• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.3
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• pp.133-146
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• 2015

This study is to investigate the causal relationship between exchange hedge and the net income of the shipbuilder through the unit root test and co-integration and vector autoregressive model(Vector Autoregressive Model: VAR). First, quarter net income of shipbuilders to order a unit root tests from 2000 to 2013 was used as a value after the Johnson transformation. In the same period, the return on bond futures(KTBF), three years bond yield(KTB3Y), America-Korea exchange differences are weekly data for each quarterly difference in value was converted by utilization, shipbuilding shares after log transformation which it was used. Also, structural change point investigation analysis to verify that looked to take advantage of the structural changes occur in the exchange hedge strategies affecting net income in the shipbuilding industry. Between the exchange hedge and net income of shipbuilders in structural change points detection and analysis showed that structural changes occur starting in 2004. In other words, strategy of shipbuilders about exchange hedge has occurred from "passive exchange hedge" to "active exchange hedge". The exchange hedge of the Korea shipbuilders through the estimation of the VAR was able to grasp that affect the profitability of mutual shipbuilders. Macroeconomic variables and stock prices could also check to see that affected the net income of the shipbuilding industry.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.1
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• pp.198-210
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• 2016

This study firstly aims to estimate a leading-price of Jeju flounders with various price-classes by fish weight and secondly plans to provide policy implications of flounder purchase projects by understanding dynamic changes and interactions among flounder producer price-classes caused by price impulses in the market. This study applies an unit root test for stability of data, uses a Granger causality test to estimate the leading-price among producer prices by fish weight, employs the vector autoregressive model to analyze statistical impacts among t-1 variables used in models, and finally utilizes impulse response analyses and forecast error variance decomposition analyses to understand dynamic changes and interactions among change rates of the producer prices caused by price impulses in the market. The results of the study are as follows. Firstly, KPSS, PP, and ADF tests show that the change rate of Jeju flounder monthly producer prices by fish weight differentiated by logarithm is stable. Secondly, the Granger causality test presents that the change rate of the 1kg flounder producer price strongly leads it of 500g, 700g, and 2kg flounder producer prices respectively. Thirdly, the vector autoregressive model indicates that the change rate of the 1kg producer price in t-1 period statistically, significantly influences it of own weight in t period and also slightly affects price change rates of other weights in t period. Fourthly, the impulse response analysis indicates that impulse responses of structural shocks for the change rate of the 1kg producer price are relatively more powerful in its own weight and in other weights than shocks emanating from price change rates of other weights. Fifthly, the variance decomposition analysis points out that the change rate of the 1kg producer price is relatively more influential than it of 500g, 700g, and 2kg producer prices respectively. In conclusion, the change rate of the 1kg Jeju flounder producer price leads the change rates of other ones and Jeju purchase projects need to be targeted to the 1kg Jeju flounder producer price as the purchase project implemented in 2014.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.4
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• pp.48-57
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• 1992

This paper addresses the estimation of the shift vector from aerial image sequences. The conventional feature-based and area-based matching methods are simulated for determining the suitable image registration scheme. Computer simulations show that the feature-based matching schemes based on the co-occurrence matrix, autoregressive model, and edge information do not give a reliable matching for aerial image sequences which do not have a suitable statistical model or significant features. In area-based matching methods we try various similarity functions for a matching measure and discuss the factors determining the matching accuracy. To reduce the estimation error of the shift vector we propose the reference window selection scheme. We also discuss the performance of the proposed algorithm based on the simulation results.

• Management & Information Systems Review
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• v.27
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• pp.131-147
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• 2008

The study examines common component existing in five Asian countries from 1991 to 2007. To do this, the daily stock market indices of Korea, Malaysia, Thailand, Indonesia, and the Philippines were used. Using a Vector Autoregressive Model this paper analyzes causal relations and dynamic interactions between five Asian stock markets. The findings in this study indicate that level of five Asian stock markets' stock return linkages are low. First, from the statistics for pair-wise Granger causality tests, I find Granger-causal relationship between Korea and Indonesia and between Malaysia and and Indonesia. Second, from the results of response function and the statistics of variance decomposition, I find that week shocks to Korean stock market return on Malaysia, Indonesia, Thailand, and the Philippines stock market returns. The results indicate increased Asian stock market linkages but the level is very low. This implies that the benefits of diversification within the five Asian stock markets are still existed.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.9
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• pp.1788-1795
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• 1992

Numerical simulations have been performed to investigate various spectral estimators used in LDV signal processing. In order to simulate a particle arrival time statistics known as the doubly stochastic poisson process, an autoregressive vector model was adopted to construct a primary velocity field. The conditional Poisson process with a random rate parameter was generated through the rescaling time process using the mean value function. The direct transform based on random sampling sequences and the standard periodogram using periodically resampled data by the sample and hold interpolation were applied to obtain power spectral density functions. For low turbulent intensity flows, the direct transform with a constant Poisson intensity is in good agreement with the theoretical spectrum. The periodogram using the sample and hold sequences is better than the direct transform in the view of the stability and the weighting of the velocity bias for high data density flows. The high Reynolds stress and high fluctuation of the transverse velocity component affects the velocity bias which increases the distortion of spectral components in the direct transform.

• The Journal of Fisheries Business Administration
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• v.51 no.4
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• pp.69-83
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• 2020

The purpose of this paper is to empirically investigate whether asymmetric price transmission exists in the distribution stage of farmed olive flounder market. For the analysis, time series data were used for the producer prices of Jeju and Wando, and the wholesale prices of Incheon, Hanam and Busan. Through the Granger causality test, the causal relationship from the producer price to the wholesale price was derived and the asymmetric price transmission was analyzed using the autoregressive distributed lag model (ARDL). As a result of the analysis, it was found that there is a phenomenon of 'positive asymmetric price transmission' from the producer price to the wholesale price. This result can be one evidence that excess profits are received in the intermediate distribution stage, and can be said to be a result showing the incompleteness and inefficiency of the distribution structure of the farmed olive flounder. In the future, it is required to establish an information-sharing system in all stages of production, distribution, and consumption that can create a competitive environment for distribution participants and resolve information asymmetry. Also, it is necessary to review the distribution center specializing in live fish from the viewpoint of the establishment of new distribution channels and sales diversification strategy under the rapidly changing fisheries environment.

• Journal of Korea Port Economic Association
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• v.35 no.3
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• pp.61-72
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• 2019

In order to enhance the competitiveness of the container shipping industry and promote its development, based on the empirical analyses using multivariate time series models, this study aims to suggest a few strategies related to the dynamics of the container shipping market. It uses the vector autoregressive (VAR) and vector error correction (VEC) models as analytical methodologies. Additionally, it uses the annual trade volumes, fleets, and freight rates as the dataset. According to the empirical results, we can infer that the most exogenous variable, the trade volume, exerted the highest influence on the total dynamics of the container shipping market. Based on these empirical results, this study suggests some implications for ship investment, freight rate forecasting, and the strategies of shipping firms. Concerning ship investment, since the exogenous trade volume variable contributes most to the uncertainty of freight rates, corporate finance can be considered more appropriate for container ship investment than project finance. Concerning the freight rate forecasting, the VAR and VEC models use the past information and the cointegrating regression model assumes future information, and hence the former models are found better than the latter model. Finally, concerning the strategies of shipping firms, this study recommends the use of cycle-linked repayment scheme and services contract.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.401-412
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• 2011

This study uses a vector error correction model to analyze the daily time series data of the spot price of EUA (European Union Allowance). As endogenous variables, five variables are considered for the analysis, including prices of crude oil, natural gas, electricity and coal in addition to carbon price. Data period is Phase 2 period (April 21, 2008 to March 31, 2010) to avoid Phase 1 period (2005-2007) where the EUA prices were distorted. Unit-root and cointegration test results reveal that all variables have a unit root and cointegration vectors exist, so a vector error correction model is adopted instead of a vector autoregressive model.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.529-545
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• 2023

This paper introduces the Banded-VHAR model suitable for high-dimensional long-memory time series with band structure. The Banded-VHAR model has nonignorable correlations only with adjacent dimensions due to data features, for example, geographical information. Row-wise estimation method is adapted for fast computation. Also, two estimation methods, namely BIC and ratio methods, are proposed to estimate the width of band. We demonstrate asymptotic consistency of our proposed estimation methods through simulation study. Real data applications to pm2.5 and apartment trading volume substantiate that our Banded-VHAR model outperforms traditional sparse VHAR model in forecasting and easy to interpret model coefficients.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.631-644
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• 2022

This paper considers robust estimation of the sparse vector autoregressive model (sVAR) useful in high-dimensional time series analysis. First, we generalize the result of Xu et al. (2008) that the adaptive lasso indeed has robustness in sVAR as well. However, adaptive lasso method in sVAR performs poorly as the number and sizes of outliers increases. Therefore, we propose new robust estimation methods for sVAR based on least absolute deviation (LAD) and Huber estimation. Our simulation results show that our proposed methods provide more accurate estimation in turn showed better forecasting performance when outliers exist. In addition, we applied our proposed methods to power usage data and confirmed that there are unignorable outliers and robust estimation taking such outliers into account improves forecasting.