• The Journal of Fisheries Business Administration
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• v.40 no.2
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• pp.49-70
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• 2009

This study proviedes GARCH model(Bollerslev, 1986) to analyze the structural characteristics of price volatility in domestic aquacultural fish market of Korea. As a case study, flatfish and rock-fish are analyzed as major species with relatively high portion in an aspect of production volume among fish captured in Korea. For analyzing, this study uses daily market data (dating from Jan 1 2000 to June 30, 2008) published by the Noryangjin Fisheries Wholesale Market which is located in Seoul of Korea. This study performs normality test on trading volume and price volatility of flatfish and rock-fish as an advanced empirical approach. The normality test adopted is Jarque-Bera test statistic. As a result, first, a null hypothesis that "an empirical distribution follows normal distribution" was rejected in both fishes. The distribution of daily market data of them were not only biased toward positive(+) direction in terms of kurtosis and skewness, but also characterized by leptokurtic distribution with long right tail. Secondly, serial correlations were found in data on market trading volume and price volatility of two species during very long period. Thirdly, the results of unit root test and ARCH-LM test showed that all data of time series were very stationary and demonstrated effects of ARCH. These statistical characteristics can be explained as a reasonable ground for supporting the fitness of GARCH model in order to estimate conditional variances that reveal price volatility in empirical analysis. From empirical data analysis above, this study drew the following conclusions. First of all, from an empirical analysis on potential effects of seasonality and the day of week on price volatility of aquacultural fish, Monday effects were found in both species and Thursday and Friday effects were also found in flatfish. This indicates that Monday is effective in expanding price volatility of aquacultural fish market and also Monday has higher effects upon the price volatility of fish than other days of week have since it has more new information for weekend. Secondly, the empirical analysis led to a common conclusion that there was very high price volatility of flatfish and rock-fish. This points out that the persistency parameter($\lambda$), an index of possibility for current volatility to sustain similarly in the future, was higher than 0.8-equivalently nearly to 1-in both flatfish and rock-fish, which presents volatility clustering. Also, this study estimated and compared and model that hypothesized normal distributions in order to determine fitness of respective models. As a result, the fitness of GARCH(1, 1)-t model was better than model where the distribution of error term was hypothesized through-distribution due to characteristics of fat-tailed distribution, was also better than model, as described in the results of basic statistic analysis. In conclusion, this study has an important mean in that it was introduced firstly in Korea to investigate in price volatility of Korean aquacultural fishery products, although there was partially a limited of official statistic data. Therefore, it is expected that the results of this study will be useful as a reference material for making and assessing governmental policies. Also, it is looked forward that the results will be helpful to build a fishery business plan as and aspect of producer, and also to take timely measures to potential price fluctuations of fishery products in market. Hence, it is advisable that further studies related to such price volatility in fishery market will extend and evolve into a wider variety of articles and issues in near future.

• Journal of Preventive Medicine and Public Health
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• v.30 no.3 s.58
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• pp.585-598
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• 1997

Background : Although there are growing concerns about the adverse health effect of air pollution, not much evidence on health effect of current air pollution level had been accumulated yet in Korea. This study was designed to evaluate the chronic health effect of ai. pollution using Korean Medical Insurance Corporation (KMIC) data and air quality data. Medical insurance data in Korea have some drawback in accuracy, but they do have some strength especially in their national coverage, in having unified ID system and individual information which enables various data linkage and chronic health effect study. Method : This study utilized the data of Korean Environmental Surveillance System Study (Surveillance Study), which consist of asthma, acute bronchitis, chronic obstructive pulmonary diseases (COPD), cardiovascular diseases (congestive heart failure and ischemic heart disease), all cancers, accidents and congenital anomaly, i. e., mainly potential environmental diseases. We reconstructed a nested case-control study wit5h Surveillance Study data and air pollution data in Korea. Among 1,037,210 insured who completed? questionnaire and physical examination in 1992, disease free (for chronic respiratory disease and cancer) persons, between the age of 35-64 with smoking status information were selected to reconstruct cohort of 564,991 persons. The cohort was followed-up to 1995 (1992-5) and the subjects who had the diseases in Surveillance Study were selected. Finally, the patients, with address information and available air pollution data, left to be 'final subjects' Cases were defined to all lung cancer cases (424) and COPD admission cases (89), while control groups are determined to all other patients than two case groups among 'final subjects'. That is, cases are putative chronic environmental diseases, while controls are mainly acute environmental diseases. for exposure, Air quality data in 73 monitoring sites between 1991 - 1993 were analyzed to surrogate air pollution exposure level of located areas (58 areas). Five major air pollutants data, TSP, $O_3,\;SO_2$, CO, NOx was available and the area means were applied to the residents of the local area. 3-year arithmetic mean value, the counts of days violating both long-term and shot-term standards during the period were used as indices of exposure. Multiple logistic regression model was applied. All analyses were performed adjusting for current and past smoking history, age, gender. Results : Plain arithmetic means of pollutants level did not succeed in revealing any relation to the risk of lung cancer or COPD, while the cumulative counts of non-at-tainment days did. All pollutants indices failed to show significant positive findings with COPD excess. Lung cancer risks were significantly and consistently associated with the increase of $O_3$ and CO exceedance counts (to corrected error level -0.017) and less strongly and consistently with $SO_2$ and TSP. $SO_2$ and TSP showed weaker and less consistent relationship. $O_3$ and CO were estimated to increase the risks of lung cancer by 2.04 and 1.46 respectively, the maximal probable risks, derived from comparing more polluted area (95%) with cleaner area (5%). Conclusions : Although not decisive due to potential misclassication of exposure, these results wert drawn by relatively conservative interpretation, and could be used as an evidence of chronic health effect especially for lung cancer. $O_3$ might be a candidate for promoter of lung cancer, while CO should be considered as surrogated measure of motor vehicle emissions. The control selection in this study could have been less appropriate for COPD, and further evaluation with another setting might be necessary.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.19-25
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• 2019

The CleanSYS(Clean SYStem) is operated to monitor air pollutants emitted from specific industrial complexes in Korea. So the industrial complexes without the system are directly monitored by the control officers. For efficient monitoring, studies using various sensors have been conducted to monitor air pollutants emitted from industrial complex. In this study, hyperspectral sensors were used to model and verify the equations for estimating the concentration of $NO_2$(nitrogen dioxide) in air pollutants emitted. For development of the equations, spectral radiance were observed for $NO_2$ at various concentrations with different SZA(Solar Zenith Angle), VZA(Viewing Zenith Angle), and RAA(Relative Azimuth Angle). From the observed spectral radiance, the calculated value of the difference between the values of the specific wavelengths was taken as an absorption depth, and the equations were developed using the relationship between the depth and the $NO_2$ concentration. The spectral radiance mixed gas of $NO_2$ and $SO_2$(sulfur dioxide) was used to verify the equations. As a result, the $R^2$(coefficient of determination) and RMSE(Root Mean Square Error) were different from 0.71~0.88 and 72~23 ppm according to the form of the equation, and $R^2$ of the exponential form was the highest among the equations. Depending on the type of the equations, the accuracy of the estimated concentration with varying concentrations is not constant. However, if the equations are advanced in the future, hyperspectral sensors can be used to monitor the $NO_2$ emitted from the industrial complex.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Journal of Korea Water Resources Association
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• v.56 no.3
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• pp.195-210
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• 2023

Additional deposition and erosion in large rivers in South Korea have continued to occur toward morphological stabilization after massive dredging through the four major river restoration project, subsequently requiring precise bathymetry monitoring. Hyperspectral bathymetry method has increasingly been highlighted as an alternative way to estimate bathymetry with high spatial resolution in shallow depth for replacing classical intrusive direct measurement techniques. This study introduced the conventional Optimal Band Ratio Analysis (OBRA) of hyperspectral bathymetry method, and evaluated the performance in a domestic large river in normal turbid and flow condition. Maximum measurable depth was estimated by applying correlation coefficient and root mean square error (RMSE) produced during OBRA with cascadedly applying cut-off depth, where the consequent hyperspectral bathymetry map excluded the region over the derived maximum measurable depth. Also non-linearity was considered in building relation between optimal band and depth. We applied the method to the Nakdong and Hwang River confluence as a large river case and obtained the following features. First, the hyperspectal method showed acceptable performance in morphological mapping for shallow regions, where the maximum measurable depth was 2.5 m and 1.25 m in the Nakdong and Hwang river, respectively. Second, RMSE was more feasible to derive the maximum measurable depth rather than the conventional correlation coefficient whereby considering various scenario of excluding range of in situ depths for OBRA. Third, highly turbid region in Hwang River did not allow hyperspectral bathymetry mapping compared with the case of adjacent Nakdong River, where maximum measurable depth was down to half in Hwang River.

• 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.