• Environmental and Resource Economics Review
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• v.13 no.2
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• pp.175-194
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• 2004

Volumes of research have been implemented to estimate and predict the oil price. These models, however, fail in accurately predicting oil price as a model composed of only a few observable variables is limiting. Unobservable variables and news that have been overlooked in past research, yet have a high likelihood of affecting the oil price. Hence, this paper analyses the news impact on the price. The standard GARCH model fails in capturing some important features of the data. The estimated news impact curve for the GARCH model, which imposes symmetry on the conditional variances, suggests that the conditional variance is underestimated for negative shocks and overestimated for positive shocks. Hence, this paper introduces the asymmetric or leverage volatility models, in which good news and bad news have different impact on volatility. They include the EGARCH, AGARCH, and GJR models. The empirical results showed that negative shocks introduced more volatility than positive shocks. Overall, the AGARCH and GJR were the best at capturing this asymmetric effect. Furthermore, the GJR model successfully revealed the shape of the news impact curve and was a useful approach to modeling conditional heteroscedasticity.

• 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.

• Journal of Convergence for Information Technology
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• v.10 no.3
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• pp.67-75
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• 2020

This paper investigated the information spillover effect between stock market and bond market with the KOSPI daily index and MMF yield data. The overall analysis period is from May 2, 1997 to August 30, 2019. The empirical analysis was conducted by dividing the period from May 2, 1997 to December 30, 2008 before the global financial crisis, and from December 30, 2008 to August 30, 2019 after the global financial crisis, and the overall analysis period. The analysis shows that the EGARCH model considering asymmetric variability is suitable. The price spillover effect and volatility spillover effect existed in both directions between the stock market and the bond market, and the price transfer effect was greater in the period before the global financial crisis than in the period after the global financial crisis. Asymmetric volatility in information between stock and bond markets appears to exist in both markets.

• 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.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.119-121
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• 2006

본 논문은 중 전력, 고 입력 전압응용에서 많이 이용되는 비대칭 하프브릿지 PWM DC-DC 컨버터의 다중 출력 구성을 위한 최적의 설계 방식을 제안한다. 제안된 방식은 기본 PWM에서 부하에 따른 연속전류동작모드(CCM)의 구간을 늘여, 제어되는 전원의 부하 변동이 크더라도, 다중출력 되어지는 전원의 정상상태 동작점의 변동이 크게 발생하지 않도록 한다. 제어되는 비대칭 하프브릿지(ASHB)의 부하에 따른 시비율 변화가 작아지면 경 부하에서 변압기에 인가되는 전압의 크기가 줄어들어 다중 출력 되어지는 단의 전체적인 전압 스트레스가 감소한다. 또한 제안된 방식의 비대칭 하프브릿지 회로는 기존의 1차 측 전류 비대칭성을 보상하여 보다 넓은 부하 영역에서 영전압 스위칭이 가능하며 스위칭 손실과 이에 의한 EMI가 감소하게 된다. 제안된 방식은 기존 하드웨어와 비교하여 그 성능을 검증하였다.

• International Area Studies Review
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• v.15 no.3
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• pp.55-76
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• 2011

In this paper, we estimate a model that incorporates key features of business cycles, co-movement among economic variables and switching between regimes of expansion and recession, to aggregate quarterly data for the NAFTA region. Two common factors reflecting the permanent and transitory components of the business cycle in the region, along with the turning points from one regime to the other, were extracted from the data by using the Kalman filter and maximum likelihood estimation approach of Kim (1994). Estimation results confirm that a typical aspect of business cycles are also observed (i.e., recessions are steeper and shorter than recoveries) in the region, and that both co-movement and that regime switching are found to be important features of the business cycle. The two common factors produce sensible representations of the trend and cycle, and the estimated turning points are in line with independently determined chronologies. It also turns out that the degree of synchronization between the NAFTA region and Korea, has significantly increased since the entry into force of the NAFTA.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6084-6091
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• 2014

This paper presents the GJR GARCH model (Glosten et. al, 1993) to analyze the influences of volume volatilities on price volatilities in the fishery market. For the analysis, this study used the monthly price and volume data of aquacultural flatfish in Jeju. As a result, empirical analysis suggested volatility clustering. The persistency parameter(${\lambda}$) was estimated to be approximately 1 in aquacultural flatfish. The results showed that there is a significant negative relationship between the conditional variance of supply and that of price for aquacultural flatfish. This means that the general law of supply is valid. Finally, the empirical analysis was that an asymmetric coefficient (${\gamma}$) of GJR GARCH model was negative (-). This means that the higher volatility of volume leads to lower price volatility. That is, it is useful to make government policies that can adjust the volume (stockpiling, stabilizing supply and demand).

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.663-670
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• 2016

This paper analyzes the asymmetric pass-through effects of crude oil price changes on export prices in Korea's manufacturing sector using a nonlinear autoregressive distributed lag (NARDL) model. These pass-through effects are important for Korean companies that are highly dependent on exports. Because the effects differ by industry, eight sectors of the manufacturing industry were examined. The model is effective for separately testing the long-term and short-term differences between the export-price pass-through effects when crude oil prices increase and decrease. The estimation results show that there is positive pass-through to export prices as crude oil prices change, and there are asymmetric effects in some manufacturing sectors. Short-term asymmetries were detected in the export prices of five sectors that include general machinery and transport equipment, and significant long-term asymmetries were found for petroleum and coal products and for textile and leather products. The long-term export price of oil and coal products rose by 0.992% with a 1% increase in the oil price and fell by 0.977% with 1% decrease. Therefore, corporate strategies and government export policies should be established in accordance with these asymmetric pass-through effects.