• Journal of the Korean Data and Information Science Society
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• 제10권2호
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• pp.485-500
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• 1999

단계적 금리자율화의 시행을 계기로 금융계에서는 시장금리의 체계적 예측이 중요한 문제점으로 대두되고 있다. 금융의 자율화, 국제화, 대형화는 금융기관간의 경쟁유발과 금융시장의 판도에 심각한 변화를 초래하였다. 또한 시장금리의 변화는 금융기관의 수익에 결정적인 영향을 미친다. 따라서 대부분의 금융기관은 시장금리를 과학적이고 체계적으로 해석하기 위하여 금리결정요인에 대한 연구 및 향후 금리수준을 예측하기 위한 금리예측모형의 개발을 활발히 진행하고 있다. 본 논문에서는 시계열분석에 근거하여 예측의 정확도를 높이고 컴퓨터환경의 체계화로 사용의 편리성을 극대화한 금리예측 시스템을 개발하고 이의 활용도에 대해 논의하고자 한다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2000년도 추계학술대회 및 정기총회
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• pp.37-39
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• 2000

The purpose of this paper is to present a neural network model based on change-point detection for the prediction of the U.S. Treasury Securities. Interest rates have been studied by a number of researchers since they strongly affect other economic and financial parameters. Contrary to other chaotic financial data, the movement of interest rates has a series of change points due to the monetary policy of the U.S. government. The basic concept of this proposed model is to obtain intervals divided by change points, to identify them as change-point groups, and to use them in interest rates forecasting. The proposed model consists of three stages. The first stage is to detect successive change points in the interest rates dataset. The second stage is to forecast the change-point group with the backpropagation neural network (BPN). The final stage is to forecast the output with BPN. This study then examines the predictability of the integrated neural network model for interest rates forecasting using change-point detection.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• 충청수학회지
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• 제34권2호
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• pp.157-168
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• 2021

This study uses the benchmark interest rate of the Federal Open Market Committee (FOMC) to predict gold futures prices. For the predictions, we used the support vector machine (SVM) (a machine-learning model) and the long short-term memory (LSTM) deep-learning model. We found that the LSTM method is more accurate than the SVM method. Moreover, we applied the Boruta algorithm to demonstrate that the FOMC benchmark interest rates correlate with gold futures.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• 한국건축시공학회지
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• 제10권5호
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• pp.31-36
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• 2010

금리의 하락은 건설투자를 유도할 수 있다. 즉 금리는 건설 경기에 영향을 주는 요소 중의 하나이다. 본 연구의 목적은 연도별 건설수주액과 금리와의 관계를 분석하고 이를 통해 향후 건설경제를 예측하는 정량적인 모델을 제시하는 것이다. 이를 위하여 1991년부터 2009년까지 19년간의 자료를 바탕으로 금리와 건설수주액 상호간의 관계를 분석하고 금리와 건설수주액을 각각 종속변수와 독립변수로 하는 회귀식을 유도하여 향후 건설경기를 예측하는 방법을 제시한다. 결과적으로 수주총액, 건축, 민간 부문 등의 수주액은 3년 뒤의 금리와, 주택부문은 2년뒤의 금리와의 상관계수가 모두 0.85이상으로 매우 밀접한 관계가 있는 것으로 나타났다. 회귀분석을 통해 수주총액, 건축, 주택, 민간 부문에 대한 수주액을 예측하는 회귀식을 도출하여 적용한 결과 수주총액, 건축, 민간의 경우 2011년까지 수주액이 감소하고 2012년에는 증가하는 것으로 분석되었으며, 주택부문의 경우 2010년까지 수주액이 감소하고 2011년부터는 증가하는 것으로 분석되었다.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 추계학술대회
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• 무역학회지
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• 제47권5호
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• pp.21-37
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• 2022

Baltic Dry Index (BDI) is difficult to forecast because of the high volatility and complexity. To improve the BDI forecasting ability, this study apply Bayesian variable selection method with a large number of predictors. Our estimation results based on the BDI and all predictors from January 2000 to September 2021 indicate that the out-of-sample prediction ability of the ADL model with the variable selection is superior to that of the AR model in terms of point and density forecasting. We also find that critical predictors for the BDI change over forecasts horizon. The lagged BDI are being selected as an key predictor at all forecasts horizon, but commodity price, the clarksea index, and interest rates have additional information to predict BDI at mid-term horizon. This implies that time variations of predictors should be considered to predict the BDI.

• 대한산업공학회지
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• 제36권3호
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• pp.203-211
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• 2010

This study examines a cointegrated vector autoregressive (VAR) model where parameters are subject to switch across the regimes in the term structure of interest rates. To employ the regime switching framework, the Markov-switching vector error correction model (MS-VECM) is allowed to the regime shifts in the vector of intercept terms, the variance-covariance terms, the error correction terms, and the autoregressive coefficient parts. The corresponding approaches are illustrated using the term structure of interest rates in the US Treasury bonds over the period of 1958 to 2009. Throughout the modeling procedure, we find that the MS-VECM can form a statistically adequate representation of the term structure of interest rate in the US Treasury bonds. Moreover, the regime switching effects are analyzed in connection with the historical government monetary policy and with the recent global financial crisis. Finally, the results from the comparisons both in information criteria and in forecasting exercises with and without the regime switching lead us to conclude that the models in the presence of regime dependence are superior to the linear VECM model.