• Korean Journal of Agricultural Science
• /
• v.45 no.4
• /
• pp.859-870
• /
• 2018

Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.

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

• Environmental and Resource Economics Review
• /
• v.13 no.2
• /
• pp.175-194
• /
• 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 Korea Port Economic Association
• /
• v.31 no.1
• /
• pp.1-14
• /
• 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 Trade Review
• /
• v.47 no.4
• /
• pp.215-231
• /
• 2022

Analysis and forecasting of the Baltic Capsize Index (BCI) is important for managing an entity's losses and risks from the uncertainty and volatility of the fast-changing maritime transport market in the future. This study conducted volatility transition analysis through the GARCH model, using BCI which is highly related to steel raw materials. As for the data, 2,385 monthly data were used from March 1999 to March 2021. In this study, after basic statistical analysis, unit root and cointegration test, the GARCH, EGARCH, and DCC-GARCH models were used for volatility transition analysis. As the results of GARCH and EGARCH model, we confirmed that all variables had no autocorrelation between the standardized residuals for error terms and the square of residuals, that the variability of all variables at this time was likely to persist in the future, and that the variability of the time-series error term impact according to Iron ore trade (IoT). In addition, through the EGARCH model, the magnitude convenience of all variables except the Iron ore price (IOP) and Capesize bulk fleet (BCF) variables was greater than the positive value (+). As a result of analyzing the DCC-GARCH (1,1) model, partial linear combinations were confirmed over the entire period. Estimating the effect of variability transition on BCF and C5 with statistically significant linear combinations with BCI confirmed that the impact of BCF on BCI was greater than the impact of BCI itself.

• Ocean and Polar Research
• /
• v.37 no.4
• /
• pp.357-368
• /
• 2015

This study analyzed structural changes and asymmetry of price volatility during the period before and after a point of structural change in price volatility, using the Korean fresh common squid daily retail price data from January 1, 2004 to September 30, 2015. This study utilized the following analytical methods: the unit-root test was applied to ensure the stability of the data, the Quandt-Andrews breakpoint test was applied to find the point of structural change, and the Glosten-Jagannathan-Runkle GARCH and EGARCH models were applied to investigate the asymmetry of price volatility. The empirical results of this study are as follows. First, ADF, PP, KPSS and Zivot-Andrews tests showed that the daily retail price change rate of the Korean fresh common squid differentiated by logarithm was stable. Secondly, the ARIMA (2,1,2) model was selected by information criteria such as AIC, SC, and HQ. Thirdly, the Quandt-Andrews breakpoint test found that a single structural change in price volatility occurred on June 11, 2009. Fourthly, the Glosten-Jagannathan-Runkle GARCH and EGARCH models showed that estimates of coefficients within the models were statistically significant before and after structural change and also that asymmetry as a leverage effect existed before and after structural change.

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

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.11
• /
• pp.7672-7676
• /
• 2015

This study examines the dynamic relations between housing price and trading volume in a set of apartment markets in Republic of Korea to explore the informational role of trading volume in predicting the price volatility. Using monthly index data, EGARCH model is utilized to test for volume effect. To estimate the EGARCH-based volatility, two different sets of region are applied for the monthly return. Strong evidence has been found towards housing turnover leading price volatility, this supports previous studies on financial sector(s). These findings also support that trading volume in the housing market contains information on investor sentiment which, in turn, has a valuation effect on the price.

• The Journal of the Korea Contents Association
• /
• v.13 no.7
• /
• pp.432-439
• /
• 2013

In this study, we evaluate the volatility of housing prices by using literature review and empirical analysis and furthermore we suggest how to improve. In order to diagnose housing market, the KB Bank's House Price Index, Real estate 114;s materials were compared. In addition, to examine the volatility, GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and EGARCH (Exponential GARCH) model are used. By analysis of this research, we found the volatility of housing price also was reduced in the medium and the large houses since 1998, while the volatility of small housing price relatively was large. We proved that the price change rate of small housing was higher than the medium's. On the order hand, the supply of small apartments fell down sharply. The short-term oriented policy should be avoided, and the efficiency and credibility of policy should be increased. Furthermore, the long-term policy system should be established. and rental market's improvement is necessary for stabilization of housing market.

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