• The Korean Journal of Financial Studies
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• v.5 no.1
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• pp.165-190
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• 1999

본 논문에서는 KOSPI 200 주가지수 선물의 만기효과와 베이시스의 행태를 체계적으로 헤지 의사결정에 반영하기 위한 몇 가지 방법을 실증분석하였다. 우선 베이시스의 동태적 운동형태를 명시적으로 설정하지 않고 통계적인 방법을 통하여 헤지해제시점이 선물만기에 접근함에 따라 베이시스가 변동되는 양상을 반영한 헤지비율을 산출한다. 그 다음에는 헤지기간 전체에 걸친 베이시스의 운동형태를 명시적으로 설정하여 이에 입각한 헤지비율을 계산한다. 명시적인 베이시스의 운동형태는 비확률적인 과정과 확률적인 과정으로 다시 구분하고, 이 각각에 입각하여 최적헤지활동을 결정한다. 모든 헤지활동은 가장 최근까지의 정보를 이용하여 사전적으로 미래 헤지기간에 대한 의사결정을 하게 된다. 그러한 헤지활동의 사후적인 결과는 베이시스 행태를 별도로 고려하지 않고 단순선형회귀분석만을 이용하여 산출된 헤지성과와 비교되고, 변동성 감소 및 손실감소의 측면에서 각 접근방법이 가지는 특징 및 효율성을 평가한다. 실증 분석 결과, 헤지의 성과를 제고하기 위하여 선물의 만기효과와 베이시스의 행태변화를 체계적으로 반영한 세 가지의 시도 중 어느 것도 위험-수익의 2차원적인 비교에서 베이시스의 행태변화를 명시적으로 반영하지 않은 전통적 단순회귀분석을 압도하지 못하였다.

• Geotechnical Engineering
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• v.35 no.1
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• pp.14-22
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• 2019

• Management & Information Systems Review
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• v.35 no.2
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• pp.273-284
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• 2016

The objective of this study is to analyze the market efficiency in the futures markets. Although many previous studies have investigated market efficiency between spot and futures prices, that with different maturities has not been studied in the futures markets extensively. For our objective, this paper examines KOSPI200 stock index future market with different maturities. We analyze the dynamic serial relationship of the difference of basis between nearest-month contract and next nearest-month contract using dynamic regression analysis suggested by Kawamoto and Hamori(2011) Using the data from 2000. 1 to 2013. 12, the major empirical findings are as follows: First. the mean and standard deviation of basis of next nearest-month contract is bigger than those of nearest-month contract. Second, the t-period basis of nearest-month contract can be explained by (t-1)period basis of that. Third, the basis spread of t-period and (t-1)period have negative affect on the return of underlying assets. This result is very reasonable because two basis spreads are derived from same underlying assets. Finally, basis information of next nearest-month contract can be used for the prediction of nearest-month contract and spot market return.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.295-300
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• 2015

In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

• The Journal of the Acoustical Society of Korea
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• v.23 no.5
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• pp.410-419
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• 2004

In this paper. we propose a method of confidence measure fusion under Bayesian framework for speech recognition. Centralized and distributed schemes are considered for confidence measure fusion. Centralized fusion is feature level fusion which combines the values of individual confidence scores and makes a final decision. In contrast. distributed fusion is decision level fusion which combines the individual decision makings made by each individual confidence measuring method. Optimal Bayesian fusion rules for centralized and distributed cases are presented. In isolated word Out-of-Vocabulary (OOV) rejection experiments. centralized Bayesian fusion shows over 13% relative equal error rate (EER) reduction compared with the individual confidence measure methods. In contrast. the distributed Bayesian fusion shows no significant performance increase.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.83-87
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• 2011

Naive Bayesian learning has been widely used in machine learning. However, in traditional naive Bayesian learning, we make two assumptions: (1) each attribute is independent of each other (2) each attribute has same importance in terms of learning. However, in reality, not all attributes are the same with respect to their importance. In this paper, we propose a new paradigm of calculating the importance of attributes for naive Bayesian learning. The performance of the proposed methods has been compared with those of other methods including SBC and general naive Bayesian. The proposed method shows better performance in most cases.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.359-366
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• 2022

While the Forward Freight Agreement (FFA) has emerged as an effective hedging tool since early 1990, the basis risk and cash flow distortions have been addressed as obstacles to the active use of FFAs. This research analyses the basis risk of FFAs and provides a feasible suggestion to reduce it. Basis risk is divided into timing basis, route basis, size basis, and low liquidity basis. The timing basis is defined as the difference between the physical hire, fixed on the specific contract date and the FFA settlement price, calculated by averaging spot rates for a certain period. Timing basis is considered the worst in eroding the effectiveness of FFAs. This paper suggests a change of hire payment criterion from contract date to 15-day moving average, as a means of mitigating the basis risk, and analyzed the effectiveness through historical simulation. The result revealed that the change is effective in mitigating the timing basis. This study delivers a meaningful implication to shipping practice in that the change of hire payment criterion mitigates the basis risk and eventually activates the use of FFAs in the future.

• Journal of KIBIM
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• v.12 no.1
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• pp.23-34
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• 2022

In the current 2D-based design, design reliability is lowered due to interference and inconsistency between plans, errors in drawings and quantities, etc. At the time of transition to BIM-based 3D design, it is necessary to expand the reliability and usability of BIM by eliminating these errors from the design stage through securing the quality of the BIM digital model. Therefore, in the railway infrastructure design stage, the quality management process and standards of the BIM digital model were defined and quality management index were developed. Based on the rule extracted from the quality management index, a pilot quality management was conducted in connection with the commercial Model-Checker rule, problems and improvement plans were derived, and a rule-based automated quality management plan was prepared.

• Electric Engineers Magazine
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• s.337
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• pp.12-15
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• 2010

대한민국 해외 건설 역사상 최대 규모($10억 달러)의 건축 프로젝트인 마리나 베이 샌즈 호텔은 그 규모 만큼이다 놀라운 기술력을 선보이고 있습니다.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.259-267
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• 2003

Though naive Bayes text classifiers are widely used because of its simplicity, the techniques for improving performances of these classifiers have been rarely studied. In this paper, we propose and evaluate some general and effective techniques for improving performance of the naive Bayes text classifier. We suggest document model based parameter estimation and document length normalization to alleviate the Problems in the traditional multinomial approach for text classification. In addition, Mutual-Information-weighted naive Bayes text classifier is proposed to increase the effect of highly informative words. Our techniques are evaluated on the Reuters21578 and 20 Newsgroups collections, and significant improvements are obtained over the existing multinomial naive Bayes approach.