• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.835-841
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• 2005

IGARCH and Stochastic Volatility Model(SVM, for short) have frequently provided useful approximations to the real aspects of financial time series. This article is concerned with modeling various Korean financial time series using both IGARCH and stochastic volatility models. Daily data sets with sample period ranging from 2000 and 2004 including KOSPI, KOSDAQ and won-dollar exchange rate are comparatively analyzed using IGARCH and SVM.

• 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.

• Management & Information Systems Review
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• v.33 no.2
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• pp.119-133
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• 2014

This Research focuses on the effect of the price limits change in KOSDAQ market change on the volatility. The sample period ranges from 22 May 2000 to 24 March 2010 for daily data. We construct two subsample periods for comparing with the effect of the change of the price limit. These limits were relaxed from 12% to 15% on March 25, 2005. The first subsample period is from 25 March 2000 to 24 March 2005. The second subsample period is from 25 March 2005. to 24 March 2010. We employee four different volatility, which are the range-based volatility of Parkinson(1980; PK), Garman and Klass(1980; GK) Rogers and Satchell(1991; RS), Yang and Zhang(2008; YZ). The empirical result as follows. The major findings are summarized as follows; First, the volatility of individual stocks in KOSDAQ market reduces significantly after the price limit change. Second, There is so high volatile especially when the volatility of stock prices is high. Third, There is no meaningful relationship between volatility and market capitalization. Fourth, the more volume stocks reduce the volatility. Our results show the volatility decreased the more large volume, the more trading amount and the high price stock.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.235-261
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• 2003

We examine the relationship between the trading activities of Korea Stock Price Index (KOSPI) 200 futures contract and its underlying stock market volatility for about six years from May 1996 when the futures contract was introduced. The trading activities of the futures contracts are proxied by the volume and open interest, which are divided into expected and unexpected portions by using the previous data. The daily, intradilay, and overnight cash volatility is estimated by the GJR-GARCH model. We find a positive contemporaneous relationship between the intradaily stock market volatility and the unexpected futures volume while the relationship between the volatility and expected futures volume is weakly negative or non-existent. We also find that the unexpected futures volume strongly causes intradaily cash volatility. On the other hand, the overnight cash volatility causes the unexpected futures volume. The impulse responses between these variables are all positive. The result implies that during a trading time futures trading tends to increase the cash volatility while the unexpected overnight changes in cash volatility tends to increase the futures trading activities. We, however, find no association between the cash volatility and futures maturities.

• Journal of Distribution Science
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• v.11 no.10
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• pp.73-79
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• 2013

Purpose - This study examines the impact of oil price volatility on economic activities in Korea. The new millennium has seen a deregulation in the crude oil market, which invited immense capital inflow into Korea. It has also raised oil price levels and volatility. Drawing on the recent theoretical literature that emphasizes the role of volatility, this paper attends to the asymmetric changes in economic growth in response to the oil price movement. This study further examines several key macroeconomic variables, such as interest rate, production, and inflation. We come to the conclusion that oil price volatility can, in some part, explain the structural changes. Research design, data, and methodology - We use two methodological frameworks in this study. First, in regards to the oil price uncertainty, we use an Exponential-GARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity: EGARCH) model estimate to elucidate the asymmetric effect of oil price shock on the conditional oil price volatility. Second, along with the estimation of the conditional volatility by the EGARCH model, we use the estimates in a VECM (Vector Error Correction Model). The study thus examines the dynamic impacts of oil price volatility on industrial production, price levels, and monetary policy responses. We also approximate the monetary policy function by the yield of monetary stabilization bond. The data collected for the study ranges from 1990: M1 to 2013: M7. In the VECM analysis section, the time span is split into two sub-periods; one from 1990 to 1999, and another from 2000 to 2013, due to the U.S. CFTC (Commodity Futures Trading Commission) deregulation on the crude oil futures that became effective in 2000. This paper intends to probe the relationship between oil price uncertainty and macroeconomic variables since the structural change in the oil market became effective. Results and Conclusions - The dynamic impulse response functions obtained from the VECM show a prolonged dampening effect of oil price volatility shock on the industrial production across all sub-periods. We also find that inflation measured by CPI rises by one standard deviation shock in response to oil price uncertainty, and lasts for the ensuing period. In addition, the impulse response functions allude that South Korea practices an expansionary monetary policy in response to oil price shocks, which stems from oil price uncertainty. Moreover, a comparison of the results of the dynamic impulse response functions from the two sub-periods suggests that the dynamic relationships have strengthened since 2000. Specifically, the results are most drastic in terms of industrial production; the impact of oil price volatility shocks has more than doubled from the year 2000 onwards. These results again indicate that the relationships between crude oil price uncertainty and Korean macroeconomic activities have been strengthened since the year2000, which resulted in a structural change in the crude oil market due to the deregulation of the crude oil futures.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.429-442
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• 2015

Volatility forecasting in financial markets is an important issue because it is directly related to the profit of return. The volatility is generally modeled as time-varying conditional heteroskedasticity. A generalized autoregressive conditional heteroskedastic (GARCH) model is often used for modeling; however, it is not suitable to reflect structural changes (such as a financial crisis or debt crisis) into the volatility. As a remedy, we introduce the Markov regime switching GARCH (MRS-GARCH) model. For the empirical example, we analyze and forecast the volatility of the daily Korea Composite Stock Price Index (KOSPI) data from January 4, 2000 to October 30, 2014. The result shows that the regime of low volatility persists with a leverage effect. We also observe that the performance of MRS-GARCH is superior to other GARCH models for in-sample fitting; in addition, it is also superior to other models for long-term forecasting in out-of-sample fitting. The MRS-GARCH model can be a good alternative to GARCH-type models because it can reflect financial market structural changes into modeling and volatility forecasting.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.213-231
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• 2003

This paper uses the Efficient Method of Moments(EMM) of Gallant and Tauchen to estimate continuous-time stochastic volatility diffusion model for the Korean Composite Stock Price Index, sampled daily over $1995\sim2002$. The estimates display non-normality of stock index return, leptokurtic distribution, and stochastic volatility. Funker, this study suggests that two factor stochastic volatility model will be more desirable than one factor stochastic volatility model to estimate daily Korean stock return and also suggests that the stochastic volatility diffusions should allow for Poisson jumps of time-varying intensity.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.291-305
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• 2014

Persistence is one of the typical characteristics appearing in the volatility of financial time series. According to the recent researches, the volatility persistence may be due to either volatility shifts or long-range dependence. In this paper, we consider residual-based CUSUM tests to distinguish volatility persistence, long-range dependence and volatility shifts in GARCH models. It is observed that this test procedure achieve reasonable powers without a size distortion. Moreover, we employ AIC and BIC criteria to estimate the change points and the number of change points in volatility. We demonstrate the superiority of residual-based CUSUM tests on various Monte Carlo simulations and empirical data analysis.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.46-54
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• 2020

This study aims to develop and analyze the performance of a selective option straddle strategy based on forecasted volatility to improve the weakness of typical straddle strategy solely based on negative volatility risk premium. The KOSPI 200 option volatility is forecasted by the SVM model combined with the asymmetric volatility spillover effect. The selective straddle strategy enters option position only when the volatility is forecasted downwardly or sideways. The SVM model is trained for 2008-2014 training period and applied for 2015-2018 testing period. The suggested model showed improved performance, that is, its profit becomes higher and risk becomes lower than the benchmark strategies, and consequently typical performance index, Sharpe Ratio, increases. The suggested model gives option traders guidelines as to when they enter option position.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.135-150
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• 2013

This study proposes a novel option strategy by using characteristic of volatility reversion and rough set algorithm in options market. Until now, various research has been conducted on stock and future markets, but minimal research has been done in options market. Particularly, research on the option trading strategy using high frequency data is limited. This study consists of two purposes. The first is to enjoy a profit using volatility reversion model when volatility gap is occurred. The second is to pursue a more stable profit by filtering inaccurate entry point through rough set algorithm. Since options market is affected by various elements like underlying assets, volatility and interest rate, the point of this study is to hedge elements except volatility and enjoy the profit following the volatility gap.