• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.33-41
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• 2006

In this paper, we analyse the volatilities in financial data such as stock prices and exchange rates in term of a class of nonlinear time series models. We compare the performance of Generalized Autoregressive Conditional Heteroscadastic(GARCH) , Integrated GARCH(IGARCH), Exponential GARCH(EGARCH) models by KOSPI (Korean stock Prices Index) data. The estimation for the parameters in the models was carried out by the ML methods.

• 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.

• The Korean Journal of Financial Studies
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• v.3 no.2
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• pp.157-185
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• 1996

본 연구는 한국증권시장에서 변동성의 정보비대칭효과를 조건부 이분산모형을 이용하여 검증하고자 하였다. 검증방법으로는 Engle과 Ng (1993)의 연구에 기초하여 정보반응곡선(News impact curve)으로 분석하였다. 분석자료로 1980년 부터 1995년 까지의 한국종합주가지수, 일별 초과수익률자료를 사용하였다. 정보반응곡선에 이용한 모형은 GARCH 모형, EGARCH 모형, TGARCH 모형, AGARCH 모형등 4개의 조건부 이분산 모형이다. 무조건 분산을 이용한 정보 반응곡선의 함수형태로 보면, 분산의 정보반응에 있어서 GARCH 모형은 대칭적으로 반응하며 나머지 조건부 이분산 모형인 EGARCH 모형, TGARCH 모형, 그리고 AGARCH 모형은 비대칭적으로 반응하는 모형임을 알 수 있었다. 실증분석결과 정보반응곡선을 통하여 악재(bad news)정보에 따라 예측하지 못한 주식수익률의 하락이 호재(good news)에 따른 예측하지 못한 주식수익률의 상승보다 더 큰 변동성을 발견할 수 있었다. 그러나 비대칭성의 크기는 그다지 큰 것으로 보이지 않았다. 모형적합성 검정에서도 4개의 조건부 이분산 모형은 모두 적합한 것으로 보인다. 그중에서도 EGARCH 모형과 TGARCH 모형이 상대적으로 주가예측력이 뛰어나 보인다. 그러나 변동성의 정보 비대칭반응을 통계적으로 유의적인 것으로 확인한 모형은 TGARCH모형 뿐이었다.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.491-493
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• 2003

Load foretasting model used generally such as times series and econometric regression model often doesn't reflect the load characteristics of small remote islands. Therefore, in this paper load demand forecast is peformed using EGARCH-M non-linear forecasting model.

• The Korean Journal of Financial Management
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• v.12 no.2
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• pp.95-120
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• 1995

자본시장에서 자산가격결정이론의 대부분은 투자자산의 기대수익률과 변동성이 시간의 흐름에 따라 일정한 것으로 가정하여 왔다. 그러나 최근의 연구 성과에 의하면 주식수익률의 변동성이 동분산이라기 보다는 이분산일 가능성이 높다는 것이다. 1982년 Engle에 의하여 개발된 자기회귀 조건부 이분산모형(ARCH)이 제시된 이래 ARCH형태의 모형개발이 계속 이루어져 왔다. 본 논문은 ARCH형태의 이분산모형 가운데서 EGARCH모형을 이용하여 위험프레미엄과 조건부 이분산과의 관계와 더불어 기대하지 않은 수익률변화와 변동성과의 관계를 규명하고자 노력하였다. 1980년에서 1994년까지의 주가자료를 전체기간과 세부기간(4기간)으로 분류하여 기술 통계량 분석을 행하고, 종합주가지수초과수익률, 동일 가치 가중지수초과수익률, 대형주 주가지수초과수익률, 소형주 주가지수초과수익률에 대하여 EGARCH모형 을 적용하여 실증분석 하였다. 그 결과 위험프레미엄과 조건부 이분산은 시간이 지남에 따라 일정한 관계를 보여주지 못하고 있어 투자자의 위험회피도(危險回避度)가 변화함을 보여주었다. 기대하지 않은 수익률변화와 변동성 관계에서는 기대하지 않은 음(陰)(-)의 주식수익률이 기대하지 않은 양(陽)(+)의 주식수익률보다 상대적으로 더 큰 변동성을 가져오는 것으로 보여 우리나라 주식시장에서 주식수익률의 변동성 정보의 비대칭 반응효과가 존재하는 것으로 나타났다.

• KDI Journal of Economic Policy
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• v.15 no.4
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• pp.155-173
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• 1993

주식수익율(株式收益率)의 조건부분산(條件附分散)의 움직임을 모형화(模型化)하기 위하여 Engle(1982)의 ARCH(Autoregressive Conditional Heteroskedasticity)모형(模型)을 효시(嚆矢)로 많은 종류의 모형(模型)이 제시되어 왔다. 이 가운데서 Nelson(1991)의 EGARCH(Exponential Generalized ARCH)는 종래의 모형(模型)에 비하여 여러가지 장점(長點)을 지니고 있는 모형(模型)이다. 그러나 EGARCH에서는 비기대수익율(非期待收益率)(unexpected return)이 조건부분산(條件附分散)에 미처는 규모효과(規模效果)(magnitude effect)와 비대칭효과(非對稱效果)(asymmetry effect)의 영향(影響)이 동일한 동태(動態)(dynamics)를 보인다고 가정(假定)하고 있다. 본(本) 논문(論文)은 이 가정(假定)을 완화하였을 때 규모효과(規模效果)와 비대칭효과(非對稱效果)가 매우 다른 동태(動態)를 가지며, 특히 규모효과(規模效果)의 영향은 오래 지속되는 반면 비대칭효과(非對稱效果)는 비교적 빠르게 사라짐을 보여준다.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.243-266
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• 2006

This paper tests cross hedging performance of the KOSPI 200 stock index futures to hedge the downside risk of the KOSPI, KOSPI 200 and KOSDAQ50 spot market. For this purpose we introduce the minimum variance hedge model, bivariate GARCH(1,1) and EGARCH(1,1) model as hedge models. The main results are as follows; First, we find that the direct hedge performance of KOSPI 200 index futures is better than those of indirect hedge performance. second, in case or cross hedge performance the hedge effect of KOSPI 200 stock index futures market against KOSPI 200 stock index spot market is relatively better than those of KOSPI 200 index futures against KOSPI and KOSDAQ spot position. Third, for the out-sample, hedging effectiveness of the risk-minimization with constant hedge ratios is higher than those of the time varying bivariate GARCH(1,1) and EGARCH(1,1) model. In conclusion, investors are encouraged to use simple risk-minimization model rather than the time varying hedge models like GARCH and EGARCH model to hedge the position of the Korean stock index cash markets.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.1-14
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• 2015

The standard GARCH model imposing symmetry on the conditional variance, tends to fail in capturing some important features of the data. This paper, hence, introduces the models capturing asymmetric effect. They are the EGARCH model and the GJR model. We provide the systematic comparison of volatility models focusing on the asymmetric effect of news on volatility. Specifically, three diagnostic tests are provided: the sign bias test, the negative size bias test, and the positive size bias test. This paper shows that there is significant evidence of GARCH-type process in the data, as shown by the test for the Ljung-Box Q statistic on the squared residual data. The estimated unconditional density function for squared residual is clearly skewed to the left and markedly leptokurtic when compared with the standard normal distribution. The observation of volatility clustering is also clearly reinforced by the plot of the squared value of residuals of export volume and values. The unconditional variance of both export volumes and export value indicates that large shocks of either sign tend to be followed by large shocks, and small shocks of either sign tend to follow small shocks. The estimated export volume news impact curve for the GARCH also suggests that $h_t$ is overestimated for large negative and positive shocks. The conditional variance equation of the GARCH model for export volumes contains two parameters ${\alpha}$ and ${\beta}$ that are insignificant, indicating that the GARCH model is a poor characterization of the conditional variance of export volumes. The conditional variance equation of the EGARCH model for export value, however, shows a positive sign of parameter ${\delta}$, which is contrary to our expectation, while the GJR model exhibits that parameters ${\alpha}$ and ${\beta}$ are insignificant, and ${\delta}$ is marginally significant. That indicates that the asymmetric volatility models are poor characterization of the conditional variance of export value. It is concluded that the asymmetric EGARCH and GJR model are appropriate in explaining the volatility of export volume, while the symmetric standard GARCH model is good for capturing the volatility.

• Korea Real Estate Review
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• v.27 no.3
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• pp.41-50
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• 2017

Previous studies, especially that by Lee (2014), showed how time series volatility models can be applied to the house price series. As the regional housing market trends, however, have shown significant differences of late, analysis with national data may have limited practical implications. This study applied volatility models in analyzing and forecasting regional house prices. The estimation of the AR(1)-ARCH(1), AR(1)-GARCH(1,1), and AR(1)-EGARCH(1,1,1) models confirmed the ARCH and/or GARCH effects in the regional house price series. The RMSEs of out-of-sample forecasts were then compared to identify the best-fitting model for each region. The monthly rates of house price changes in the second half of 2017 were then presented as an example of how the results of this study can be applied in practice.

• Land and Housing Review
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• v.12 no.3
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• pp.27-37
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• 2021

This paper empirically examined the relationship between the housing market and the stock market to investigate the price and the asymmetric volatility spillover effects. The monthly housing price index and the monthly KOSPI were used for analysis. This research employed the EGARCH model. The analysis period was from January 1986 until June 2021 with periodization centered on the Asian Financial Crisis: before and after the crisis - the end of December 1997. The EGARCH model allows analysis of 'good news' and 'bad news' in understanding volatility. The price spillover effect was observed one way from the stock market to the housing market. On the contrary, the spillover effect was not found from the housing market to the stock market. The empirical evidence suggests that there are price and asymmetric volatility effects in the entire period of analysis in both housing and the stock markets. In the housing market, the negative effects of information were found pre-financial crisis while the positive effects, in other periods. However, in the stock market, the negative effects of information were found in the pre- and post-financial crisis periods. This means that the housing market is more affected by 'good news' than 'bad news' when information spreads to the markets while the stock market is more affected by 'bad news' than 'good news'. It is of significance to discover the variable returns by different information.