• 한국경영과학회지
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• 제37권3호
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• pp.135-150
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• 2012

This paper presents a portfolio model for a long-term power generation mix problem. The proposed portfolio model evaluates generation mix by considering the tradeoffs between the expected cost for power generation and its variability. Unlike conventional portfolio models measuring variance, we introduce Conditional Value-at-Risk (CVaR) in designing the variability with aims to considering events that are enormously expensive but are rare such as nuclear power plant accidents. Further, we consider uncertainties associated with future electricity demand, fuel prices and their correlations, and capital costs for power plant investments. To obtain an objective generation by each energy source, we employ the sample average approximation method that approximates the stochastic objective function by taking the average of large sample values so that provides asymptotic convergence of optimal solutions. In addition, the method includes Monte Carlo simulation techniques in generating random samples from multivariate distributions. Applications of the proposed model and method are demonstrated through a case study of an electricity industry with nuclear, coal, oil (OCGT), and LNG (CCGT) in South Korea.

• 대한수학회논문집
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• 제18권1호
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• pp.127-131
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• 2003

Let X$_1$, X$_2$,... be a sequence of independent and identically distributed random variables with continuous cumulative distribution function F(x). X$_j$ is an upper record value of this sequence if X$_j$ > max {X$_1$,X$_2$,...,X$_{j-1}$}. We define u(n)=min{j$\mid$j> u(n-1), X$_j$ > X$_{u(n-1)}$, n $\geq$ 2} with u(1)=1. Then F(x) = 1-x$^{\theta}$, x > 1, ${\theta}$ < -1 if and only if (${\theta}$+1)E[X$_{u(n+1)}$$\mid$X$_{u(m)}$=y] = ${\theta}E[X_{u(n)}$\mid$X_{u(m)}=y], (\theta+1)^2E[X_{u(n+2)}$\mid$X_{u(m)}=y] = \theta^2E[X_{u(n)}$\mid$X_{u(m)}=y], or (\theta+1)^3E[X_{u(n+3)}$\mid$X_{u(m)}=y] = \theta^3E[X_{u(n)}$\mid$X_{u(m)}=y], n $\geq$ M+1$.

• 대한수학회논문집
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• 제17권3호
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• pp.535-540
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• 2002

Let X$_1$, X$_2$‥‥,X$\_$n/ be n independent and identically distributed random variables with continuous cumulative distribution function F(x). Let us rearrange the X's in the increasing order X$\_$1:n/ $\leq$ X$\_$2:n/ $\leq$ ‥‥ $\leq$ X$\_$n:n/. We call X$\_$k:n/ the k-th order statistic. Then X$\_$n:n/ - X$\_$n-1:n/ and X$\_$n-1:n/ are independent if and only if f(x) = 1-e(equation omitted) with some c > 0. And X$\_$j/ is an upper record value of this sequence lf X$\_$j/ > max(X$_1$, X$_2$,¨¨ ,X$\_$j-1/). We define u(n) = min(j|j > u(n-1),X$\_$j/ > X$\_$u(n-1)/, n $\geq$ 2) with u(1) = 1. Then F(x) = 1 - e(equation omitted), x > 0 if and only if E[X$\_$u(n+3)/ - X$\_$u(n)/ | X$\_$u(m)/ = y] = 3c, or E[X$\_$u(n+4)/ - X$\_$u(n)/|X$\_$u(m)/ = y] = 4c, n m+1.

• 아태비즈니스연구
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• 제12권1호
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• pp.121-134
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• 2021

Purpose - This study examines whether flight-to-liquidity (FTL) explains the dynamic liquidity risk on stock returns, and whether it has a significant influence on determinants the cross-section of stock returns. Design/methodology/approach - This study suggests a new risk factor, dynamic liquidity hedge portfolio (DLP), to reflect the dynamic impact of liquidity risk on stock returns and the Fama-MacBeth 2 stage regression analysis is employed in order to analyze the data. Findings - First, the DLP factor shows more positive and significant beta for the small or illiquidity stocks. Second, the DLP shows a different influence than SMB (size risk factor), HML (value risk factor), NMP (liquidity risk factor), FTVOL (total volatility factor) in determining the cross-section of stock returns. In addition, the DLP has a statistically significant risk premium of around 5%, which is relatively larger than other risk factors. Research implications or Originality - This study has academic value in terms of newly confirming that the DLP factor has a more significant impact on cross-sectional determination of stock returns than other risk factors by proposing a conditional liquidity factor that can explain the FTL phenomenon.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.857-861
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• 2021

The study aims to find the asymmetric effect in National Stock Exchange in which the Nifty50 is considered as proxy for NSE. A return can be stated as the change in value of a security over a certain time period. Volatility is the rate of change in security value. It is an arithmetical assessment of the dispersion of yields of security prices. Stock prices are extremely unpredictable and make the investment in equities risky. Predicting volatility and modeling are the most profuse areas to explore. The current study describes the association between two variables, namely, stock yields and volatility in equity market in India. The volatility is measured by employing asymmetric GARCH technique, i.e., the EGARCH (1,1) tool, which was used in building the study. The closing prices of Nifty on day-to-day basis were used for analysis from the period 2011 to 2020 with 2,478 observations in the study. The model arrests the lopsided volatility during the mentioned period. The outcome of asymmetric GARCH model revealed the subsistence of leverage effect in the index and confirms the impact of conditional variance as well. Furthermore, the EGARCH technique was evidenced to be apt in seizure of unsymmetrical volatility.

• 한국통신학회논문지
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• 제34권6C호
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• pp.588-592
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• 2009

본 논문에서는 기존의 global soft decision 방법에서 음성부재확률의 고정 파라미터에 2차 조건 사후 최대 확률기법을 적용한 음성 향상 기법을 제안한다. 기존의 global soft decision 방법은 음성부재확률을 구하기 위해 가정한 가설에 따라 파라미터값을 고정하여 다양한 음성 환경 변화에 민감한 점을 고려하여 본 논문에서 제안한 알고리즘은 기존의 고정 파라미터 값에 직전 2 프레임에서의 음성 존재와 부재에 대한 조건을 부여해주어 음성과 음성사이의 상호 연관성을 고려해주고, 보다 유동적으로 현재 프레임의 음성부재확률을 추정하는 음성향상 기법이다. 제안된 방법의 성능평가를 위해 ITU-T P.862 perceptual evaluation of speech quality (PESQ)를 이용하여 평가하였고, 그 결과 제안된 2차 조건 사후 최대 확률기법을 적용한 global soft decision 방법은 기존의 Global soft decision 방법보다 향상된 결과를 나타내었다.

• 응용통계연구
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• 제25권1호
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• pp.29-43
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• 2012

주식, 환율 등과 같은 금융자료의 수익률의 분포는 정규분포에 비해 꼬리가 두껍고, 좌우 비대칭성을 보인다. 조건부수익률이 정규분포를 따른다고 가정한 GARCH 모형을 이용하여 VaR을 추정하였을 때, 이러한 비정규성 때문에 적절한 추정이 이루어지지 않고, VaR을 초과하는 손실의 발생과정에 군집(clustering)현상이 발생하는 문제점이 있다. 이러한 문제를 해결하기 위해, 본 논문에서는 조건부수익률의 분포로 unbounded Johnson 분포를 이용한 GARCH 모형을 이용하여 VaR을 추정한다. 또한, 조건부수익률이 각각 정규분포, Student-t 분포를 따르는 GARCH 모형의 경우와 비교하였다. 초과손실 발생과정 자료를 이용하여 실패율검정과 군집성검정을 통해 조건부수익률 분포로 unbounded-Johnson 분포를 사용하는 방법의 타당성을 살펴보았다. Unbounded Johnson 분포가 조건부수익률 분포로 주어지는 GARCH 모형의 경우는 과소, 과대추정을 하지 않고, 군집현상 또한 발생하지 않아 적절한 추정을 하고 있음을 확인하였다.

• 응용통계연구
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• 제28권5호
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• pp.1013-1024
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• 2015

본 논문에서는 다변량 DCC(dynamic conditional correlation) GARCH 모형에서 동태적 상관계수를 추정하기 위한 대표적 방법인 쌍별 추정법과 다차원 추정법의 효율성을 비교한다. 이를 위하여 금융 시장의 변동성을 반영하는 다변량 시계열을 생성하고 이에 대한 DCC GARCH 모형을 수립 및 추정하는 시뮬레이션을 실시하였다. 또한 KOSPI 200 섹터지수를 이용하여 포트폴리오를 구성하고 이의 변동성 추정 및 VaR 계산을 통하여 동태적 상관계수 추정에 대한 정확성을 평가하였다. 그 결과로서, 전반적으로 다차원 추정법이 쌍별 추정법보다 우수함을 발견하였다. 특히, 다차원 추정법에서 상대적으로 상관관계가 낮은 시계열을 추가할수록 쌍별 시계열에 대한 동태적 상관계수 추정의 정확성을 높여줌을 발견하였다.

• 한국연소학회지
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• 제14권3호
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• pp.29-36
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• 2009

Simulation is performed to analyze the characteristics of turbulent spray combustion in a diesel engine condition. An extended Conditional Moment Closure (CMC) model is employed to resolve coupling between chemistry and turbulence. Relevant time and length scales and dimensionless numbers are estimated at the tip and the mid spray region during spray development and combustion. The liquid volume fractions are small enough to support validity of droplets assumed as point sources in two-phase flow. The mean scalar dissipation rates (SDR) are lower than the extinction limit to show flame stability throughout the combustion period. The Kolmogorov scales remain relatively constant, while the integral scales increase with decay of turbulence. The chemical time scale decreases abruptly to a small value as ignition occurs with subsequent heat release. The Da and Ka show opposite trends due to variation in the chemical time scale. More work is in progress to identify the spray combustion regimes.

• 대한산업공학회지
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• 제41권4호
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• pp.338-343
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• 2015

We suggest a method to configure feature functions of continuous conditional random field (C-CRF). Regression tree and similarity analysis are introduced to construct the first and second feature functions of C-CRF, respectively. Rules from the regression tree are transformed to logic functions. If a logic in the set of rules is true for a data then it returns the corresponding value of leaf node and zero, otherwise. We build an Euclidean similarity matrix to define neighborhood, which constitute the second feature function. Using two feature functions, we make a C-CRF model and an illustrate example is provided.