• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.531-540
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• 2007

In this research, we proposed a two-phase hybrid stock price forecasting model with cointegration tests and artificial neural networks. Using not only the related stocks to the target stock but also the past information as input features in neural networks, the new model showed an improved performance in forecasting than that of the usual neural networks. Firstly in order to extract stocks which have long run relationships with the target stock, we made use of Johansen's cointegration test. In stock market, some stocks are apt to vary similarly and these phenomenon can be very informative to forecast the target stock. Johansen's cointegration test provides whether variables are related and whether the relationship is statistically significant. Secondly, we learned the model which includes lagged variables of the target and related stocks in addition to other characteristics of them. Although former research usually did not incorporate those variables, it is well known that most economic time series data are depend on its past value. Also, it is common in econometric literatures to consider lagged values as dependent variables. We implemented a price direction forecasting system for KOSPI index to examine the performance of the proposed model. As the result, our model had 11.29% higher forecasting accuracy on average than the model learned without cointegration test and also showed 10.59% higher on average than the model which randomly selected stocks to make the size of the feature set same as that of the proposed model.

• Journal of Energy Engineering
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• v.20 no.4
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• pp.318-329
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• 2011

This study focuses on enhancing the accuracy of consumption function of Korean natural gas for city gas. It is using time-series model with time-varying coefficients taking into account the recent abnormal temperature phenomenon and the changing gross domestic product (GDP) as important variables. This study estimates the cointegrating regression model for the long-run estimation and the error correction model for the short-run estimation. The consumption function of Korean natural gas is estimated to be influenced by the time-varying coefficients of GDP and temperature. Using the estimated time-series model with time-varying coefficients, this study forecasts the consumption of natural gas for city gas from July 2011 to December 2012. The consumption in 2011 would be 18,303 thousand tons, which is little different from the imported 18,681 thousand tons. The consumption of natural gas for city gas in 2012 is forecast to be 19,213 thousand tons. The consumption model of this study is needed to extend by considering the relative prices between natural gas and its substitutes, the scale of consumers and others.

• Journal of Energy Engineering
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• v.22 no.3
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• pp.245-249
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• 2013

This paper attempts to estimate the kerosene demand function in Korea over the period 1981-2012. As the kerosene demand function provides us information on the pattern of consumer's kerosene consumption, it can be usefully utilized in predicting the impact of policy variables such as kerosene price and forecasting the demand for kerosene. We apply least absolute deviations and least median squares estimation methods as a robust approach to estimating the parameters of the kerosene demand function. The results show that short-run price and income elasticities of the kerosene demand are estimated to be -0.468 and 0.409, respectively. They are statisitically significant at the 1% level. The short-run price and income elasticities portray that demand for kerosene is price- and income-inelastic. This implies that the kerosene is indispensable goods to human-being's life, thus the kerosene demand would not be promptly adjusted to responding to price and/or income change. However, long-run price and income elasticities reveal that the demand for kerosene is price- and income-elastic in the long-run.

• The Korean Journal of Financial Management
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• v.25 no.3
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• pp.111-130
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• 2008

This paper compares long term equilibrium relation of KOSPI 200 which is underling stock and its futures by using general method fractional cointegration instead of existing integer cointegration. Existence of integer cointegration between two price time series gives much wider information about long term equilibrium relation. These details grasp long term equilibrium relation of two price time series as well as reverting velocity to equilibrium by observing difference coefficient of error term when it renounces from equilibrium relation. The result of this study reveals existence of long term equilibrium relation between KOSPI200 and futures which follow fractional cointegration. Difference coefficient, d, of 'two price time series error term' satisfies 0 < d < 1/2 beside bandwidth parameter, m(173). It means two price time series follow stationary long memory process. This also means impulse effects to balance price of two price time series decrease gently within hyperbolic rate decay. It indicates reverting speed of error term is very low when it bolts from equilibrium. It implies to market maker, who is willing to make excess return with arbitrage trading and hedging risk using underling stock, how invest strategy should be changed. It also insinuates that information transition between KOSPI 200 Index market and futures market does not working efficiently.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.37-44
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• 2016

The demand for electricity has a considerable impact on various energy sectors since electricity is generated from various energy sources. This paper attempts to estimate the electricity demand function and obtain some quantitative information on price and income elasticities of the demand. To this end, we apply a lagged dependent variable model to derive long-run as well as short-run elasticities using the time-series data over the period 1991-2014. Our dependent variable is annual electricity demand. The independent variables include constant term, real price of electricity, and real gross domestic product. The results show that the short-run price and income elasticities of the electricity demand are estimated to be -0.142 and 0.866, respectively. They are statistically significant at the 5% level. That is, the electricity demand is in-elastic with respect to price and income changes in the short-run. The long-run price and income elasticities of the electricity demand are calculated to be -0.210 and 1.287, respectively, which are also statistically meaningful at the 5% level. The electricity demand is still in-elastic with regard to price change in the long-run. However, the electricity demand is elastic regarding income change in the long-run. Therefore, this indicates that the effect of demand-side management policy through price-control is restrictive in both the short- and long-run. The growth in electricity demand following income growth is expected to be more remarkable in the long-run than in the short-run.

• 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.

• International Journal of Highway Engineering
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• v.9 no.3
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• pp.1-12
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• 2007

Roads in Korea can be classified into four types according to their responsible authorities. For example, Motorway is constructed, managed, and operated by the Korea Highway Corporation. Ministry of Construction and Transportation is in charge of National Highway, and Province Roads are run by each province government. Urban/county Roads are run by corresponding local government. This study analyses the trends of road accidents for each road type. For this purpose, the numbers of accidents, fatalities, and injuries are compared for each road type for last 15 years. The result shows that Urban/County Roads are the most dangerous, while Motorways are the safest, when we simply compare the numbers of accidents, fatalities, and injuries. However, when we compare these numbers by dividing by total road length, National Highway becomes the most dangerous while Province Roads becomes the safest. In the case of road accidents, fatalities, and injuries per vehicle km, which is known as the most objective comparison measure, it turns out that National Highway is the most dangerous roads again. This study also developed time series models to estimate trends of fatalities for each road type. These models will be useful when we set up or evaluate targets of national road safety.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1001-1019
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• 2017

The signal processing is a field of the electrical engineering but it is very much related with the time series analysis. Thesedays the commercial softwares are widely used by the reseachers. We have attempted to make a guide for using the R-package in the digital signal processing. It would be good to read the materials in each section first and to follow the plots in the section 8 and to run the attached R-codes. The article consists of (1) Fourier transform and Fourier inverse transform, (2) spectral analysis (3) parametric and non-parametric estimation for the period (4) filter design. Simple theoretical explanations are provided and R implementations are added.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.3
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• pp.136-145
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• 2006

SWAT includes a lot of parameters related with geography, hydrological time series, land management and water pollution, etc. So, it needs many spatial, non-spatial and time series data to run SWAT. If SWAT is operated in conjunction with GIS, we can use database which includes model input data and do all the processes which covers data creation, model input and analysis of simulation results in a system. The objective of this study is to develop HyGIS-SWAT which is the interface system to couple the SWAT model and HyGIS. To achieve this object, system operation process based on HyGIS-SWAT data model is evaluated and databases are designed and established. As a result, HyGIS-SWAT prototype system is developed. HyGIS data model and HyGIS-Model operation process can be applied effectively to the development of HyGIS-SWAT. The technologies from this study can be used as base technology to develop another HyGIS application which connect HyGIS with models.

• International Area Studies Review
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• v.22 no.3
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• pp.67-84
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• 2018

The purpose of this paper is to investigate the Balassa-Samuelson effect that real exchange rate could deviate from its long-run equilibrium. To analyze this effect, I estimated the long-run relationship between real exchange and productivity using the dynamic panel ordinary least square(DOLS) and panel error correction model(ECM) after conducting the unit root and cointegration test. The results show that all variables except for the real exchange rate have the unit root. Then I conducted the cointegration test to find out whether there exist the stable long-run relationships. The results show that the variables are cointegrated and significant statistically. The DOLS and ECM methods are used to estimate the coefficient of the cointegrated variables. The major finding are that the estimates are statistically significant and that they show the same sign as the economic theory predicts.