• Title/Summary/Keyword: short-rate models

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Estimation of VaR Using Extreme Losses, and Back-Testing: Case Study (극단 손실값들을 이용한 VaR의 추정과 사후검정: 사례분석)

  • Seo, Sung-Hyo;Kim, Sung-Gon
    • The Korean Journal of Applied Statistics
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    • v.23 no.2
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    • pp.219-234
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    • 2010
  • In index investing according to KOSPI, we estimate Value at Risk(VaR) from the extreme losses of the daily returns which are obtained from KOSPI. To this end, we apply Block Maxima(BM) model which is one of the useful models in the extreme value theory. We also estimate the extremal index to consider the dependency in the occurrence of extreme losses. From the back-testing based on the failure rate method, we can see that the model is adaptable for the VaR estimation. We also compare this model with the GARCH model which is commonly used for the VaR estimation. Back-testing says that there is no meaningful difference between the two models if we assume that the conditional returns follow the t-distribution. However, the estimated VaR based on GARCH model is sensitive to the extreme losses occurred near the epoch of estimation, while that on BM model is not. Thus, estimating the VaR based on GARCH model is preferred for the short-term prediction. However, for the long-term prediction, BM model is better.

Dam Inflow Forecasting for Short Term Flood Based on Neural Networks in Nakdong River Basin (신경망을 이용한 낙동강 유역 홍수기 댐유입량 예측)

  • Yoon, Kang-Hoon;Seo, Bong-Cheol;Shin, Hyun-Suk
    • Journal of Korea Water Resources Association
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    • v.37 no.1
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    • pp.67-75
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    • 2004
  • In this study, real-time forecasting model(Neural Dam Inflow Forecasting Model; NDIFM) based on neural network to predict the dam inflow which is occurred by flood runoff is developed and applied to check its availability for the operation of multi-purpose reservoir Developed model Is applied to predict the flood Inflow on dam Nam-Gang in Nak-dong river basin where the rate of flood control dependent on reservoir operation is high. The input data for this model are average rainfall data composed of mean areal rainfall of upstream basin from dam location, observed inflow data, and predicted inflow data. As a result of the simulation for flood inflow forecasting, it is found that NDIFM-I is the best predictive model for real-time operation. In addition, the results of forecasting used on NDIFM-II and NDIFM-III are not bad and these models showed wide range of applicability for real-time forecasting. Consequently, if the quality of observed hydrological data is improved, it is expected that the neural network model which is black-box model can be utilized for real-time flood forecasting rather than conceptual models of which physical parameter is complex.

Development of Water Demand Forecasting Simulator and Performance Evaluation (단기 물 수요예측 시뮬레이터 개발과 예측 알고리즘 성능평가)

  • Shin, Gang-Wook;Kim, Ju-Hwan;Yang, Jae-Rheen;Hong, Sung-Taek
    • Journal of Korean Society of Water and Wastewater
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    • v.25 no.4
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    • pp.581-589
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    • 2011
  • Generally, treated water or raw water is transported into storage reservoirs which are receiving facilities of local governments from multi-regional water supply systems. A water supply control and operation center is operated not only to manage the water facilities more economically and efficiently but also to mitigate the shortage of water resources due to the increase in water consumption. To achieve the goal, important information such as the flow-rate in the systems, water levels of storage reservoirs or tanks, and pump-operation schedule should be considered based on the resonable water demand forecasting. However, it is difficult to acquire the pattern of water demand used in local government, since the operating information is not shared between multi-regional and local water systems. The pattern of water demand is irregular and unpredictable. Also, additional changes such as an abrupt accident and frequent changes of electric power rates could occur. Consequently, it is not easy to forecast accurate water demands. Therefore, it is necessary to introduce a short-term water demands forecasting and to develop an application of the forecasting models. In this study, the forecasting simulator for water demand is developed based on mathematical and neural network methods as linear and non-linear models to implement the optimal water demands forecasting. It is shown that MLP(Multi-Layered Perceptron) and ANFIS(Adaptive Neuro-Fuzzy Inference System) can be applied to obtain better forecasting results in multi-regional water supply systems with a large scale and local water supply systems with small or medium scale than conventional methods, respectively.

Study on Optimization of Operating Conditions for High Temperature PEM Fuel Cells Using Design of Experiments (실험계획법을 이용한 고온 고분자 전해질 막 연료전지의 운전조건 최적화 연구)

  • Kim, Jintae;Kim, Minjin;Sohn, Youngjun
    • Transactions of the Korean hydrogen and new energy society
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    • v.24 no.1
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    • pp.50-60
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    • 2013
  • High temperature proton exchange membrane fuel cells (PEMFCs) using phosphoric acid (PA) doped polybenzimidazole (PBI) membranes have been concentrated as one of solutions to the limits with traditional low temperature PEMFCs. However, the amount of reported experimental data is not enough to catch the operational characteristics correlated with cell performance and durability. In this study, design of experiments (DOE) based operational optimization method for high temperature PEMFCs has been proposed. Response surface method (RSM) is very useful to effectively analyze target system's characteristics and to optimize operating conditions for a short time. Thus RSM using central composite design (CCD) as one of methodologies for design of experiments (DOE) was adopted. For this work, the statistic models which predict the performance and degradation rate with respect to the operating conditions have been developed. The developed performance and degradation models exhibit a good agreement with experimental data. Compared to the existing arbitrary operation, the expected cell lifetime and average cell performance during whole operation could be improved by optimizing operating conditions. Furthermore, the proposed optimization method could find different new optimal solutions for operating conditions if the target lifetime of the fuel cell system is changed. It is expected that the proposed method is very useful to find optimal operating conditions and enhance performance and durability for many other types of fuel cell systems.

A Long Run Classical Model of Price Determination (한국(韓國)의 물가모형(物價模型))

  • Park, Woo-kyu;Kim, Se-jong
    • KDI Journal of Economic Policy
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    • v.14 no.4
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    • pp.3-26
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    • 1992
  • The pupose of this paper is to construct a price determination model of the Korean economy and to find out the propogation mechanism of monetary and fiscal policies. The model is a small-size macroeconometric model consisted of ten core equations : consumption, investment, exports, imports, consumer price index, wage rate, corporate bond rate, potential GNP, capital stock, and GNP identity. The model is a Keynesian model : consumer price index is determined by markup over costs, and wage rate is expressed by Phillipse curve ralation. Two features of the model, however, distinguish this model from other macroeconometric models of the Korean economy. First of all, the estimation of potential GNP and the capital stock is endogenized as suggested by Haque, Lahiri, and Montiel (1990). This allows us to calculate the level of excess demand, which is defined as the difference between the actual GNP and the potential GNP. Second, interest rate, inflation and wages are all estimated as endogenous variables. Moreover, all quantity variables include price variables as important determinants. For instance, interest rate is an important determinant of consumption and investment. Exports and imports are determined by the real effective exchange rate. These two features make the interactions between excess demand and prices the driving forces of this model. In the model, any shock which affects quantity variable(s) affects excess demand, which in turn affects prices. This strong interaction between prices and quantities makes the model look like a classical model over the long run. That is, increases in money supply, government expenditures, and exchange rate (the price of the U.S. dollar in terms of Korean won) all have expansionery effects on the real GNP in the short run, but prices, wage, and interest rate all increase as a result. Over the long run, higher prices have dampenning effects on output. Therefore the level of real GNP turns out to be not much different from the baseline level ; on the other hand, the rates of inflation, wage and interest rate remain at higher levels.

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Investigating the Au-Cu thick layers Electrodeposition Rate with Pulsed Current by Optimization of the Operation Condition

  • Babaei, Hamid;Khosravi, Morteza;Sovizi, Mohamad Reza;Khorramie, Saeid Abedini
    • Journal of Electrochemical Science and Technology
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    • v.11 no.2
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    • pp.172-179
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    • 2020
  • The impact of effective parameters on the electrodeposition rate optimization of Au-Cu alloy at high thicknesses on the silver substrate was investigated in the present study. After ensuring the formation of gold alloy deposits with the desired and standard percentage of gold with the cartage of 18K and other standard karats that should be observed in the manufacturing of the gold and jewelry artifacts, comparing the rate of gold-copper deposition by direct and pulsed current was done. The rate of deposition with pulse current was significantly higher than direct current. In this process, the duty cycle parameter was effectively optimized by the "one factor at a time" method to achieve maximum deposition rate. Particular parameters in this work were direct and pulse current densities, bath temperature, concentration of gold and cyanide ions in electrolyte, pH, agitation and wetting agent additive. Scanning electron microscopy (SEM) and surface chemical analysis system (EDS) were used to study the effect of deposition on the cross-sections of the formed layers. The results revealed that the Au-Cu alloy layer formed with concentrations of 6gr·L-1 Au, 55gr·L-1 Cu, 24 gr·L-1 KCN and 1 ml·L-1 Lauryl dimethyl amine oxide (LDAO) in the 0.6 mA·cm-2 average current density and 30% duty cycle, had 0.841 ㎛·min-1 Which was the highest deposition rate. The use of electrodeposition of pure and alloy gold thick layers as a production method can reduce the use of gold metal in the production of hallow gold artifacts, create sophisticated and unique models, and diversify production by maintaining standard karats, hardness, thickness and mechanical strength. This will not only make the process economical, it will also provide significant added value to the gold artifacts. By pulsating of currents and increasing the duty cycle means reducing the pulse off-time, and if the pulse off-time becomes too short, the electric double layer would not have sufficient growth time, and its thickness decreases. These results show the effect of pulsed current on increasing the electrodeposition rate of Au-Cu alloy confirming the previous studies on the effect of pulsed current on increasing the deposition rate of Au-Cu alloy.

Development of Pressure Drop Model for the Compartment in Reactor Containment (격납용기내 구분방사이의 압력 강하 계산모델 개발)

  • Park, Cheol;Song, In-ho;Lee, Un-Chul
    • Nuclear Engineering and Technology
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    • v.18 no.3
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    • pp.183-193
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    • 1986
  • Full scale HDR containment experiment series pointed out that the previous containment analysis models have a number of shortcomings. One of them is on the calculational model of short term (0~2sec) pressure difference. The pressure differences between subcompartments are dependent on the flow rate, fluid density, head loss coefficient, and flow area ratio. It, however, is not known that any of them is largely attributed to the disagreement of pressure difference between the measured and the calculated values. In this study, the head loss coefficients are expressed with another form to improve the analytic model. The pressure and the pressure difference are evaluated by using COMPARE code with new correlation, and the results show better agreements with experimental values for V.42 test, but overestimate the measured values for V, 43 and underestimate for V.44.

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The Analysis of the Relation between Regional Industrial Diversity and Regional Business Cycle (지역의 산업다양성과 지역경기변동의 관계 분석)

  • Woo, Youngjin;Kim, Euijune
    • Journal of the Korean Regional Science Association
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    • v.33 no.3
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    • pp.3-19
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    • 2017
  • The purpose of this paper is to analyze the impacts of regional industrial diversity on regional business cycle response to national volatility. We employed mean group and pooled mean group estimators of panel vector error-correction models in order to control unobserved heterogeneity of the port cities, such as Pusan, Ulsan and Incheon. The results show that in various industrial regions, short-term fluctuations in the unemployment rate are small compared to other regions. On the contrary, long-term volatility of manufacturing production index is low in those regions.

Ultra Wideband (UWB) - Introduction and Signal Modeling

  • Manandhar, Dinesh;Shibasaki, Ryosuke
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.1421-1423
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    • 2003
  • Ultra Wideband is a new technology from commercial or civilian application viewpoint. It uses already allocated radio spectrum without causing significant interference to other users. It uses very low power, which is below the thermal noise of the receiver and is inherently difficult to detect by un-intentional users. Since, FCC approved the regulation for the commercial use of UWB in February 2002, the development of UWB technology is drastically gaining momentum. However, the technology itself is not new. It has already been used in military applications. UWB has three basic areas of applications, which are communication, positioning and imaging (UWB Microwave). The main commercial application will be for communication since it has very high data transfer rate for short distance. It can also be used for both indoor and outdoor 3-D positioning. Another important application is imaging like microwave remote sensing. An UWB sensor can pass through doors and walls and hence detect the objects inside the room. In this paper, we will introduce about UWB technology along with it’s various possible applications. We will also present some models to generate UWB signal and it’s analysis using signal-processing tools.

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Removal and Regrowth Inhibition of Microcystis aeruginosa using Artemisia asiatica Extracts (쑥 추출액을 이용한 Microcystis aeruginosa 제거 및 성장억제 연구)

  • Choi, Hee-Jeong
    • Journal of Korean Society on Water Environment
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    • v.33 no.4
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    • pp.441-448
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    • 2017
  • Microcystis aeruginosa (M. aeruginosa) is a cyanobacterium species that can form harmful algal blooms in freshwater bodies worldwide. The use of Artemisia asiatica extracts to control M. aeruginosa inhibition will be environmentally friendly and promising. Artemisia asiatica extracts removed successfully upto 88% of M. aeruginosa pH 8 at $25^{\circ}C$ of temperature. These results was indicated that the amount of 2.24 g/L Artemisia asiatica extracts was removed 1g dryweight/L of M. aeruginosa. The kinetic data showed substrate inhibition kinetics and maximum growth rate was obtained when the M. aeruginosa was grown in medium containing 2.5 g/L of initial concentration of Artemisia asiatica extracts. In the various growth control models, Luong model showed the highest correlation coefficient of 0.9916. Therefore, the Luong model was the most suitable control model for the growth control of M. aruginosa using Artemisia asiatica extracts. In conclusion, the growth control of M. aruginosa using Artemisia asiatica extracts can be applied in the field without controlling the temperature and pH of rivers and streams, and it is possible to control the growth of M. aruginosa efficiently in a short time. The natural extract, Artemisia asiatica extracts, can be a promising inhibition due to its high efficiency and low dose requirements.