• Management & Information Systems Review
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• v.36 no.3
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• pp.37-49
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• 2017

This study investigates the dynamic relationship between KOSPI200 stock index and stock index futures and stock index option markets which is its derived from KOSPI200 stock index. We use 5-minutes rate of return data from 2012. 06 to 2014. 12. To empirical analysis, this study use autocorrelation and cross-correlation analysis as a preliminary analysis and then following Stoll and Whaley(1990) and Chan(1992), the multiple regression is estimated to examine the lead-lag patterns between the stock index and stock index futures and option markets by Newey and West's(1987) Empirical results of our study shows as follows. First, there exist a strong autocorrelation in the KOSPI200 stock index before 10minutes but a very weak autocorrelation in the stock index futures and option markets. Second, there is a strong evidence that stock index future and option markets lead KOSPI200 stock index in the cross-correlation analysis. Third, based on the multiple regression, the stock index futures and option markets lead the stock index prior to 10-15 minutes and weak evidence that the stock index leads the future and option markets. This results show that the market efficient of KOSPI200 stock index market is improved as compared to the early stage of stock index future and option market.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.308-316
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• 2015

The purpose of this study is to verify TOM(turn-of-the-month) effect in the Kosdaq market, and that to compare to TOM effect of KOSPI for supporting degree of identification and to find new features. For this study, as the study basis sample, we used the daily data of the KOSDAQ from January 1996 to December 2013 and verified the TOM effect through yearly, monthly, classification by event as financial crisis, different period of TOM in order to clarify the effect of the KOSPI and KOSDAQ. As a result, We find that the TOM effect in KOSDAQ is always present uniformly in yearly, monthly, event-specific, which unlike TOM period also in KOSPI and generally TOM effect in KOSDAQ market which has larger volatility was appeared more pronouncedly than KOSPI market, and particularly TOM effect of KOSDAQ was larger than that of KOSPI on financial crisis occasion. But TOM effect of KOSDAQ was less stable than KOSPI.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.457-460
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• 2000

주가를 예측하는 것은 이미 오래 전부터 여러 가지 방법으로 시도되어 왔었다. 기업의 본질가치를 보는 기본적 분석부터 과거의 자료를 가지고 미래를 예측하는 기술적 분석까지 많은 연구가 있었으나 실제로 모든 예측이 그렇듯이 많이 적중을 했다는 것을 일부의 정형화된 분석방법을 제외하고는 찾지 못하였다. 그럼에도 불구하고 이번 연구에서는 기술적 분석에서 많은 요인들 중에서 기존에 많이 연구해 보지 못한 시계열적인 인자를 가지고 단기간의 주가를 예측하고자 한다. 주식이 상한가에 도달하였을 경우 그 상한가격의 잔량과 그 주식의 일일거래량을 비교하여 그 서로 두 관계가 다음날 주가에 어느 정도의 영향을 미치는지 회귀분석을 통하여 상관성을 분석하고 통계적 자료를 토대로 단기간의 주가를 상한 잔량 대비 일일거래량에 비추어 의사결정 지표를 제시하려고 한다. 적절한 예측결과가 나오게 되면 주식에 대해 매수를 희망하는 사람 뿐 아니라 주식을 보유하고 있는 사람에게 어느 정도 정보효과가 미치게 될 것이라 기대한다.

• 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.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.205-216
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• 2018

This paper is concerned with volatility computations for high frequency time series. A threshold-asymmetric realized volatility (T-RV) is suggested to capture a leverage effect. The T-RV is compared with various conventional volatility computations including standard realized volatility, GARCH-type volatilities, historical volatility and exponentially weighted moving average volatility. High frequency KOSPI data are analyzed for illustration.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.891-901
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• 2009

In this paper, we investigate the approach to estimate VaR under the transformed GARCH model. The time series are transformed to approximate to the underlying distribution of error terms and then the parameters and the one-sided prediction interval are estimated with the transformed data. The back-transformation is applied to compute the VaR in the original data scale. The analyses on the asset returns of KOSPI and KOSDAQ are presented to verify the accuracy of the coverage probabilities of the proposed VaR.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1203-1214
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• 2009

In this paper we introduce the time series clustering methods in the time and frequency domains and discuss the merits or demerits of each method. We analyze 15 daily stock prices of KOSPI 200, and the nonparametric method using the wavelet shows the best clustering results. For the clustering of nonstationary time series using the spectral density, the EMD method remove the trend more effectively than the differencing.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.251-260
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• 2017

The option pricing of Black와 Scholes (1973) and Merton (1973) has been widely reported to fail to reflect the time varying volatility of financial time series in many real applications. For example, Duan (1995) proposed GARCH option pricing method through Monte Carlo simulation. However, financial time series is known to follow a fat-tailed and leptokurtic probability distribution, which is not explained by Duan (1995). In this paper, in order to overcome such defects, we proposed the option pricing method based on GARCH models with normal mixture errors. According to the analysis of KOSPI200 option price data, the option pricing based on GARCH models with normal mixture errors outperformed the option pricing based on GARCH models with normal errors in the unstable period with high volatility.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.983-991
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• 2017

High frequency time series are now prevalent in financial data. However, models need to be further developed to suit high frequency time series that account for intraday volatilities since traditional volatility models such as ARCH and GARCH are concerned only with daily volatilities. Due to $H{\ddot{o}}rmann$ et al. (2013), functional ARCH abbreviated as fARCH is proposed to analyze intraday volatilities based on high frequency time series. This article introduces fARCH to readers that illustrate intraday volatility configuration on the KOSPI and the Hyundai motor company based on the data with one minute high frequency.