• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.117-126
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• 2020

This paper is aimed to explore the co-movement capital market in Southeast Asia and analysis the correlation of conventional and Islamic Index in the regional and global equity. This research become necessary to represent the risk on the capital market and measure market performance, as investor considers the volatility before investing. The time series daily data use from April 2012 to April 2020 both conventional and Islamic stock index in Malaysia and Indonesia. This paper examines the dynamics of conditional volatilities and correlations between those markets by using Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH). Our result shows that conventional or composite index in Malaysia less volatile than Islamic, but on the other hand, both drive correlation movement. The other output captures that Islamic Index in Indonesian capital market more gradual volatilities than the Composite Index that tends to be low in risk so that investors intend to keep the shares. Generally, the result shows a correlation in each country for conventional and the Islamic index. However, Internationally Indonesia and Malaysia composite and Islamic is low correlated. Regionally Indonesia's indices movement looks to be more correlated and it's similar to Malaysian Capital Market counterparts. In the global market distress condition, the diversification portfolio between Indonesia and Malaysia does not give many benefits.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.164-164
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• 2016

Radar rainfall estimates have been widely used in calculating rainfall amount approximately and predicting flood risks. The radar rainfall estimates have a number of error sources such as beam blockage and ground clutter hinder their applications to hydrological flood forecasting. Moreover, it has been reported in paper that those errors are inter-correlated spatially and temporally. Therefore, in the current study, we tested influence about spatio-temporal errors in radar rainfall estimates. Spatio-temporal errors were simulated through a stochastic simulation model, called Multivariate Autoregressive (MAR). For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo. The results indicated that spatio-temporal dependent errors caused much higher variations in peak discharge than spatial dependent errors. To further investigate the effect of the magnitude of time correlation among radar errors, different magnitudes of temporal correlations were employed during the rainfall-runoff simulation. The results indicated that strong correlation caused a higher variation in peak discharge. This concluded that the effects on reducing temporal and spatial correlation must be taken in addition to correcting the biases in radar rainfall estimates. Acknowledgements This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform Using Hydrological Radars), which was funded by the Korea Institute of Construction Technology.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Journal of Navigation and Port Research
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• v.44 no.1
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• pp.44-52
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• 2020

More accurate forecasting of port cargo in the global long-term recession is critical for the implementation of port policy. In this study, the Busan Port container volume (export cargo and transshipment cargo) was estimated using the Vector Autoregressive (VAR) model and the vector error correction (VEC) model considering the causal relationship between the economic scale (GDP) of Korea, China, and the U.S. as well as ARIMA, a single volume model. The measurement data was the monthly volume of container shipments at the Busan port J anuary 2014-August 2019. According to the analysis, the time series of import and export volume was estimated by VAR because it was relatively stable, and transshipment cargo was non-stationary, but it has cointegration relationship (long-term equilibrium) with economic scale, interest rate, and economic fluctuation, so estimated by the VEC model. The estimation results show that ARIMA is superior in the stationary time-series data (local cargo) and transshipment cargo with a trend are more predictable in estimating by the multivariate model, the VEC model. Import-export cargo, in particular, is closely related to the size of our country's economy, and transshipment cargo is closely related to the size of the Chinese and American economies. It also suggests a strategy to increase transshipment cargo as the size of China's economy appears to be closer than that of the U.S.

• Management & Information Systems Review
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• v.39 no.2
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• pp.17-37
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• 2020

The purpose of this study is to investigate the global spillover effects through the existence of linear and nonlinear causal relationships between the US, European and BRIC financial markets after the period from the introduction of the Euro, the financial crisis and the subsequent EU debt crisis in 2007~2010. Although the global spillover effects of the financial crisis are well described, the nature of the volatility effects and the spread mechanisms between the US, Europe and BRIC stock markets have not been systematically examined. A stepwise filtering methodology was introduced to investigate the dynamic linear and nonlinear causality, which included a vector autoregressive regression model and a multivariate GARCH model. The sample in this paper includes the post-Euro period, and also includes the financial crisis and the Eurozone financial and sovereign crisis. The empirical results can have many implications for the efficiency of the BRIC stock market. These results not only affect the predictability of this market, but can also be useful in future research to quantify the process of financial integration in the market. The interdependence between the United States, Europe and the BRIC can reveal significant implications for financial market regulation, hedging and trading strategies. And the findings show that the BRIC has been integrated internationally since the sub-prime and financial crisis erupted in the United States, and the spillover effects have become more specific and remarkable. Furthermore, there is no consistent evidence supporting the decoupling phenomenon. Some nonlinear causality persists even after filtering during the investigation period. Although the tail distribution dependence and higher moments may be significant factors for the remaining interdependencies, this can be largely explained by the simple volatility spillover effects in nonlinear causality.

• The Korean Journal of Financial Management
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• v.12 no.2
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• pp.239-271
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• 1995

본 연구는 현재 미국에서 거래되고 있는 세 가지 주가지수선물 상호간의 일중(intradaily) 가격선도(price leadership) 관계에 관한 실증분석이다. 본 연구가 기존의 연구와 다른점은, 기존의 연구가 주가지수선물과 그 기준이 되는 현물 가격사이의 가격 선도 관계에 초점을 두고 있는데 반하여 본 연구는 주가지수선물 시장 사이에서 존재하는 가격 선도관계를 분석하고 있다는 점이다. 실증 분석의 대상이 된 주가지수선물들은 Chicago Mercantile Exchange의 Standard and Poor's 500 Index(S&P 500), New York Futures Exchange의 New York Stock Exchange Composit Index (NYSE), 그리고 Chicago Board of Trade의 Major Market Index(MMI)이다. 만약 이들 시장들이 정보의 전달에 있어서 효율적(informationally efficient) 이라면 이들 가격간에 선도-지연(lead-lag) 현상은 존재하지 않을 것이다. 그러나 어느 한 시장이 새로운 정보를 선물가격에 반영하는데 다른 시장에 비해 상대적으로 느리다면, 이들 시장 상호간에는 가격의 전이(transmission)현상이 존재하게 될 것이다. 이들 선물간의 일중 가격선도 관계 연구는 이러한 시장의 효율성 문제를 밝히는데 의의가 있을 뿐만 아니라, 시장간의 단기적 가격 괴리를 이용하려는 차익거래자들에게도 유용하게 쓰일 수 있을 것이다. 본 연구는 위에서 언급한 각각의 주가지수선물들이 가격 선도성을 가질 수 있는 이유와 관련된 다음과 같은 세 가지 가설을 설정하였다. 첫째 가설은, 가격의 선도성은 거래량과 관련이 있다는 것이다. 즉, 이들 주가지수선물 중 가장 거래량이 많은 S&P 500 선물이 다른 선물을 선도할 것이라는 가설이다. 둘째, 가격의 선도성은 주가지수를 구성하는 주식의 수에 비례한다는 가설이다. 다시 말하면, 보다 않은 수로 구성된 주가지수일수록 정보처리 속도가 빠르다는 가설이다. 따라서, 본 연구에 포함된 주가지수선물 중 가장 많은 수의 주식을 대상으로 하는 NYSE 선물이 다른 선물을 선도할 것이다. 마지막 가설은 정보의 처리는 대형주 혹은 기관선호주(institutionally-favored)들이 주도한다는 것이다. 따라서, 주로 이와 같은 주식들로 구성 된 MMI 선물이 선도성을 가질 수 있다는 것이다. 위의 가설들을 검증하고 시장간의 가격 선도관계를 분석하기 위하여 본 연구는 vector autoregressive(VAR) 모형을 이용하여 충격-반응 함수(impulse response functions)를 계산하고, 분산분해(variance decomposition)를 수행하였다. 또한 가격 상호간에 존재할지도 모르는 공적분(cointegration)관계를 Johansen(1991)과 Jokansen and Juselius (1992) 등이 제시한 다변량 공적분 검정(multivariate cointegration test)를 통하여 분석하였다. 분석기간은 1986년 1월부터 1990년 7월까지이며, 각 주가지수선물들의 5분 간격 data를 사용하였다. 연구결과, 충격-반응 분석은 어느 한 시장에서의 충격(shock)은 다른 시장으로 매우 빠르게 전달되고 있음을 보여 주었다. 그러나 충격의 지속정도는 그 충격의 진원지에 따라 달랐다. 즉, NYSE나 MMI 선물로부터 발생 한 충격은 다른 시장의 가격에 5분 안에 반영을 끝냈지 만 S&P 500 선물에서 발생한shock은 그 이상 지속되었다. 또한, 분산분해 결과 S&P 500 선물이 자기자신 뿐만 아니라 다른 시장의 예상하지 못했던 움직임(unexpected movements)을 설명하는데 가장 큰 설명력(explanatory power)을 가지고 있었다. 결론적으로 S&P 500 선물이 다른 선물을 약 5분 간격으로 선도하였다. 이는 가격의 선도가 거래량과 밀접한 관계가 있음을 보여 주는 것이다.