• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.181-200
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• 2003

Comovement of international stock market prices has been lately a major controversy in the global stock market. This paper explores whether the common trend has really existed among the US, Japan and Korea's stock markets using the econometric techniques such as VAR, VECM as applied. Pair of indices from the exchange market and the over-the-counter market in each country has been tested, and the exchange market only has been turned out that the common trend existed. The dynamic analyses using the Granger causality test, impulse response function, and the forecast error decomposition have followed to show that the US stock market has played some important role in the Korea and Japan's market in the exchange as well as in the OTC market. The results of the paper imply that the more careful investigation with respect to the co-integration may be necessary in the global market integration studies.

• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.49-67
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• 2006

This study examines whether won/dollar futures have price discovery function and volatility spillover effect or not, using intraday won/dollar futures prices, volumes, and spot rates for the interval from March 2, 2005 through May 30, 2005. Futures prices and spot rates are non-stationary, but there is the cointegration relationship between two time series. Futures returns, spot returns, and volumes are stationary. Asymmetric effects on volatility in futures returns and spot returns does not exist. Analytical results of mean equations of the BGARCH-EC (bivariate GARCH-error correction) model show that the increase of futures returns raise spot returns after 5 minutes, which implies that futures returns lead spot returns and won/dollar futures have price discovery function. In addition, the long-run equilibrium relationship between the two returns could help forecast spot returns. Analytical results of variance equations indicate that short-run innovations in the futures market positively affect the conditional variances of spot returns, that is, there is the volatility spillover effect in the won/dollar futures market. A dummy variable of volumes does not have an effect on two returns but influences significantly on two conditional variances.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.59.5-60
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• 2017

We present the relationship between vector magnetic field parameters and solar major flare occurrence rate. Based on this, we are developing a forecast model of major flare (M and X-class) occurrence rate within a day using hourly vector magnetic field data of Space-weather HMI Active Region Patch (SHARP) from May 2010 to April 2017. In order to reduce the projection effect, we use SHARP data whose longitudes are within ${\pm}60$ degrees. We consider six SHARP magnetic parameters (the total unsigned current helicity, the total photospheric magnetic free energy density, the total unsigned vertical current, the absolute value of the net current helicity, the sum of the net current emanating from each polarity, and the total unsigned magnetic flux) with high F-scores as useful predictors of flaring activity from Bobra and Couvidat (2015). We have considered two cases. In case 1, we have divided the data into two sets separated in chronological order. 75% of the data before a given day are used for setting up a flare model and 25% of the data after that day are used for test. In case 2, the data are divided into two sets every year in order to reduce the solar cycle (SC) phase effect. All magnetic parameters are divided into 100 groups to estimate the corresponding flare occurrence rates. The flare identification is determined by using LMSAL flare locations, giving more numbers of flares than the NGDC flare list. Major results are as follows. First, major flare occurrence rates are well correlated with six magnetic parameters. Second, the occurrence rate ranges from 0.001 to 1 for M and X-class flares. Third, the logarithmic values of flaring rates are well approximated by two linear equations with different slopes: steeper one at lower values and lower one at higher values. Fourth, the sum of the net current emanating from each polarity gives the minimum RMS error between observed flare rates and predicted ones. Fifth, the RMS error for case 2, which is taken to reduce SC phase effect, are smaller than those for case 1.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.215-226
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• 2018

The purpose of this study is to improve the accuracy of the urban runoff and drainage network analysis by using the observed water level in the drainage network. To do this, sensitivity analysis for major parameters of SWMM (Storm Water Management Model) was performed and parameters were calibrated. The sensitivity of the parameters was the order of the roughness of the conduit, the roughness of the impervious area, the width of the watershed, and the roughness of the pervious area. Six types of scenarios were set up according to the number and types of parameter considering four parameters with high sensitivity. These scenarios were applied to the Seocho-3/4/5, Yeoksam, and Nonhyun drainage basins, where the serious flood damage occurred due to the heavy rain on 21 July, 2013. Parameter optimization analysis based on PEST (Parameter ESTimation) model for each scenario was performed by comparing observed water level in the conduits. By analyzing the accuracy of each scenario, more improved simulation results could be obtained, that is, the maximum RMSE (Root Mean Square Error) could be reduced by 2.41cm and the maximum peak error by 13.7%. The results of this study will be helpful to analyze volume of the manhole surcharge and forecast the inundation area more accurately.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.85-98
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• 2016

This study makes an empirical comparison of various realized volatilities (RVs) in terms of overnight returns. In financial asset markets, during overnight or holidays, no or few trading data are available causing a difficulty in computing RVs for a whole span of a day. A review will be made on several RVs reflecting overnight return variations. The comparison is made for forecast accuracies of several RVs for some financial assets: the US S&P500 index, the US NASDAQ index, the KOSPI (Korean Stock Price Index), and the foreign exchange rate of the Korea won relative to the US dollar. The RV of a day is compared with the square of the next day log-return, which is a proxy for the integrated volatility of the day. The comparison is made by investigating the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). Statistical inference of MAE and RMSE is made by applying the model confidence set (MCS) approach and the Diebold-Mariano test. For the three index data, a specific RV emerges as the best one, which addresses overnight return variations by inflating daytime RV.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.291-295
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• 2007

강수진단모형을 이용하여 저수지 이수운영을 위한 실시간 유량예측기법을 개발하였다. 강수진단모형은 현재 기상청 현업에서 수행중인 강우수치예보를 기반으로 상세 지역의 지형 효과에 의한 강수를 예측하는 정량강수예측모형(QPM; Quantitative Precipitation Model)으로서 부경대학교 환경대기과학과에서 개발된 모형이다. QPM은 중규모 예측 모형으로부터 계산된 수평 바람, 고도, 기온, 강우 강도, 그리고 상대습도 등의 예측 자료를 이용하고, 소규모 상세지형 효과를 고려함으로써 중규모 예측 모형에서 생산된 강수량 예측 값을 상세 지역의 지형을 고려한 강수량 예측 값으로 재구성하여 결과적으로 3km 간격의 상세지역 강우산출과 지형에 따른 강수량의 분포 파악이 용이할 뿐만 아니라 계산 효율성을 개선된 모형이다. QPM 검증을 위하여 기상학적 평가와 수문학적 평가를 수행하였다. 호우 사례별 일강수량의 시공간 분포로 부터, QPM을 활용한 시스템에 의한 예측결과가 원시자료 RDAPS 보다 고해상도의 예측 및 지형효과의 반영도가 높았으며, AWS의 관측자료와 비교하여 보다 높은 예측성을 보여 주었다. 대상기간인 2006년 1월 1일부터 6월 20일까지 관측강우는 총 391.5mm 였으며 RQPM은 실적강우에 비하여 119.5mm 정도 과소산정하고 있으나 분위사상과정을 거치게 되면 351.7mm로서 실적강우에 불과 10.2% 못미치고 있다. 이는 고무적인 결과로 볼 수 있으며 현업에서의 활용성이 기대되는 수준이라 볼 수 있다. 강우-유출모의를 위한 QPM신뢰도를 높이기 위하여 분위사상법(Quantile Mapping)을 이용하여 QPM모의에 존재할 수 있는 계통오차에 대한 추가적인 보정을 수행하였다. 수문학적 평가를 위하여는 장기연속유출모형인 SSARR모형을 기반으로 개발된 RRFS(Rainfall-Runoff Forecast System)을 이용하여 2006년 1월${\sim}$9월까지의 용담댐 유입량에 대하여 모의예측결과와 관측유입량 비교를 통한 검증을 수행하였다. 위 기간중 예측유입량의 RMSE(Root Mean Squared Error), COE(Sutcliffe Coefficient of Efficiency), MAE(Mean Absolute Error), $R^2$값은 각각 7.50, 0.68, 2.59, 0.69 값을 보이고 있다. 본 연구에서는 QPM에 의한 예측성의 향상 및 구축된 시스템에 의한 일강수량의 장기예측 가능성을 확인하였고, 향후 시스템을 현업에 활용하기 위해서 생산된 예측자료의 보다 장기적인 검증을 통한 시스템의 안정화가 필요할 것으로 사료된다.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.821-832
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• 1998

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. Fuzzy neuron, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an adaptive learning method by which a membership function and fuzzy rules were adapted for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water supplied to the city of Kwangju. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supplied (b) the mean temperature, and (c)the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.35% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of the Korea Society for Simulation
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• v.24 no.2
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• pp.31-39
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• 2015

Unemployment is related to social issues as well as personal economics activity so various policies have been made to reduce the unemployment rate in many countries. Because of delay inherent in the survey mechanism to collect unemployment data, it takes lots of time to acquire survey unemployment data. To develop proper policies for reducing unemployment rate at the right time, it is quite critical to obtain faster and more accurate information concerning about unemployment level. To remedy this problem, recently an advanced analytics utilizing internet queries is suggested. To examine the potential of Web query information, this research investigates the usefulness of internet activity data to predict Korean unemployment rate. One of selected web-query data(unemployment claim) has a quite strong correlation with unemployment rate. This research employes a time series approach of the ARIMA model that utilizes the information of keyword queries provided by the Naver(Korean representative portal site) trend together with unemployment rate data provisioned from Statistics Korea. With respect to model selection guidelines of mean squared error and prediction error, the model with utilizing the web query information shows better results than the model without such information. This suggests that there is a strong potential for the used method, which needs to be further explored.

• International Area Studies Review
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• v.13 no.3
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• pp.287-308
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• 2009

This article examines the distributional characteristics of the return of Chinese stock market indices. The majority of previous empirical researches have tended to focus upon the simple stock market index. However, this study focuses on the four indices which represent the characteristics of each stock market index. The empirical findings indicate that the returns of the four chinese indices are not normally distributed at conventional levels. The Ljimg-Box -statistics indicate the returns of the index of A shares are not serially autocorrelated. However, the returns of the index of B shares are serially autocorrelated. The empirical findings also indicate returns of the four chinese indices are not serially autocorrelated. The statistics of Regression Specification Error Test and ARCH indicate the returns of all four indices are not serially linear. The findings also indicate that E- GARCH model is the most fittest model for the returns of the four chinese indices and the forecast error can be reduced by using student t distribution rather normal distribution.