• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.103-117
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• 2017

In longitudinal data analysis, the serial correlation of repeated outcomes must be taken into account using covariance matrix. Modeling of the covariance matrix is important to estimate the effect of covariates properly. However, It is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcome the restrictions, several Cholesky decomposition approaches for the covariance matrix were proposed: modified autoregressive (AR), moving average (MA), ARMA Cholesky decompositions. In this paper we review them and compare the performance of the approaches using simulation studies.

• The Korean Journal of Financial Management
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• v.17 no.1
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• pp.203-226
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• 2000

본 연구는 주가지수 선물시장이 도입된 1996년 5월 3일부터 1998년 12월 5일까지 1분 간격 KOSPI 200 선물가격과 현물가격의 거래자료를 이용하여 각 선물가격과 기초자산가격간의 관계와 상호작용을 검토하는데 있다. 특히 본 연구는 차익거래자나 초단기 투기자(scalper)들이 거래체결을 위해 촌각을 다투는 선물시장의 거래행태에서 볼 때, 경제적 의미를 부여할 수 있는 1분 간격 수익률 자료를 이용함으로써 시장참여자의 실제 거래에서 표출되는 정형화된 현상을 정확히 파악한다는 점에서 중요하다. 본 연구의 주요 결과를 제시하면 다음과 같다. 첫째, 주가지수 선물시장과 현물시장간에 체계적이고 긴 선도-지연 관계가 발견되었다. 주가지수 선물가격의 변화가 현물가격의 변화를 대략 26분 정도 선도하고 있으며, 대략 5분 정도 현물시장의 선도효과도 발견된다. 따라서 KOSPI 200 선물수익률과 현물수익률간의 선도-지연 관계는 한 시장에서 다른 시장으로의 일방적인 것이 아니라 시장간의 피드백(feedback)효과가 존재하며, 선물의 선도효과가 지배적인 것으로 보인다. 이러한 선도-지연 현상은 노이즈에 의한 비동시거래보다는 거래비용과 공매제약 차이 등 각 시장의 제도적 차이에 의해 발생하는 것으로 보여진다. 둘째, 약세시장 하에서 선물의 선도효과가 더욱 크게 나타났다. 이러한 현상은 약세시장 하에서 현물시장의 공매제약이 선물가격과 현물가격간의 괴리를 더욱 크게 하여 선물가격이 현물지수를 더욱 선도하게 하는 요인이 될 수도 있음을 나타내는 것이다. 셋째, 만기별 하위기간 중 97년 6월과 97년 12월을 제외한 기간은 선물과 현물가격간에 장기 안정적인 균형관계가 성립함을 발견하였다. 넷째, ARMA(p, q) 여과를 거친 선물과 현물수익률을 이용하여 97년 6월과 12월은 백터자기회귀(VAR)모형, 그 외의 기간은 오차수정(EC)모형으로 추정하였다. 표본전체기간동안 장기균형오차에 대한 조정은 선물과 현물시장에서 동시에 이루어지고 있으며, 시장간에 발생하는 불균형 상황은 아비트라지 거래로 조정되고 있음이 발견되었다. 각 만기별 모든 하위기간에 있어서는 시장간의 장기 불균형 상황이 현물시장을 통해서 조정되고 있으며, 시장이 성숙된 최근의 만기 98년 12월 하위기간에서는 선물의 15분 선도효과와 현물의 1분 선도효과가 발견되어 선물의 선도효과가 지배적임을 발견하였다.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.1
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• pp.19-26
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• 1987

In analyzing time series, estimating the level or the current mean of the process plays an important role in understanding its structure and in being able to make forecasts. The studies the class of time series models where the level of the process is assumed to follow a random walk and the deviation from the level follow an ARMA process. The estimation and forecasting problem in a Bayesian framework and uses the Kalman filter to obtain forecasts based on estimates of level. In the analysis of time series, we usually make the assumption that the time series is generated by one model. However, in many situations the time series undergoes a structural change at one point in time. For example there may be a change in the distribution of random variables or in parameter values. Another example occurs when the level of the process changes abruptly at one period. In order to study such problems, the assumption that level follows a random walk process is relaxed to include a major level change at a particular point in time. The major level change is detected by examining the likelihood raio under a null hypothesis of no change and an alternative hypothesis of a major level change. The author proposes a method for estimation the size of the level change by adding one state variable to the state space model of the original Kalman filter. Detailed theoretical and numerical results are obtained for th first order autoregressive process wirth level changes.

• Computers and Concrete
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• v.20 no.5
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• The Journal of Industrial Distribution & Business
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• v.8 no.4
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• pp.15-21
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• 2017

Purpose - In this work, we examined the causal relationship between credit loans from households (CLH), loan collateralized with housing (LCH) and an interest of certificate of deposit (ICD) among others in South Korea. Furthermore, the optimal forecasts on the underlying model will be obtained and have the potential for applications in the economic field. Research design, data, and methodology - A total of 31 realizations sampled from the 4th quarter in 2008 to the 4th quarter in 2016 was chosen for this research. To achieve the purpose of this study, a regression model with correlated errors was exploited. Furthermore, goodness-of-fit measures was used as tools of optimal model-construction. Results - We found that by applying the regression model with errors component ARMA(1,5) to CLH, the steep and lasting rise can be expected over the next year, with moderate increase of LCH and ICD. Conclusions - Based on 2017-2018 forecasts for CLH, the precipitous and lasting increase can be expected over the next two years, with gradual rise of two major explanatory variables. By affording the assumption that the feedback among variables can exist, we can, in the future, consider more generalized models such as vector autoregressive model and structural equation model, to name a few.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.45-53
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• 2020

Past literatures have not studied the impact of real-world events or information on the return and volatility of virtual currencies, particularly on the COVID-19 event, day-of-the-week effect, daily high-low price spreads and information flow rate. The study uses the ARMA-GARCH model to capture Bitcoin's return and conditional volatility, and explores the impact of information flow rate on conditional volatility in the Bitcoin market based on the Mixture Distribution Hypothesis (Clark, 1973). There were 3,064 samples collected during the period from 1^st of January 2012 to 20^th April, 2020. Empirical results show that in the Bitcoin market, a daily high-low price spread has a significant inverse relationship for daily return, and information flow rate has a significant positive relationship for condition volatility. The study supports a significant negative relationship between information asymmetry and daily return, and there is a significant positive relationship between daily trading volume and condition volatility. When Bitcoin trades on Saturday & Sunday, there is a significant reverse relationship for conditional volatility and there exists a day-of-the-week volatility effect. Under the impact of COVID-19 event, Bitcoin's condition volatility has increased significantly, indicating the risk of price changes. Finally, the Bitcoin's return has no impact on COVID-19 events and holidays (Saturday & Sunday).

• Advances in Energy Research
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• v.6 no.2
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• pp.131-143
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• 2019

Global Solar Radiation (GSR) is the key element for performance estimation of any Solar Power Plant (SPP). Its forecasting may help in estimation of power production from a SPP well in advance, and may also render help in optimal use of this power. Seasonal Auto-Regressive Moving Average (SARMA) and Artificial Neural Network (ANN) models are combined in order to develop a hybrid model (SARMA-ANN) conceiving the characteristics of both linear and non-linear prediction models. This developed model has been used for prediction of GSR at Gorakhpur, situated in the northern region of India. The proposed model is beneficial for the univariate forecasting. Along with this model, we have also used Auto-Regressive Moving Average (ARMA), SARMA, ANN based models for 1 - 6 day-ahead forecasting of GSR on hourly basis. It has been found that the proposed model presents least RMSE (Root Mean Square Error) and produces best forecasting results among all the models considered in the present study. As an application, the comparison between the forecasted one and the energy produced by the grid connected PV plant installed on the parking stands of the University shows the superiority of the proposed model.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.95-103
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• 2021

This study was aimed to investigate the education needs for prevention and control of infectious diseases by lifecycle based on age group and to provide the fundamental data to develop the educational programs. A research was conducted with 328 adults over 19 years old for a month of February 2021 through online and mobile survey by Gallup Korea. Research contents include the general characteristics, personal hygiene practices related to infection, perceived risks related to infection, importance and level of knowledge on infectious diseases, and education needs for prevention and control of infectious diseases. For the research data analysis, PASW Statistics Ver 20.0 was used as a statistical program. Ranks from analysis upon conversion as the formula of Borich needs to sum up with importance and knowledge level showed first (Borich 3.11) with treatments for infectious diseases; second (Borich 2.15) with process in case of suspicion and diagnosis of infectious diseases; third (Borich 1.75) with transmission routes of infectious diseases; fourth (Borich 1.73) with preventive ways of infectious diseases; fifth (Borich 1.50) with diagnostic and test methods of infectious diseases; sixth (Borich 1.45) with characteristics of infectious diseases; and seventh (Borich1.38) with main symptoms of infectious diseases. It is anticipated that development of educational programs applying education needs for prevention and control of infectious diseases in this research can contribute to enhance the physical health, mental health, and psychological well-being of the subjects.

• Water for future
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• v.29 no.2
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• pp.179-190
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• 1996

The characteristics of water quality in the downstream of the Han River were analyzed by statistical techniques. Basic characteristics, areal and temporal variations, and correlations of water quality data were investigated. Monthly water quality data have been investigated systematically by exploring data analysis, including time series plot, summary statistics, distribution test, time dependence test, seasonality test and flow relatedness test. Results show that water quality data in this river have seasonality. And applicability of stochastic models such as Thomas-Fiering model and ARMA(1,1) model was identified. From the examination of water quality data related to discharge, it was found that DO and SS are sensitive to water temperature rather than discharge, while BOD and COD are sensitive to discharge at dry seasons. Seasonal periodicities were identified in all water quality variables from the cross correlation analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1756-1761
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• 2021

Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.