• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.211-231
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• 2022

This study attempted to identify the effects of macroeconomic variables such as the All Industry Production Index, Consumer Price Index, CD Interest Rate, and KOSPI on apartment lease prices divided into nationwide, Seoul, metropolitan, and region, and to present a methodological prediction model of apartment lease prices by region using Long Short Term Memory (LSTM). According to VAR analysis results, the nationwide apartment lease price index and consumer price index in Lag1 and 2 had a significant effect on the nationwide apartment lease price, and likewise, the Seoul apartment lease price index, the consumer price index, and the CD interest rate in Lag1 and 2 affect the apartment lease price in Seoul. In addition, it was confirmed that the wide-area apartment jeonse price index and the consumer price index had a significant effect on Lag1, and the local apartment jeonse price index and the consumer price index had a significant effect on Lag1. As a result of the establishment of the LSTM prediction model, the predictive power was the highest with RMSE 0.008, MAE 0.006, and R-Suared values of 0.999 for the local apartment lease price prediction model. In the future, it is expected that more meaningful results can be obtained by applying an advanced model based on deep learning, including major policy variables

• Management & Information Systems Review
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• v.34 no.4
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• pp.67-81
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• 2015

The purpose of this study was to evaluate the effect of this factor on the macroeconomic variables for the healthcare industry market. First, the government bond interest rates and the exchange rate is the cause variable of drug industry index. Drug industry index is a mutual influence between the Call interest rate. Second, the medical equipment index haver mutual cause variable such as call rate index, government bond interest rates, and exchange rate. A current account balance variable is the cause variable of drug industry index. Third, the drug industry index has a negative relationship with a Call interest rate and an exchange rate. but it has a positive relationship with a government bond interest rates. the medical equipment index has a negative relationship with an exchange rate. but it has a positive relationship with a government bond interest rates.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.457-467
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• 2015

Despite the recent phenomena of Chonsei price increase, low interest rate and low growth, the indexes of financial and insurance industry production showed the results contrary to the common belief that the financial industry is sensitive to such financial crises. This is because the index of financial industry has continuously maintained a certain level of increase as opposed to the index of all industry production. Thus, this study aimed to analyze the dynamic correlation between the index of financial industry production and Chonsei price increase. A vector autoregression (VAR) model, which doesn't have a cointegrating relationship, was used to define the Chonsei price index and the indexes of all industry production and financial and insurance industry, which are macro economic variables, and describe the data. The results of the analysis on the time series data of 183 months from January 2000 to May 2015 showed that Chonsei price increase was not directly derived from the index of financial industry, but the finance industrial index affected Chonsei price increase.

• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.206-213
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• 2024

This paper aims to investigate the causal relationship between geopolitical risk and stock price volatility in the shipping industry. Given its international nature and dependence on global trade, this industry is exposed to various uncertainties and risk factors. This study specifically focuses on the impact of geopolitical risk, which has gained significant attention in recent years due to events such as the Russia-Ukraine War and the Israel-Hamas War. To analyze this relationship, the study utilizes vector autoregressive model-based causality tests. The research estimates the causal relationship between geopolitical risk indicators and the stock price volatility of five shipping companies listed on the Korea Exchange. The study covers the period from 2000 to 2023. The results indicate the following: Firstly, an increase in geopolitical risk leads to a rise in stock price volatility for shipping companies. Moreover, the impact of actual geopolitical events, rather than just diplomatic disputes, is statistically significant. Lastly, the impact of geopolitical risk is particularly significant in the bulk shipping sector.

• 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.

• Korea Trade Review
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• v.42 no.4
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• pp.1-20
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• 2017

This study is to investigate the dynamic relationship between international capital flows and won exchange rate to the major currency in Korea. As the results of Granger causality test, international capital flows Granger-cause currency rate volatility in the short term. However, over time, won exchange rate volatility Granger-cause international capital flows in Korea. According to the results by period divided based on 2008 financial crisis, international capital flows have the significant effects on won-dollar exchange rate volatility before 2008 crisis although currency rate volatility Granger-cause international capital flows after the crisis. As the results of impulse-response function of the basis of VAR, foreign exchange rate volatility has no connection with international capital flows before the crisis while it doesn't after. After the crisis, currency rate volatility has promoted international capital flows, while its influence diminishes as time passes. As these results, the uncertainty of foreign exchange market tend to influence the international capital flows rather than vice versa in Korea. Thus, it would be a more effective policy to control the uncertainty of market than the direct restrictions international capital flows.

• 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.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.117-140
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• 1991

The purpose of this paper is primarily to introduce a nonparametric statistical tool developed by Baek and Brock to detect a unidirectional causal ordering between two economic variables and apply it to interesting macroeconomic relationships among money, production and prices. It can be applied to any other causal structure, for instance, defense spending and economic performance, stock market index and market interest rates etc. A key building block of the test for nonlinear Granger causality used in this paper is the correlation. The main emphasis is put on nonlinear causal structure rather than a linear one because the conventional F-test provides high power against the linear causal relationship. Based on asymptotic normality of our test statistic, the nonlinear causality test is finally derived. Size of the test is reported for some parameters. When it is applied to a money, production and prices model, some evidences of nonlinear causality are found by the corrected size of the test. For instance, nonlinear causal relationships between production and prices are demonstrated in both directions, however, these results were ignored by the conventional F-test. A similar results between money and prices are obtained at high lag variables.

• International Area Studies Review
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• v.20 no.1
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• pp.23-51
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• 2016

This study analyzed the expected economic effects of the Korea-GCC FTA and sought strategies for industrial cooperation. To see the economic effects of Korea-GCC FTA, we analysed the effect of the oil tariff reduction of economy by Vector Autoregression(VAR) model. The estimation results shows that following the abolishment of the tariff on crude oil imports, GDP, GNI and consumption are expected to grow by 0.212%, 0.389% and 0.238%, respectively. Meanwhile, investment, export and import are estimated to drop by 0.462%, 0.413% and 0.342%, respectively. As for prices, producer prices are to rise by 6.356%p, whereas consumer prices fall by 2.996%p. In short, the Korea-GCC FTA and resultant abolishment of the tariff on crude oil imports followed by the decline in crude oil prices will result in declining prices whilst macroeconomic indices, such as GDP, GNI and consumption, will increase exerting positive effects on domestic economic growth. Also, it is necessary to proactively respond to GCC member states' industrial diversification policies for FTA-based industrial cooperation to diversify the sources of crude oil and natural gas imports for further resource risk management.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.