• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.47-53
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• 2019

The purpose of the study was to utilize VECM(Vector Error Correction Model) and detect causal relationships among shipping freight rates. Shipping freight rates used in this study were BDI(Baltic Dry Index), HRCI(Howe Robinson Containership Index), WS(World Scale rate) and SCFI(Shanghai Containerized Freight Index). Using weekly data published since August 2nd, 2013 to September 6th, 2019, it was discovered that BDI and WS were heavily influenced by past week's BDI and WS respectively. VECM also found that one percent increase in WS resulted in 0.022% increase in following week's HRCI data. One percent increase in HRCI affects SCFI by 0.77% on the following week. This study believes that finding may help each shipping market of shipping freight rates estimates, thereby encouraging decision markers to exercise discretion and establish best interest decision.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.37-46
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• 2020

The paper examines the dynamic relationship of domestic credit and stock market liquidity on the economic growth of the Philippines from 1995 to 2018 applying the autoregressive distributed lag (ARDL) bounds testing approach to cointegration, together with Granger causality test based on vector error correction model (VECM). The ARDL model indicated a long-run relationship of domestic credit and stock market liquidity on GDP growth. When the GDP per capita is the dependent variable there is weak cointegration. Also, the Johansen cointegration test confirmed the existence of long-run relationship of domestic credit and stock market liquidity both on GDP growth and GDP per capita. The VECM concludes a long-run causality running from domestic credit and stock market liquidity to GDP growth. At levels, domestic credit has significant short-run causal relationship with GDP growth. As for stock market liquidity at first lag, has significant short-run causal relationship with GDP growth. With regards to VECM for GDP per capita, domestic credit and stock market liquidity indicates no significant dynamic adjustment to a new equilibrium if a disturbance occurs in the whole system. At levels, the results indicated the presence of short-run causality from stock market liquidity and GDP per capita. The CUSUMSQ plot complements the findings of the CUSUM plot that the estimated models for GDP growth and GDP per capita were stable.

• International Commerce and Information Review
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• v.12 no.4
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• pp.171-192
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• 2010

In general, a stock index and its individual stocks are assumed to follow a random walk. A stock index is an important source of information and one that is seen by people everyday, regardless of their investment intentions. This paper examines the correlation between the KOSPI-the index that best reflects the Korean stock market and the macro - economic variables that have been found to influence the index by previous studies. The sample period considers the years after 2000 when the Korean stock market matured as restrictions on foreign investors were removed. For this purpose, a Vector Error Correction Model (VECM) and KOSPI equation with a general pacific approach were used. This paper aims at verifying the factors that determined the KOSPI after 2000 and at examining whether there was structural change in the investment environment. It also investigates changes in the factors determining the KOSPI's performance as a result of structural changes in the investment environment. The V AR (Vector Autoregressive) model including the nine variables was selected as a baseline model whose stability was tested using the unit root test. The results from the VECM and the structural changes in the investment environment can be summarized by the following Inner story points.

• Journal of Distribution Science
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• v.11 no.10
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• pp.73-79
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• 2013

Purpose - This study examines the impact of oil price volatility on economic activities in Korea. The new millennium has seen a deregulation in the crude oil market, which invited immense capital inflow into Korea. It has also raised oil price levels and volatility. Drawing on the recent theoretical literature that emphasizes the role of volatility, this paper attends to the asymmetric changes in economic growth in response to the oil price movement. This study further examines several key macroeconomic variables, such as interest rate, production, and inflation. We come to the conclusion that oil price volatility can, in some part, explain the structural changes. Research design, data, and methodology - We use two methodological frameworks in this study. First, in regards to the oil price uncertainty, we use an Exponential-GARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity: EGARCH) model estimate to elucidate the asymmetric effect of oil price shock on the conditional oil price volatility. Second, along with the estimation of the conditional volatility by the EGARCH model, we use the estimates in a VECM (Vector Error Correction Model). The study thus examines the dynamic impacts of oil price volatility on industrial production, price levels, and monetary policy responses. We also approximate the monetary policy function by the yield of monetary stabilization bond. The data collected for the study ranges from 1990: M1 to 2013: M7. In the VECM analysis section, the time span is split into two sub-periods; one from 1990 to 1999, and another from 2000 to 2013, due to the U.S. CFTC (Commodity Futures Trading Commission) deregulation on the crude oil futures that became effective in 2000. This paper intends to probe the relationship between oil price uncertainty and macroeconomic variables since the structural change in the oil market became effective. Results and Conclusions - The dynamic impulse response functions obtained from the VECM show a prolonged dampening effect of oil price volatility shock on the industrial production across all sub-periods. We also find that inflation measured by CPI rises by one standard deviation shock in response to oil price uncertainty, and lasts for the ensuing period. In addition, the impulse response functions allude that South Korea practices an expansionary monetary policy in response to oil price shocks, which stems from oil price uncertainty. Moreover, a comparison of the results of the dynamic impulse response functions from the two sub-periods suggests that the dynamic relationships have strengthened since 2000. Specifically, the results are most drastic in terms of industrial production; the impact of oil price volatility shocks has more than doubled from the year 2000 onwards. These results again indicate that the relationships between crude oil price uncertainty and Korean macroeconomic activities have been strengthened since the year2000, which resulted in a structural change in the crude oil market due to the deregulation of the crude oil futures.

• International Area Studies Review
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• v.16 no.1
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• pp.3-27
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• 2012

This paper adopts recently developed panel unit root test that is cross-sectionally robust. Cointegration test is also used to find whether regional house prices are in line with gross regional domestic production (GRDP) in the long run in Korea during 1989-2009. Based on the panel VECM and the panel ARDL models, we examine causal relationships among the variables and estimate the long-run elasticity. We find evidence of cointegration and bidirectional causal relationships between regional house prices and GRDP. The results of long-run estimates, using both fixed effect and ARDL models, show that house prices positively and significantly influence on the GRDP and vice versa. Together with these results, the findings of ARDL-ECM imply that there exists a long-run equilibrium relationship between house prices and regional economic variables even if there is a possibility of short-run deviation from its long-run path.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.925-932
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• 2012

The article introduces a method to estimate a cointegrated vector autoregressive model, using mixed-frequency data, in terms of a state-space representation of the vector error correction(VECM) of the model. The method directly estimates the parameters of the model, in a state-space form of its VECM representation, using the available data in its mixed-frequency form. Then it allows one to compute in-sample smoothed estimates and out-of-sample forecasts at their high-frequency intervals using the estimated model. The method is applied to a mixed-frequency data set that consists of the quarterly real gross domestic product and three monthly coincident indicators. The result shows that the method produces accurate smoothed and forecasted estimates in comparison to a method based on single-frequency data.

• Land and Housing Review
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• v.11 no.1
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• pp.59-66
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• 2020

This thesis investigates relationship between Stock-to-Flow price and housing starts in Seoul metropolitan form 2008 year to 2019 year. The paper tests the relationship through two time-series models such as a vector error correction model and Dynamic Panel regression model. The model results show evidence of positive correlation between Stock-to-Flow price and housing starts in the long run. By transforming the regional data into a panel data set and running a fixed effects model, we test the explanatory power of PBR on housing starts. The result of VECM confirms that one unit uprising PBR raises up apartment construction by 7.4%. This result supports that PBR is a major factor in choosing a start of housing construct. Base on the result of empirical model, We also suggest that the market self-regulation function of housing providers is operating in the entire metropolitan area market.

• Journal of Digital Convergence
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• v.14 no.6
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• pp.157-165
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• 2016

The purpose of this study was to analyze the effect of the shipping industry real economy index on the stock prices of domestic shipping companies. The parameters used in this analysis were the stock price of H Company in South Korea and shipping industry real economy indices including BDI, CCFI and HRCI. The period analysis was from 2012 to 2015. The weekly data for four years of the stock price index of shipping companies, BDI, CCFI, and HRCI were used. The effects of CCFI and HRCI on the stock price index of domestic shipping companies were analyzed using the VAR model, and the effects of BDI on the stock price index of domestic shipping companies were analyzed using the VECM model. The VAR model analysis results showed that CCFI and HRCI had negative effects on the stock price index, and the VECM model analysis results showed that BDI also had a negative effect on the stock price index.

• International Commerce and Information Review
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• v.14 no.4
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• pp.27-47
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• 2012

The study aims to analyze how the variables for Korea, such as the exports, imports, FDI(Inward) and FDI(Outward), influence the economic growth and how they affect each other. For the purpose of empirical analysis, this paper used the quarterly time series data from 1980 to 2010, dividing the period before and after 1997(IMF). The variables used in this study were log-transformation from the original variables. This study empirically tests the relationship among variables by using VECM with considering the time-series properties of each variable. The results found from the study are as followings. Causality analysis using VECM proved that no causality between GDP and exports existed, whereas causality between GDP and FDI(Inward) existed, in which GDP affected FDI(Inward) since IMF. However, it was found that other periods and FDI(Inward) did not affect GDF and had no causality among them.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1449-1466
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• 2014

In this paper, we consider a hedge portfolio based on futures of underlying asset. A classical way to estimate a hedge ratio for a hedge portfolio of a spot and futures is a regression analysis. However, a regression analysis is not capable of reflecting long-run equilibrium between a spot and futures and volatility clustering in the conditional variance of financial time series. In order to overcome such defects, we analyzed KOSPI200 index and futures using VECM-CC-GARCH model and computed a hedge ratio from the estimated conditional covariance-variance matrix. In real data analysis, we compared a regression and VECM-CC-GARCH models in terms of hedge effectiveness based on variance, value at risk and expected shortfall of log-returns of hedge portfolio. The empirical results show that the multivariate GARCH models significantly outperform a regression analysis and improve hedging effectiveness in the period of high volatility.