regarding the evidence from the three hypothesis tests on the firms in the chaebols, this research found that the control variabels measuring profitability, business risk, and non-debt tax shields, showed their statistically significant relationships with the different types of a debt ratio. While FCFF(free cash flow to the firm) showed its significant influence to discriminate between the firms in the chaebols and their counterparts, not belonging to the chaebols, BDRELY as the ratio of liabilities to total assets, comprising the enhanced 'Dupont' system, only showed its statistically significant effect on leverage in the context of the parametric and nonparametric tests. In line with the results obtained from the present research, one may expect that a firm in the Korean chaebol, may control or restructure its present level of capital structure to revert to its target optimal capital structure towards maximizing the shareholders' wealth.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.61-69
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• 2011

In GARCH context, the conditional variance (or volatility) is of a quadratic function of the observation process. Examine standard ARCH/GARCH and their variant models in terms of quadratic formulations and it is interesting to note that most models in GARCH context have contained neither the first order term nor the interaction term. In this paper, we consider three models possessing the first order and/or interaction terms in the formulation of conditional variances, viz., quadratic GARCH, absolute value GARCH and bilinear GARCH processes. These models are investigated with a view to model comparisons and applications to financial time series in Korea