• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• 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.

• 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 Financial Management
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• v.17 no.1
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• pp.45-65
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• 2000

본 논문은 시장상황별 주식시장의 제 현상이 상이하다는 점을 고려하여 한국주식시장에서 시장 상승기(bull market)와 시장 하락기(bear market)에 대한 주식수익률 분포의 특성을 파악하고, 음의 수익률충격에 대한 비대칭적 변동성과 시장이상현상들 중 하나인 요일효과를 시장 상황별로 실증분석하였다. 본 논문에 사용된 자료는 1990년 1월 3일부터 1997년 3월 31일 동안의 한국종합주가지수 및 자본금 규모별로 대형주지수, 중형주지수, 소형주지수의 명목수익률로 전환된 일별자료이다. 시장상황별 분석을 위하여 시장 상승기와 하락기에 따라 3기의 하위기간으로 구분하여 분석하였다. 분석에 사용된 모형은 EGARCH모형과 수정된 GARCH모형인 GJR모형이다. 분석결과 시장하락기인 하부기간1과 하부기간3에서 음의 수익률충격에 대한 비대칭적 변동성이 강하게 나타나지만 시장상승기인 2기간에는 비대칭적 변동성반응이 나타나지 않았다. 이는 주식시장이 상승국면일 때보다는 하락국면일 때 나쁜 뉴스에 대해 훨씬 민감하게 반응하는 결과이다. 또한 한국주식시장에서 월요일의 수익률이 시장하락기에 음의 수익률을 보이지만 통계적 유의성은 없었으며, 반면에 시장이 상승기인 하부기간2에서는 월요일과 수요일에 통계적 유의성이 매우 큰 양의 값을 나타냈다.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.155-160
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• 2018

The article studies the effect of US real effective exchange rate and short-term interest rate on Chnise exports and imports using the EGARCH-GED model. This article analyze the effect of US major economic variables on China's exports and imports as the US pushes for interest rate hikes and worsens trade wars with China. The main results are as follows. The US short-term interest rate has a significant positive effect on China's trade volume. Even in the case of China's exports, US short-term interest rate has a significant positive effect. However, in the case of China's imports, in contrast to exports, US short-term interest rate do not have a significant effects and US real effective exchange rate has a significant positive effect. On the other hand, China's policy interest rate has a negative impact on China's imports and not statistically significant, but it has a significant positive effect on China's exports.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.113-118
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• 2019

This study analyzed how interdependence between China, Hong Kong, Taiwan and Singapore stock markets changed after the implementation of Shanghai-Hong Kong Stock Connect system using the EGARCH-GED model that allow simultaneous analysis of return and variability. Since the implementation of this system, the interdependence of Taiwan stock market with the Greater China stock markets has been weakened, and the interdependence of Singapore's stock market with the Greater China stock markets has not been exist. On the other hand, he interdependence between China and Hong Kong stock markets has been shown to be significantly enhanced since the implementation of this system. This is appears to be the result of improved conditions for Chinese and Hong Kong investors to invest in the two stock markets following the implementation of this system. Thus, considering the portfolio investment in the Greater China stock markets, the investors will need to develop their investment strategies in light of these facts that the weakening interdependence of the Taiwan and Singapore securities markets and the strengthening interdependence of the Chinese and Hong Kong securities markets.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.183-188
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• 2019

This study analyzed the effects of short-term interest rates, exchange rates and international oil prices on international grain prices using the EGARCH-GED model. The yield before one month of the international grain prices itself was found to have a significant effect on international grain prices for most periods. During the entire analysis period, none of the economic variables appeared to have a significant effect on international grain prices, whereas during the exchange fall period, only oil prices were shown to have a significant effect on international grain prices. In addition, during the pre-crisis period, interest rates, exchange rates and oil prices did not all have a significant effect, but during the post-crisis period only oil prices had a significant effect on international grain prices. It turns out that the factors affecting international grain prices are changing with the passage of time.

• Journal of Korea Port Economic Association
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• v.35 no.2
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• pp.61-76
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• 2019

The purpose of this study is to use the EGARCH model and Granger causality test to analyze how the change in the BDI affects the Korean stock price volatility. The main analysis results are summarized as follows. First, according to the results of the mean equation, the change in the BDI is significant in large-cap stocks, as well as in the manufacturing, service, and chemistry indexes, but not in others. This implies that the Korean stock market does not respond appropriately to the maritime market situation; further, the increase in demand for raw materials has not led to a real economic recovery. Second, in the result of the variance equation, the coefficient on the change in the BDI is negative(-), and the change in the BDI is significant for all size indexes. Particularly, the change in the BDI has a greater impact on the volatility of small-cap stocks than that of large-cap stocks. The results of the analysis of the sector indexes were statistically significant for the service, financial, construction, and electric and electronics industries, but not for the manufacturing and chemical industries. In particular, the changes in the BDI have the greatest impact on the construction industry. Third, according to the Granger causality test results, the change in the BDI leads the financial industry and construction industry. There is, however, no relationship between the BDI and the other indexes. This shows that change in the shipping freight index can be used to predict the volatility in the Korean stock market. This can help investors and policymakers make better decisions.

• 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.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.107-118
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• 2022

In this study, daily data from January 2002 to June 2022 were used to investigate the relationship between risk-return relationship and market fear, uncertainty, stock market, and maritime freight index for the crude oil market. For this study, the time varying EGARCH-M model was applied to the risk-return relationship, and the wavelet consistency model was used to analyze the relationship between market fear, uncertainty, stock market, and maritime freight index. The analysis results of this study are as follows. First, according to the results of the time-varying risk-return relationship, the crude oil market was found to be related to high returns and high risks. Second, the results of correlation and Granger causality test, it was found that there was a weak correlation between the risk-return relationship and VIX, EPU, S&P500, and BDI. In addition, it was found that there was no two-way causal relationship in the risk-return relationship with EPU and S&P500, but VIX and BDI were found to affect the risk-return relationship. Third, looking at the results of wavelet coherence, it was found that the degree of the risk-return relationship and the relationship between VIX, EPU, S&P500, and BDI was time-varying. In particular, it was found that the relationship between each other was high before and after the crisis period (financial crisis, COVID-19). And it was found to be highly associated with organs. In addition, the risk-return relationship was found to have a positive relationship with VIX and EPU, and a negative relationship with S&P500 and BDI. Therefore, market participants should be well aware of economic environmental changes when making decisions.