• The Journal of Fisheries Business Administration
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• v.36 no.1 s.67
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• pp.51-79
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• 2005

The main propose of this study is to analyze of the Chinese Fisheries Products competitiveness in Korean Market. This study was using a model of working partnerships by James C. Anderson & James A. Narus(1990). That is, Support is found for a number of the hypothesized construct relations and in both manufacture firm and distributor firm model, for the respecification of cooperation as an antecedent rather than a consequence of trust. This study was able to apply this model's intention for the relationship between Chinese fisheries products exporter and Korean importer, because I thought that competitiveness of trade market was based on relationship between the two countries traders. The results of this study are summarized as follows. As the above result, the several hypothesized correlation among the factors were significant. These results was tried to apply the competitiveness degree index as main factors among the countries, The method of measuring competitiveness .degree index was [(outcome + influence + communication + coopration + trust + satisfaction) - conflict, In result, China was 21.5583, USA was 20.2667, East Asia was 18.79126, EU was 18.4723, Russia was 16.3858.

• The Korean Journal of Financial Management
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• v.25 no.2
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• pp.69-101
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• 2008

We empirically investigate the option leverage property that should be priced under much more general conditions than the Black-Scholes assumptions and the option redundancy property that is based on the assumption that the underlying asset price follows a one-dimensional diffusion process and examine the systematic risk factors implied in the return dynamics of KOSPI 200 index options. We find that the option leverage pattern is similar to the theoretical result but the options are not redundant securities and in the nonlinear structure of option payoffs, the traders of KOSPI 200 index options price the systematic higher-moments and the negative volatility risk premium significantly affects delta-hedged gains, even after accounting for jump fears. But the empirical evidence on jump risk preference is less conclusive.

• Journal of Distribution Science
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• v.11 no.8
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• pp.31-37
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• 2013

Purpose - This study aims to strengthen the economic cooperation between Korea and Japan by studying the pattern of trade between them and identifying drawbacks. Thus, it aims to enable trade expansion by analyzing the factors that affect trade and identifying ways to improve them. If economic cooperation is improved, transport and communication costs, as well as the transaction cost of economic exchanges, can be minimized. Research design, data, methodology - The trade intensity index developed by the Japanese economist Yamazawa under his trade intensity theory was used to analyze the trade decision factor of Korea and Japan. Trade structure and decision factors were analyzed for the target period of 2000 to 2012, and the period ranging from 2000 to 2005 was compared with the period ranging from 2005 to 2012. This paper is an analysis of the resultant time series. The data were collected from Korea Traders Association, Korea Customs Office, and UN Comtrade (2000, 2005, 2012) and whole table indexes were calculated by the author. Trade related index was used to analyze the comparative advantage based on time-series analysis statistics data (2000. 2005, 2012) through an analysis of the trade intensity index (TII), revealed comparative advantage index (RCA), and trade specialization index (TSI). Results - The trade intensity index of the industries of Japan and Korea is 1.814 in 2000. The export ratio of Japan against China was slightly higher at 2.128. TII is indicated to be 1.600 in both 2005 and 2012, which means export ratio of Japan against China is considerably maintained in 2005; however, export ratio of Japan against China is diminishing gradually as its index is 1.600 in 2012. Second, as per the trade specialization index of the ship industry in Japan and China, TSI is indicated to be -0.818 in 2000, -0.308 in 2005, and -0.847 in 2012. Generally, it is still closer to -1 and especially, we can see it is more closer to -1 in 2012. Third, as per the revealed comparative advantage index of the ship industry in Japan and China, the RCA index in 2012 is 0.007, which is quite far from 1 as compared to the value in 2000 and 2005. Hence, the Japanese ship industry has a significant comparative disadvantage against the Chinese ship industry. Conclusions - Both countries invest most of their capital in the shipping industry. It is the shipping industry that receives the most capital investment in the two countries is invested and governmental policy funds are needed. As both countries have large shipping industries, this research project is very valuable. Japan and China are compared because they are Korea's neighbors. Also, Korea is strategically located in Northeast Asia and has a history of foreign intrusion from several countries. Therefore, the purpose of this research study is to understand the trade structures of both countries and intensify the economic cooperation between Japan and China.

• Journal of Distribution Science
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• v.12 no.2
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• pp.105-112
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• 2014

Purpose - This research undertakes to understand the trade structures of both China and Japan to strengthen Sino-Japan economic cooperation and examines impediments to trade between the 2 countries to analyze causes which affect trade and to examine improvements in these areas to find out ways of trade expansion. Through this survey of a defined period of time, we can identify the structural factors of trade dependence in the relationship between China and Japan. Research design, data, methodology - The data were collected from Korea Traders Association, Korea Customs Office and UN Comtrade, from which whole table indexes are calculated by author. This research methodology uses trade related indexes to focus on analyzing comparative advantages based on time-series analysis statistics data (2000~2012), by using the analysis index of Trade Intensity Index (TII), Revealed Comparative Advantage Index (RCA) and Trade Specialization Index (TSI). Results - The export ratio for China against Japan was a little higher in 2000 at 2.867 and the export ratio for China against Japan was sustained in 2005. However, it diminished gradually and reached 1.263 in 2012. During the whole period of 2000~2012, the indexes were maintained without any significant change. However, they are still moving closer to -1. Especially, in 2012 it is the closest it has been to -1. Therefore, Japan has a comparative advantage toward export specialization. On the other hand, China has a comparative advantage toward import specialization. For the whole research period, all indexes were much smaller than 1, which means that China has comprehensively had a comparative disadvantage against Japan for the past 10 years when compared to other industries, even though it had improved in 2000. Conclusions - The summary of conclusions based on empirical analysis research are as follows: First, per the Trade Intensity Index of industries between the 2 countries, we can conclude that export ratio index is 2.867, based on the formula, in 2000, which means the export ratio of China against Japan is a little bit higher. Furthermore, the ratios of 2.259 and 1.263 are indicated in 2005 and 2012 respectively which mean the export ratio of China against Japan was maintained in 2005 but was diminishing gradually as the index is 1.263 in 2012. Second, per the Trade Specialization Index of the shipping industry between China and Japan, -0.379 is indicated in 2000, -0.368 in 2005 and -0.568 in 2012. Looking at the whole period of 2000~2012, the indexes were maintained without any significant change. However, they are still moving closer to -1. Especially, in 2012 it is the closest it has been to -1. Third, per the Revealed Comparative Advantage Index of the automobile industry between China and Japan, the RCA indexes in 2005 and 2012 are 0.246 and 0.306 respectively which are still far from 1 even though the index is improved compared to 2000's value of 0.0001. Therefore, the Chinese automobile industry is very much at a comparative disadvantage to that of the Japanese automobile industry.

• Journal of Distribution Science
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• v.12 no.6
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• pp.21-29
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• 2014

Purpose - This research undertakes to understand the competitiveness of the steel distribution industry of both Korea and China to strengthen Korea-Sino economic cooperation, examines impediments to trade between the two countries to analyze causes which affect trade, and examines improvements in these areas to identify means of trade expansion. Through this survey of a defined period, we can identify the structural factors of trade dependence in the relationship between Korea and China. Research design, data, and methodology - The data were collected from the Korea Traders Association, the Korea Customs Office, and UN Comtrade, from which whole table indexes are calculated. The research methodology uses trade-related indexes to focus on analyzing comparative advantages based on time-series analysis statistics data (2000-2012) by using the analysis index of trade intensity index (TII), the revealed comparative advantage index (RCA), and the trade specialization index (TSI). Results - The export ratio for Korea to China was slightly higher in 2000 at 2.867, and the export ratio for Korea to China was sustained in 2005. However, it diminished gradually, reaching 1.263 in 2012. During the period 2000-2012, the indexes were maintained without any significant change. However, they still remain close to -1. In particular, in 2012 it is the closest it has ever been to -1. Therefore, China has a comparative advantage in export specialization. On the other hand, Korea has a comparative advantage in import specialization. For the research period, all indexes were much lower than 1, which means that Korea has consistently had a comparative disadvantage against China for the past 10 years when compared to other industries, even though it experienced improvement in 2000. Conclusions - The summary of conclusions based on empirical analysis research are as follows: First, per the trade intensity index of industries between the two countries, we conclude that the export ratio index in 2000 is 2.867, which means the export ratio of Korea to China is slightly higher. Furthermore, the ratios of 2.259 and 1.263 held in 2005 and 2012, respectively, meaning that the export ratio of Korea to China was maintained in 2005, but was diminishing gradually as the index in 2012 was 1.263. Second, per the trade specialization index of the steel distribution industry between Korea and China, the value was -0.379 in 2000, -0.368 in 2005 and -0.568 in 2012. Looking at the whole period of 2000-2012, the indexes remained without any significant change. However, they are still moving closer to -1. In particular, in 2012 it is the closest it has ever been to -1. Third, regarding the revealed comparative advantage index of the steel distribution industry between Korea and China, the RCA indexes in 2005 and 2012 are 0.246 and 0.306, respectively, which are still far from 1, even though the index has improved compared to the 2000's value of 0.0001. Therefore, the Korean steel distribution industry is at a significant comparative disadvantage to that of the Chinese steel distribution industry.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.189-195
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• 2014

In this research, we investigate the relationship between volatility and the trading volumes of trader types in the KOSPI 200 index stock market, futures market, and options market. Three types of investors are considered: individual, institutional, and foreign investors. The empirical results show that the volatility of the stock market and futures market are affected by the transaction information from another market. This means that there exists the cross-market effect of trading volume to explain volatility. It turns out that the option market volatility is not explained by any trading volume of trader types. This is because the option market volatility, VKOSPI, is the volatility index that reflects traders' expectation on one month ahead underlying volatility. Third, individual investors tend to increase volatilities, whereas institutions and foreign investors tend to stabilize volatilities. These results can be used in the areas of investment strategies, risk management, and financial market stability.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.46-54
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• 2020

This study aims to develop and analyze the performance of a selective option straddle strategy based on forecasted volatility to improve the weakness of typical straddle strategy solely based on negative volatility risk premium. The KOSPI 200 option volatility is forecasted by the SVM model combined with the asymmetric volatility spillover effect. The selective straddle strategy enters option position only when the volatility is forecasted downwardly or sideways. The SVM model is trained for 2008-2014 training period and applied for 2015-2018 testing period. The suggested model showed improved performance, that is, its profit becomes higher and risk becomes lower than the benchmark strategies, and consequently typical performance index, Sharpe Ratio, increases. The suggested model gives option traders guidelines as to when they enter option position.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.191-212
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• 2003

Recently, there is a growing body of literature that suggests that information inefficiency is one of the causes of the asymmetric volatility. If this explanation for the asymmetric volatility is appropriate, then innovations, such as the introduction of futures, may be expected to impact the asymmetric volatility of stock market. As transaction costs and margin requirements in the futures market are lower than those in the spot market, new information is transmitted to futures prices more quickly and affects spot prices through arbitrage trading with spots. Also, the merit of the futures market may attract noise traders away from the spot market to the futures market. This study examines the impact of futures on the asymmetry of stock market volatility. If the asymmetric volatility is significant lower post-futures and exist in the futures market, it has validity that the asymmetric volatility is caused by information inefficiency in the spot market. The data examined are daily logarithmic returns on KOSPI 200 stock price index from January 4, 1993 to December 26, 2000. To examine the existence of the asymmetric volatility in the futures market, logarithmic returns on KOSPI 200 futures are used from May 4, 1996 to December 26, 2000. We used a conditional mode of TGARCH(threshold GARCH) of Glosten, Jagannathan and Runkel(1993). Pre-futures the spot market exhibits significant asymmetric responses of volatility to news and post-futures asymmetries are significantly lower, irrespective of bear market and bull market. The results suggest that the introduction of stock index futures has an effect on the asymmetric volatility of the spot market and are inconsistent with leverage being the sole explanation of asymmetry. However, it is found that the volatility of futures is not so asymmetric as expected.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Korean Business Review
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• v.19 no.1
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• pp.81-92
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• 2006

This study investigates the relation between institutional investor's net purchase and the volatility of KOSPI. Some portion of volatility in stock prices comes from noise trading of irrational traders. Observed volatility may be defined as the sum of the portion caused by information arrival, fundamental volatility, and the portion caused by noise trading, transitory volatility. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. Most studies investigates the effect on the observed volatility. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Estimation results show that institutional investor's net purchase was not significantly related to all kinds of volatility(observed volatility, fundamental volatility and transitory volatility). This means that institutional investor's net purchase did not increase noise trading.