• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.15-21
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• 2014

The long memory properties of the hedge ratio for stock and futures have not been systematically investigated by the extant literature. To investigate hedge ratio' long memory, this paper employs a data set including KOSPI200 and S&P500. Coakley, Dollery, and Kellard(2008) employ a data set including a stock index and commodities foreign exchange, and suggested the S&P500 to be a fractionally integrated process. This paper firstly estimates hedge ratios with two dynamic models, BEKK(Bollerslev, Engle, Kroner, and Kraft) and diagonal-BEKK, and tests the long memory of hedge ratios with Geweke and Porter-Hudak(1983)(henceforth GPH) and Lo's modified rescaled adjusted range test by Lo(1991). In empirical results, two hedge ratios based on KOSPI200 and S&P500 show considerably significant long memory behaviours. Thus, such results show the hedge ratios to be stationary and strongly reject the random walk hypothesis on hedge ratios, which violates the efficient market hypothesis.

• Journal of Korea Water Resources Association
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• v.42 no.10
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• pp.809-824
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• 2009

Hydrological system forecasting, which is the short term runoff historical data during the limited period in dam site, is a conditional precedent of hydrological persistence by stochastic analysis. We have forecasted the monthly hydrological system from Andong dam basin data that is the rainfall, evaporation, and runoff, using the seasonal ARIMA (autoregressive integrated moving average) model. Also we have conducted long term runoff simulations through the forecasted results of TANK model and ARIMA+TANK model. The results of analysis have been concurred to the observation data, and it has been considered for application to possibility on the stochastic model for dam inflow forecasting. Thus, the method presented in this study suggests a help to water resource mid- and long-term strategy establishment to application for runoff simulations through the forecasting variables of hydrological time series on the relatively short holding runoff data in an object basins.

• The Korean Journal of Financial Management
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• v.18 no.1
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• pp.1-21
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• 2001

주가가 장기기억과정에 의하여 생성되면 주가과정에 가해진 충격은 쌍곡선감소율로 소멸한다. 따라서 충격의 영향이 대단히 느리게 감소하여 충격이 지속성을 가진다. 반면 주가가 단기 기억과정을 따르면 지수율로 감소하여 소멸한다. 지수율감소는 충격의 영향을 급속히 소멸시키므로 충격의 영향이 조만간 소멸한다. 따라서 충격으로 변화된 주가는 평균으로 회귀한다. 충격의 영향이 영원히 존재하는 과정도 존재한다. 장기기억과정은 쪽거리차분과정 또는 분수차분과정이다. 차분모수가 분수일 것이 요구되는 시계열은 장기기억과정이다. 주가가 장기기억과정에 의하여 생성되고 있는지의 여부를 검정하였다. 장기기억과정을 형성시키는 차분모수는 분수차분모수이다. 일별 주가지수의 수익률을 사용하여 차분모수를 추정하였는 바 그 값이 0에 근접하고 있음이 밝혀졌다. 그러나 Kospi, Nasdaq과 Mib30은 장기기억모수가 0에 접근하고 있으나 0이 아니다. 따라서 이 지수들은 장기기억과정에 의하여 생성된다고 할 수 있다. 반면 Dow Jones, S&P 500와 Dax는 장기기억모수가 0이라는 가설이 기각되지 않고 있어 이 지수들은 단기기억과정을 따르고 있다. 따라서 평균회귀과정에 의하여 생성되고 있음을 알 수 있다.

• 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.

• The Journal of Engineering Geology
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• v.32 no.2
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• pp.281-293
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• 2022

Time series analysis was performed on data from 2009 to 2018 from the Chuncheon groundwater observation network to understand the characteristics of groundwater level fluctuations in the network. There are five observatories, all of which are installed in rock aquifers, and periodic inspections and management are performed by the relevant operating organization. Auto-correlation, spectral density, and cross-correlation analysis was performed.

• 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.

• KDI Journal of Economic Policy
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• v.34 no.3
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• pp.187-214
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• 2012

Korea's national income statistics have been compiled by the Bank of Korea since 1953. However, there is a break in the time series. The current time series (1970 onward) is based on the '1993 SNA (System of National Accounts)' suggested by the UN, and the previous time series (1953~1970) was based on the '1953 SNA'. The difference between the previous and current time series is 4.8% in 1970 when the two series overlap. The difference is even greater in terms of comparisons across industries. In addition, it has now become even more difficult to connect the current and the previous time series because, in 2009, the Bank of Korea introduced a chain weighted method for calculating the current time series (1970 onward). Under the chain weighted method, the time series underwent substantial modification; for instance, the economic growth rate during 1970~2005 is 0.9%p higher than the rate under the general method. This paper applies chain weighted values and the '1993 SNA' to the previous time series (1953~1970) by utilizing various national account manuals published by the UN and previous Korean input-output tables in order to calculate a long term time series from 1953 to 2010 based on the same criteria as the current time series (1970 onward). In the revised time series, it appears that 1953 GDP at current basic prices is 3.5% higher and the growth rate for the period of 1953~1970 is 1.5%p higher each year than under the previous time series. Under the revised time series the size of the Korean economy as of 2010 is 50-fold bigger than that of 1953. In terms of industries, manufacturing and SOC show significant expansion whereas the extent of that of the service industry is relatively small.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.599-603
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• 2012

본 연구에서는 부정류 계산 모형을 이용한 확률 홍수량 및 홍수위 산정 방법을 개발하고, 이를 한강 살리기 사업이 진행 중인 남한강 구간에 적용하였다. 우선 한강 살리기 사업 전과 후의 하도에 대하여 부정류 계산 모형을 각각 수립하였으며, 과거 발생한 홍수사상을 조사하였다. 사업 전 모형과 최근에 발생한 홍수사상을 이용하여 모형의 보정 및 검증을 실시하고, 추정된 매개변수를 사업 후의 하도에 대한 모형에 적용하였다. 대상 유역에 과거 발생한 홍수사상을 사업 후 모형으로 모의하여 각 홍수사상 별로 최대 홍수량 및 홍수위를 계산하였다. 이때 최대 홍수량 모의 결과들을 빈도해석 대상 자료군으로 사용하여, 연최대치 계열이나 부분 시계열에 대하여 빈도해석을 통하여 확률 홍수량을 산정할 수 있다. 본 연구에서는 장기간의 관측자료의 확보가 어려운 국내의 현실을 고려하여, 부분 시계열의 빈도해석 방법을 사용하여 확률 홍수량을 산정하였다. 다음으로 부정류 계산모형의 모의 결과인 최대 홍수량 및 홍수위 자료를 회귀분석하여 수위-유량 관계식을 유도하고, 각 빈도별 확률 홍수량을 관계식에 대입하여 확률 홍수량에 대응하는 확률 홍수위를 산정하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.366-366
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• 2022

유역의 하천유량과 같은 수문 시계열을 모의 또는 예측하기 위한 수문 모델링에서 최근 기계 학습 방법을 활용한 연구가 활발하게 적용되고 있는 추세이다. 이러한 데이터 기반 모델링 접근법은 입출력 자료에서 관찰된 패턴을 학습하며, 특히, 장단기기억(Long Short-Term Memory, LSTM) 네트워크는 많은 연구에서 수문 시계열 예측에 대한 적용성이 검증되었으나, 장기간의 고품질 관측자료를 활용할 때 더 나은 예측성능을 보인다. 그러나 우리나라의 경우 장기간 관측된 고품질의 하천유량 자료를 확보하기 어려운 실정이다. 따라서 본 연구에서는 LSTM 네트워크의 학습 시 가용한 모든 유역의 자료를 통합하여 학습시켰을 때 하천유량 예측성능을 개선할 수 있는지 판단해보고자 하였다. 이를 위해, 우리나라 13개 댐 유역을 대상으로 대상 유역의 자료만을 학습한 모델의 예측성능과 모든 유역의 자료를 학습한 모델의 예측성능을 비교해 보았다. 학습은 2001년부터 2010년까지 기상자료(강우, 최저·최고·평균기온, 상대습도, 이슬점, 풍속, 잠재증발산)를 이용하였으며, 2011년부터 2020년에 대해 테스트 되었다. 다지점 통합학습을 통해 테스트 기간에 대해 예측된 각 유역의 일 하천유량의 KGE 중앙값이 0.74로 단일지점 학습을 통해 예측된 KGE(0.72)보다 다소 개선된 결과를 보여주었다. 다지점 통합학습이 하천유량 예측에 큰 개선을 달성하지는 못하였으며, 추가적인 가용 자료 확보와 LSTM 구성의 개선을 통해 추가적인 연구가 필요할 것으로 판단된다.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.529-545
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• 2023

This paper introduces the Banded-VHAR model suitable for high-dimensional long-memory time series with band structure. The Banded-VHAR model has nonignorable correlations only with adjacent dimensions due to data features, for example, geographical information. Row-wise estimation method is adapted for fast computation. Also, two estimation methods, namely BIC and ratio methods, are proposed to estimate the width of band. We demonstrate asymptotic consistency of our proposed estimation methods through simulation study. Real data applications to pm2.5 and apartment trading volume substantiate that our Banded-VHAR model outperforms traditional sparse VHAR model in forecasting and easy to interpret model coefficients.