• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.177-182
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• 2003

많은 경제 시계열 자료 중에서 주가는 국내외 경제상황은 물론 정부정책 등 시장 외적인 영향에 가장 민감하게 반응한다. 하지만, 지금까지의 주가예측에 있어서는 이러한 외부의 영향, 즉 개입(Intervention)이 발생했을 때 주가의 변동에 능동적으로 대처하는 모형이 부재하였다. 실제로 이러한 개입사실을 예측모형에 반영하지 않는다면, 주가예측 있어 그 예측력을 따진다는 것은 무의미하다고 판단된다. 따라서, 개입시점을 발견하고, 이 개입효과를 측정하여 이를 모형에 반영한다면 좋은 예측결과를 얻을 수 있을 것이다. 이 연구에서는 이상점 탐지절차를 이용하여 개입 시점을 발견하고 개입의 효과가 개입시점에만 영향을 주는 모형과 효과가 일정기간 지속되는 모형으로 두 개의 개입시계열모형을 구축하고, 이러한 두 모형의 예측성과와 인공신경망모형을 이용한 예측성과를 비교하였다. 초단기예측(개입 직후 예측)에 있어서 개입의 효과가 지속되는 경우에는 개입시계열이 인공신경망보다 좋을 결과 를 나타내긴 했지만 그 차이는 크지 않았으며, 개입의 효과가 시점에만 영향을 준 경우에는 인공신경망의 결과가 더 우수한 것으로 나타났다. 단기예측(개입 후 20 일후의 예측)에 있어서는 개입 효과의 지속여부에 상관없이 인공신경망이 개입시계열모형보다 우수한 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.293-303
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• 2003

Under the case that we know the period and the reason of external events, we reviewed the method of model identification, parameter estimation and model diagnosis with the former papers that have been studied about the linear time series model with intervention, and compared with nonlinear time series model such as ARCH, GARCH model that it has been used widely in economic models, and also we compared with the combination prediction method that Tong(1990) introduced.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.65-76
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• 2014

Housing prices are influenced by external shock factors such as real estate policy or economy. Thus, the intervention effect is important for the development of forecasting model for housing price index. In this paper, we examined the degree of effective power of external shock factors for forecasting housing price index and analyzed time series models for efficient forecasting of housing price index. It is shown that intervention models are better than other models in forecasting results using real data based on the accuracy criteria.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.735-743
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• 2011

This study analyzes inbound and outbound Korea tourism data through an intervention model. For the analysis, we adopt three intervention factors: (1) IMF bailout crisis in December 1997, (2) Severe Acute Respiratory Syndrome(SARS) outbreak in March 2003, and (3) Lehman Brothers bankruptcy in September 2008. The empirical results show that only the SARS factor lowered inbound tourism from April 2003 with a drastic decline in May 2003 and gradually decaying since then. However, all three factors significantly lowered tourism in the case of outbound tourism. Especially, the effect of the IMF is shown to be permanent from December 1997 and the effects of SARS and the Lehman Brothers bankruptcy abrupt and temporary with a gradual decay.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.69-84
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• 2015

The purpose of this study is to forecast the seaborne trade volume during January 1994 to December 2014 using the multiplicative seasonal autoregressive integrated moving average (ARIMA) along with intervention factors and an artificial neural network (ANN) model. Diagnostic checks of the ARIMA model were conducted using the Ljung-Box Q and Jarque-Bera statistics. All types of ARIMA process satisfied the basic assumption of residuals. The ARIMA(2,1,0) $(1,0,1)_{12}$ model showed the lowest forecast error. In addition, the prediction error of the artificial neural network indicated a level of 5.9% on hidden layer 5, which suggests a relatively accurate forecasts. Furthermore, the ex-ante predicted values based on the ARIMA model and ANN model are presented. The result shows that the seaborne trade volume increases very slowly.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.139-148
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• 2019

This study proposed a multiple intervention time series model to predict KTX passenger demand. In order to revise the research of Kim and Kim (Korean Society for Railway, 14, 470-476, 2011) considering only the intervention of the second phase of Gyeong-bu before November of 2011, we adopted multiple intervention seasonal ARIMA models to model the time series data with additional interventions which occurred after November of 2011. Through the data analysis, it was confirmed that the effects of various interventions such as Gyeong-bu and Ho-nam 2 phase, outbreak of MERS and national holidays, which affected the KTX transportation demand, are successfully explained and the prediction accuracy could be quite improved significantly.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.477-489
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• 2021

COVID-19 has spread seriously around the world in 2020 and it is still significantly affecting our whole daily life. Currently, the whole world is still undergoing the pandemic and South Korea is no exception to it. During the pandemic, South Korea had several events that prevented or accelerated its spread. To establish the prevention policies for infectious diseases, it is very important to evaluate the intervention effect of such events. The susceptible-infected-removed (SIR) model is often used to describe the dynamic behavior of the spread of infectious diseases through ordinary differential equations. However, the SIR model is a deterministic model without considering the uncertainty of observed data. To consider the uncertainty in the SIR model, the Bayesian approach can be employed, and this approach allows us to evaluate the intervention effects by time-varying functions of the infection rate in the SIR model. In this study, we describe the time trend of the spread of COVID-19 in South Korea and investigate the intervention effects for the events using the stochastic SIR model based on the Bayesian approach.

• The Korean Journal of Financial Management
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• v.15 no.1
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• pp.165-181
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• 1998

1987년 10월 미국의 주가폭락과 1990년대 일본주식시장의 지속적 침체 이후로 미국과 일본 등의 주식시장에서는 주식시장의 변동성 증대 및 침체의 원인으로서 주가지수선물이 주목받기 시작하였다. 1987년 주가대폭락을 연구한 브레디보고서에는 주가지수선물과 이를 이용한 포트폴리오보험전략이 주가폭락의 한 요인으로 지목되고 있으며, 일본의 경우 장기적인 주식시장 침체가 주가지수선물에 기인한다는 생각이 일반화되어 있다. 본 연구는 우리나라에서 1996년 5월 3일부터 시작된 주가지수선물거래 도입이 주식시장에 미치는 개입효과를 분석하는데 목적이 있다. 본 연구의 목적을 위하여 Box와 Tiao(1975)에 의해 제시된 개입분석모형(intervention analysis model)을 이용하여 분석한 결과 개입의 효과가 전체 모형 설정에 유의한 영향을 미치지 않음을 알 수 있었다. 따라서 우리나라의 경우 주가지수선물거래 도입이 주식시장에 미치는 개입효과는 미미하다고 할 수 있다.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.25-37
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• 2016

The economy of South Korea has experienced two financial crises: the 1997 Asian financial crisis and the 2008 global financial crisis. These crises had a significant impact on the nation's macro-economic indicators. Furthermore, they had a profound influence on container traffic in container ports in Busan, which is the largest port in South Korea in terms of TEUs handled. However, the impact of the Asian financial crisis on container throughput is not clear. In this study, we assume that the two financial crises are independent and different, and then analyze how each of them impacted container throughput in Busan ports. To perform this analysis, we use an intervention model that is a special type of ARIMA model with input series. Intervention models can be used to model and forecast a response series and to analyze the impact of an intervention or event on the series. This study focuses on the latter case, and our results show that the impacts of the financial crises vary considerably.

• Journal of the Korean Society for Railway
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• v.14 no.5
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• pp.470-476
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• 2011

This study proposed the intervention ARIMA model as a way to forecast the KTX passenger demand. The second phase of the Gyeongbu high-speed rail project and the financial crisis in 2008 were analyzed in order to determine the effect of time series on the opening of a new line and economic impact. As a result, the financial crisis showed that there is no statistically significant impact, but the second phase of the Gyeongbu high-speed rail project showed that the weekday trips increased about 17,000 trips/day and the weekend trips increased about 26,000 trips/day. This study is meaningful in that the intervention explained the phenomena affecting the time series of KTX trip and analyzed the impact on intervention of time series quantitatively. The developed model can be used to forecast the outline of the overall KTX demand and to validate the KTX O/D forecasting demand.