• Land and Housing Review
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• v.12 no.3
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• pp.11-26
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• 2021

With the increasing demand for office space, there have been questions on how office rent distribution produces a change in the urban spatial structure in Seoul. The purpose of this paper is to investigate a relative price gradient and to present a time-series model that can quantitatively explain the dynamic changes in the urban spatial structure. The analysis was dealt with office rent above 3,306 m² for the past 10 years from 1Q 2010 to 4Q 2019 within Seoul. A modified repeat sales model was employed. The main findings are briefly summarized as follows. First, according to the estimates of the office price gradient in the three major urban centers of Seoul, the CBD remained at a certain level with little change, while those in the GBD and the YBD continued to increase. This result reveals that the urban form of Seoul has shifted from monocentric to polycentric. This shows that the spatial distribution of companies has gradually accelerated decentralized concentration implying that the business networks have become significant. Second, contrary to small and medium-sized office buildings that have undertaken no change in the gradient, large office buildings have seen an increase in the gradient. The relative price gradients in small and medium-sized buildings were inversely proportional among the CBD, the GBD, and the YBD, implying their heterogeneous submarkets by office rent movements. Presumably, those differences in the submarkets were attributed to investment attraction, industrial competition, and the credit and preference of tenants. The findings are consistent with the hierarchical system identified in the Seoul 2030 Plan as well as the literature about Seoul's urban form. This research claims that the proposed method, based on the modified repeat sales model, is useful in understanding temporal dynamic changes. Moreover, the findings can provide implications for urban growth strategies under rapidly changing market conditions.

• Economic Analysis
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• v.26 no.1
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• pp.84-132
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• 2020

A central bank infers market expectations of future yields based on yield curves. The central bank needs to precisely understand the changes in market expectations of future yields in order to have a more effective monetary policy. This need explains why a range of models have attempted to produce yield curves and market expectations that are as accurate as possible. Alongside the development of bond markets, the interconnectedness between them and macroeconomic factors has deepened, and this has rendered understanding of what macroeconomic variables affect yield curves even more important. However, the existence of various theories about determinants of yields inevitably means that previous studies have applied different macroeconomics variables when estimating yield curves. This indicates model uncertainties and naturally poses a question: Which model better estimates yield curves? Put differently, which variables should be applied to better estimate yield curves? This study employs the Dynamic Nelson-Siegel Model and takes the Bayesian approach to variable selection in order to ensure precision in estimating yield curves and market expectations of future yields. Bayesian variable selection may be an effective estimation method because it is expected to alleviate problems arising from a priori selection of the key variables comprising a model, and because it is a comprehensive approach that efficiently reflects model uncertainties in estimations. A comparison of Bayesian variable selection with the models of previous studies finds that the question of which macroeconomic variables are applied to a model has considerable impact on market expectations of future yields. This shows that model uncertainties exert great influence on the resultant estimates, and that it is reasonable to reflect model uncertainties in the estimation. Those implications are underscored by the superior forecasting performance of Bayesian variable selection models over those models used in previous studies. Therefore, the use of a Bayesian variable selection model is advisable in estimating yield curves and market expectations of yield curves with greater exactitude in consideration of the impact of model uncertainties on the estimation.

• The Korean Journal of Financial Management
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• v.24 no.4
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• pp.107-134
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• 2007

This paper tests the relationship among returns, volatilities, contracts and open interests of KOSPI 200 futures markets with the various dynamic models such as granger-causality, impulse response, variance decomposition and ARMA(1, 1)-GJR-GARCH(1, 1)-M. The sample period is from July 7, 1998 to December 29, 2005. The main empirical results are as follows; First, both contract change and open interest change of KOSPI 200 futures market tend to lead the returns of that according to the results of granger-causality, impulse response and variance decomposition with VAR. These results are likely to support the KOSPI 200 futures market seems to be inefficient with rejecting the hypothesis 1. Second, we also find that the returns and volatilities of the KOSPI 200 futures market are effected by both contract change and open interest change of that due to the results of ARMA(1,1)-GJR-GARCH(1,1)-M. These results also reject the hypothesis 1 and 2 suggesting the evidences of inefficiency of the KOSPI 200 futures market. Third, the study shows the asymmetric information effects among the variables. In addition, we can find the feedback relationship between the contract change and open interest change of KOSPI 200 futures market.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.181-200
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• 2003

Comovement of international stock market prices has been lately a major controversy in the global stock market. This paper explores whether the common trend has really existed among the US, Japan and Korea's stock markets using the econometric techniques such as VAR, VECM as applied. Pair of indices from the exchange market and the over-the-counter market in each country has been tested, and the exchange market only has been turned out that the common trend existed. The dynamic analyses using the Granger causality test, impulse response function, and the forecast error decomposition have followed to show that the US stock market has played some important role in the Korea and Japan's market in the exchange as well as in the OTC market. The results of the paper imply that the more careful investigation with respect to the co-integration may be necessary in the global market integration studies.

• Journal of Korea Port Economic Association
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• v.23 no.1
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• pp.1-18
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• 2007

The purpose of this study is to examine the effect of exchange rate volatility on Trading Volume of Container of Korea, and to induce policy implication in the contex of GARCH and regression model. In order to test whether time series data is stationary and the model is fitness or not, we put in operation unit root test, cointegration test. And we apply impulse response functions and variance decomposition to the structural model to estimate dynamic short run behavior of variables. The major empirical results of the study show that the increase in exchange rate volatility exerts a significant negative effect on Trading Volume of Container in long run. The results Granger causality based on an error correction model indicate that uni-directional causality between trading volume of container and exchange rate volatility is detected. This study applies impulse response function and variance decompositions to get additional information regarding the Trading Volume of Container to shocks in exchange rate volatility. The results indicate that the impact of exchange rate volatility on Trading Volume of Container is negative and converges on a stable negative equilibrium in short-run. Th exchange rate volatility have a large impact on variance of Trading Volume of Container, the effect of exchange rate volatility is small in very short run but become larger with time. We can infer policy suggestion as follows; we must make a stable policy of exchange rate to get more Trading Volume of Container

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.357-368
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• 2012

The purpose of this study is to empirically examine the impact of enterprise R&D investment on international trade in Korea under the new Normal Era. In order to test whether the time series data of trade variables are stationary or not, we put in operation unit root test and cointegration test. Based on VECM (Vector Error Correction Model), we also apply impulse response functions and variance decomposition to estimate the dynamic effects in the short-run and long-run. The results show that the relationship between enterprise R&D investment and international trade (export and import) exists in the long-run as well as in the short-run. The results of applying impulse response functions and variance decomposition also indicate that the impact of enterprise R&D investment on international trade is positive, and a significant portion of fluctuations in the trade variable is explained by enterprise R&D investment. Therefore, enterprise R&D investment must be continuously increased to improve economic growth with promoting trading competition power in Korea under the new Normal Era.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.57-67
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• 2020

Recently, smart cities are attracting attention as a solution for a plethora of urban problems, including transportation, environment, safety, and energy. However, despite a substantial body of research dealt with the concept, trends, policy, and legal institutions of smart cities, few researchers have examined how the smart city services influence the cities from the dynamic perspective that considers the entire cycle of a city, including its growth, stagnation, and decline. Thus, it is vital to understand how the city changes with time from the view that a city is a system of sub-elements-population, industry, transportation, environment, housing, and land-closely interacting together. Within this context, this study explores how the urban dynamics of Anyang-city develop for the long term using the System Dynamics method and analyzes the effect of smart city project investment on the dynamics of Anyang-city. According to the result, Anyang-city is a "mature and stable" type, and its population is expected to decrease slowly by 2040. Specifically, the Anyang-city population will be reduced to 553,000 by 2030. It was analyzed that the number will decrease to 543,000 by 2040. It was also found that the investment in smart city projects in Anyang, based on the Plan for Anyang Smart City, would have the following effects: easing population decline, increasing number of businesses, improving urban safety index, and increasing average driving speed. The population will grow by 4,000 and the number of businesses will increase by 761 than before budget investment. The result of this paper is expected to contribute to identifying and predicting the effect of smart city policies from a long-term perspective.

• Journal of Korea Technology Innovation Society
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• v.13 no.2
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• pp.252-281
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• 2010

In this paper, we analyze the result of the Technology Level Evaluation of 'Biochip and biosensor (BB) Technology' consisted of 3 sub-categorized technologies; biochip sensing (BS), lab on a chip and high-efficient customized health care technology. As an analysis tool, authors used a delphi (a repeated survey) and dynamic methodology with technology growth model to overcome limits of previous evaluations. As a result, levels of BB were evaluated 51.5% (Korea) and 75.1% (US), and the technology gap between two countries was 6.1 yrs. In 2013, these levels were expected to change to 60.1% (Korea), 78.4% (US) and 4.3 yrs, respectively. In comparison with other biotechnology, the gap of BB was smaller and expected to catch up with US faster. In the case of sub-categorized technologies, they showed the smallest gap and would have faster catch-up speed than other sub-categorized technologies in the Biotechnology field. Based on the result of the survey, relative superiority of BB in Korea was originated from competent researchers and research fund, but weak basic science would be weak points. We think that BB's characteristic as an emerging technology and concentrated research activities on BB are additional strong points. This research proposes the supporting and supplemented points to promote the BB in Korea.

• Journal of Korean Society of Forest Science
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• v.88 no.1
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• pp.1-10
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• 1999

The dynamic relationships among import prices of roundwood are analyzed using the time series approach. A vector autoregression(VAR) model is estimated for six import prices(New Zealand, Chile, Russia, U.S.A., PNG, and Malaysia). Then Granger's causality test, variance decomposition analysis, and impulse response function analysis are also conducted. The major results are summarized as follows : (1) The prices of New Zealand and Russia are caused by only own lagged prices. (2) The prices of Chile and PNG are effected by New Zealand, the price of PNG is effected by New Zealand and Russia, and the price of U.S.A. is effected by those of Chile and PNG, respectively. (3) An exogenous shock in New Zealand will affect the prices of New Zealand, PNG, U.S.A., Chile, Russia. (4) An exogenous shock in Chile may also affect the prices of Chile, U.S.A., Russia.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.13-21
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• 2021

The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.