• Journal of the Korean Geographical Society
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• v.39 no.6 s.105
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• pp.819-832
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• 2004

Daily rainfall data from 14 stations during 1941 to 2000 were analyzed in order to examine the characteristics of the variation of summer rainfall and the identify relationship between the variation of summer rainfall and the variation of SOI(Southern Oscillation Index) and NPI(North Pacific Index), global temperature. For further investigation, study period is divided into two 30 year intervals, 1941-1970 and 1971-2000. There are the trend of increase in August and decrease in September in the later period compared with the earlier one. It was Mid-west in August where there is the largest variation. It is related to the increase of the frequency of heavy rainfall. The second period of extreme rainfall by ten days is absent, or it change from early in September to late in August. According to the result, the dry spell in August disappears and Changma is continued to early in September. Gradually, there is change from negative (or positive) to positive (or negative) to the rainfall anomaly of the mid of August and the mid of September (or July). The correlation between the variation of rainfall and oceanic variation and global temperature is statistically significant.

• International Area Studies Review
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• v.14 no.2
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• pp.25-40
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• 2010

Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing and risk management because volatility is equal to market risk. So, we attempted to delineate a model with good ability to forecast and identified stylized features of volatility, with a focus on volatility persistence or long memory in the Australian futures market. In this context, we assessed the long-memory property in the volatility of index futures contracts using three conditional volatility models, namely the GARCH, IGARCH and FIGARCH models. We found that the FIGARCH model better captures the long-memory property than do the GARCH and IGARCH models. Additionally, we found that the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove a useful technique in forecasting the long-memory volatility in the Australian index futures market.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.85-100
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• 2023

The stochastic volatility (SV) model is one of the main methods of modeling time-varying volatility. In particular, SV model is actively used in estimation and prediction of financial market volatility and option pricing. This paper attempts to model the time-varying volatility of the bitcoin market price using SV model. Hidden Markov model (HMM) is combined with the SV model to capture characteristics of regime switching of the market. The HMM is useful for recognizing patterns of time series to divide the regime of market volatility. This study estimated the volatility of bitcoin by using data from Upbit, a cryptocurrency trading site, and analyzed it by dividing the volatility regime of the market to improve the performance of the SV model. The MCMC technique is used to estimate the parameters of the SV model, and the performance of the model is verified through evaluation criteria such as MAPE and MSE.

• Journal of the Korean Geotechnical Society
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• v.25 no.6
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• pp.73-88
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• 2009

Geotechnical variability is a complex feature that results from many independent sources of uncertainties, and is mainly affected by inherent variability and measurement errors. This study evaluates the coefficient of variation (COV) of soil properties and soil layers at Song-do region in Korea. Since soil variability is sensitive to soil layers and soil types, the Cays by soil layers (reclaimed layer and marine layer) and the COVs by soil types (clay and silt) were separately evaluated. It is observed that geotechnical variability of marine layer and clay is relatively smaller than that of reclamation layer and silt. And, the highly weathered rock and soil show the higher cays in the interpretation of the strength parameters of the fresh and weathered rock. And the proposed COV of Songdo area can be used for the reliability-based design procedure.

• Information and Communications Magazine
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• v.33 no.8
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• pp.52-62
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• 2016

해양에서 음파를 사용하여 수중통신을 시도할 경우 해양매질은 음향 도파관(acoustic waveguide)의 역할을 하게 되고, 이 경우 해양환경의 변동성과 그에 따른 음파와 매질의 간섭에 의해 수중통신 채널의 변동성이 발생한다. 수중음향 채널은 대역 제한 채널이면서 잔향음 제한 채널이고 강한 도플러 변이 채널이므로 수신된 통신 신호는 육상통신에 비해 심한 인접 심볼간 간섭(intersymbol interference)과 위상변이를 가지게 된다. 따라서 수중통신을 시도함에 있어 이러한 해양환경 변동성과 그에 따른 수중음향 채널 변동에 대한 충분한 고려가 필요하다. 본 논문은 수중통신 시스템 구성에 도움을 줄 수 있도록 수중통신 채널에 영향을 미치는 해양 매질의 기본적 특성에 대해 소개하고 수중통신 채널과의 상관성 및 환경 변동성에 따른 통신채널의 변동성에 대해 소개하고자 한다.

• Journal of Labour Economics
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• v.37 no.1
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• pp.87-116
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• 2014

Forecasting of short-term employment fluctuations provides a useful tool for policy makers in risk managing the labor market. Following the process of producing the composite leading indicator for macro economy, the paper develops the employment leading indicator(ELI) for the purpose of short-term forecasting non-farm payroll employment in private sectors. ELI focuses on early detecting the point of time and the speed in phase change of employment level.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.898-902
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• 2004

본 연구에서는 보천거로 인해 발생되는 보 상하류의 하상변동을 예측하여 보았다. 대상 지역은 최근 철거된 경안천 장지보 부근으로 양벌보에서 경안 1교까지 3.5km 구간이다. 흐름분석은 RMA-2를 이용하였으며 하상변동은 SED-2D모형을 이용하여 예측하였다. 모의 결과 보 철거로 인할 유속의 증가로 보 상류에서 침식이 발생하였고, 하류부는 유속 감소로 인할 최적이 발생하였다. 보 철거로 인한 하상변동의 영향이 보 주변에만 국한되지 않고 하류구간에 까지 영향을 미치는 것으로 나타났다. 그러나 이 모형은 장기하상변동에는 어려움이 있는 것으로 판단되어 보철거로 인한 2차원 장기하상변동을 예측하기 위해서는 새로운 모형의 개발이 요구된다.

• Environmental and Resource Economics Review
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• v.8 no.2
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• pp.227-244
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• 1999

환율의 과도한 변동이 무역량을 위축시키는지에 대하여 선진국을 대상으로 그 동안 많은 연구가 있어 왔다. 최근 한국은 1990년 이후 제한하여 오던 환율의 변동허용폭을 폐지함에 따라 환율의 과도한 변동을 경험하고 있다. 본 연구는 한국의 경우 환율의 변동성이 원유수입(原油輸入)에 미치는 효과를 장단기(長短期)로 구분하여 Johansen에 의하여 개발된 공적분기법으로 분석하였다. 분석결과에 의하면 단기에 있어서 환율의 변동성은 원유수입을 감소시키나 장기에는 원유수입에 큰 영향을 주지 않는 것으로 나타났다. 이는 단기에는 원유(原油)의 비축물량이 존재하여 가격의 불확실성이 원유수입을 감소시키나, 장기적으로는 원유수입이 비경쟁적 수입이고 수입기업이 환위험을 감소시키는 기법등을 사용한 결과로 환율의 변동성이 원유수입에 큰 영향을 주지 않았다고 할 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.139-139
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• 2018

국내 하천은 산업화와 도시화로 인하여 하천수가 오염되고, 치수를 위한 인공적, 획일적인 하천개수가 보편화 되었다. 그 결과 본래 하천이 가지고 있던 생물서식처 기능과 자정, 친수 기능 등 하천환경 기능이 점차 상실되었고, 하천형태도 변형되었다. 이와 같은 자연적인 변화와 더불어 준설, 수리구조물 설치 등 인위적인 변화에 의하여 흐름 및 유사이송 양상이 바뀌어 하상변동이 초래되기도 한다. 하상변동은 하천 시설물의 안정, 홍수위 및 지하수위 변화, 하천부지의 변화 등 하천관리에 중대한 영향을 미치고, 또한 수생태계의 서식환경에 영향을 미치기 때문에 하천을 복원하는데 있어 중요한 고려사항이 된다. 본 연구의 목적은 청미천 복원 사업 구간에 대하여 장기하상변동 모의를 수행하고 하도 안정성을 평가하는 것이다. 이를 위하여 구하도 복원구간에서 새롭게 수행된 측량데이터를 토대로 지형자료를 구축하였으며, 2차원 하상변동 모의가 가능한 CCHE2D 모형을 이용하여 장기하상변동 모의를 실시하였다. 또한 구하도 복원의 하천지형학적 영향을 평가하기 위하여 구하도를 복원하지 않았을 경우의 시나리오를 도입하여 비교하고 그 결과를 분석하였다.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.