• The Korean Journal of Financial Management
• /
• v.25 no.1
• /
• pp.1-22
• /
• 2008

This study is to evaluate, to the extent to, which advanced currency futures and emerging currency futures markets can predict accurately the future spot rate. To this end, Johansen's the maximum-likelihood cointegration method(1988, 1991) is adopted to test the unbiasedness and efficiency hypothesis. Also, this study is to estimate and compare a quantitative measure of relative efficiency as a ratio of the forecast error variance from the best-fitting quasi-error correction model to the forecast error variance of the futures price as predictor of the spot price in advanced currency futures with in emerging currency futures market. Advanced currency futures is British pound and Japan yen. Emerging currency futures includes Korea won, Mexico peso, and Brazil real. The empirical results are summarized as follows : First, the unbiasedness hypothesis is not rejected for Korea won and Japan yen futures exchange rates. This indicates that the emerging currency Korea won and the advanced currency Japan yen futures exchange rates are likely to predict accurately realized spot exchange rate at a maturity date without the trader having to pay a risk premium for the privilege of trading the contract. Second, in emerging currency futures markets, the unbiasedness hypothesis is not rejected for Korea won futures market apart from Mexico peso and Brazil real futures markets. This indicates that in emerging currency futures markets, Korea won futures market is more efficient than Mexico peso and Brazil real futures markets and is likely to predict accurately realized spot exchange rate at a maturity date without risk premium. Third, this findings show that the results of unbiasedness hypothesis tests can provide conflicting finding. according to currency futures class and forecasts horizon period, Fourth, from the best-fitting quasi-error correction model with forecast horizons of 14 days, the findings suggest the Japan yen futures market is 27.06% efficient, the British pound futures market is 26.87% efficient, the Korea won futures market is 20.77% efficient, the Mexico peso futures market is 11.55%, and the Brazil real futures market is 4.45% efficient in the usual order. This indicates that the Korea won-dollar futures market is more efficient than Mexico peso, and Brazil real futures market. It is therefore possible to concludes that the Korea won-dollar currency futures market has relatively high efficiency comparing with Mexico peso and Brazil real futures markets of emerging currency futures markets.

• Management & Information Systems Review
• /
• v.32 no.4
• /
• pp.189-207
• /
• 2013

Using high-frequency data for 2 years, this study investigates intraday lead-lag relationship between stock index and stock index futures markets in Korea and China. We found that there are some differences in price discovery and volatility transmission between Korea and China after the stock index futures markets was introduced. Following Stoll-Whaley(1990) and Chan(1992), the multiple regression is estimated to examine the lead-lag patterns between the two markets by Newey-West's(1987) heteroskedasticity and autocorrelation consistent covariance matrix(HAC matrix). Empirical results of KOSPI 200 shows that the futures market leads the cash market and weak evidence that the cash market leads the futures market. New market information disseminates in the futures market before the stock market with index arbitrageurs then stepping in quickly to bring the cost-of-carry relation back into alignment. The regression tests for the conditional volatility which is estimated using EGARCH model do not show that there is a clear pattern of the futures market leading the stock market in terms of the volatility even though controlling nonsynchronous trading effects. This implies that information in price innovations that originate in the futures market is transmitted to the volatility of the cash market. Empirical results of CSI 300 shows that the cash market is found to play a more dominant role in the price discovery process after the Chinese index started a sharp decline immediately after the stock index futures were introduced. The new stock index futures markets does not function well in its price discovery performance at its infancy stage, apparently due to high barriers to entry into this emerging futures markets. Based on EGAECH model, the results uncover strong bi-directional dependence in the intraday volatility of both markets.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2000.11a
• /
• pp.489-497
• /
• 2000

After IMF situation, the money market environment is changing rapidly. Therefore, many companies including financial institutions and many individual investors are concerned about forecasting the money market, and they make an effort to insure the various profit and hedge methods using derivatives like option, futures and swap. In this research, we developed a prototype of forecasting system for KOSPI 200 option, especially call option, trading using artificial neural networks(ANN), To avoid the overfitting problem and the problem involved int the choice of ANN structure and parameters, we employed the ANN ensemble approach. We conducted two types of simulation. One is conducted with the hold signals taken into account, and the other is conducted without hold signals. Even though our models show low accuracy for the sample set extracted from the data collected in the early stage of IMF situation, they perform better in terms of profit and stability than the model that uses only the theoretical price.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2002.03a
• /
• pp.39-51
• /
• 2002

Online exchange is a cost-effective approach to trade goods and information among multiple sellers and buyers. Shipping industry includes lots of global entities such as shippers, liners, ship owners and shipping agents. Marine insurance companies and ship repairers and many other groups are also supporting the industry. However, international shipping exchanges are located on few cities in the world. Its our motivation that a shipping market can be online so that market participants do the dealing while sitting where they are with more efficient manner, preferable price and larger pool of candidates of trading partners. This paper presents Korean governmental project of building a cyber shipping exchange. The exchange covers ship sale and purchase, charter, insurance, freight futures, repairs, supplying of ships oil and database service. The workflows of each business were analyzed and designed to fit for online environment. The project includes design of trading mechanism, online documents, data flow, data storage and security. Online match making and trading mechanisms such as auction, reverse auction, bid are used. The whole trading process involves multiple organizations and business processes. So, this Paper focuses on how each organization would play their roles so that users can complete transactions with integrated and transparent view. The online exchange selves also as maritime portal site that links to other sites for cooperation vertically or horizontally, and serves database and information in global perspective. This paper also issues and discusses the justification of an online shipping exchange

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.582-583
• /
• 2021

As the production of new and renewable energy such as solar and wind power has diversified, microgrid systems that can simultaneously produce and consume have been introduced. . In general, a decrease in electricity prices through solar power is expected in summer, so producer protection is required. In this paper, we propose a transparent and safe gift power transaction system between users using blockchain in a microgrid environment. A futures is simply a contract in which the buyer is obligated to buy electricity or the seller is obliged to sell electricity at a fixed price and a predetermined futures price. This system proposes a futures trading algorithm that searches for futures prices and concludes power transactions with automated operations without user intervention by using a smart contract, a reliable executable code within the blockchain network. If a power producer thinks that the price during the peak production period (Hajj) is likely to decrease during production planning, it sells futures first in the futures market and buys back futures during the peak production period (Haj) to make a profit in the spot market. losses can be compensated. In addition, if there is a risk that the price of electricity will rise when a sales contract is concluded, a broker can compensate for a loss in the spot market by first buying futures in the futures market and liquidating futures when the sales contract is fulfilled.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1997.10a
• /
• pp.105-108
• /
• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• Management & Information Systems Review
• /
• v.33 no.3
• /
• pp.93-104
• /
• 2014

This study investigates price discovery between BlueNext spot and futures in EU ETS carbon emission markets using vector error correction model, GG and Hasbruck information ratio. Especially EUA is European Union Allowances traded on the Emissions Trading Scheme. This emission asset attracts and increasing attention among operators, investors and brokers on emission markets. In this study, we found BlueNext spot and EUA futures market are cointegrated. Following the preceding studies, we judged that EUA futures market contribute to the price discovery process than BlueNext spot market when this GG and Hasbrouck information ratio for BlueNext market are larger than 0.5. In other words, the futures market of EUA plays a more dominant role in price discovery than the spot market.

• Journal of The Korean Digital Architecture Interior Association
• /
• v.12 no.1
• /
• pp.35-43
• /
• 2012

This study aimed to seek a method capable of hedging a rising risk of housing rent price by introducing derivatives with the target of Korean housing rent markets. The research model used in this thesis progressed a research by applying a futures contract method with the target of the rent price of major apartments in Gangnam and Gangbuk Regions of Seoul. As an analysis result, the rent price of all complexes has risen during its analysis period, so it could be confirmed that the CRB future index was also risen according to this. Finally, it was confirmed that the rising risk of the rent price can be hedged through a purchase position of futures. But, as the difference between rent price variation and CRB future index variation occurs, it appeared that 100% of hedge is difficult. However, it is judged that if considering that a method capable of hedging the rising risk of the existing rent price was nonexistent, the hedge trading effect utilizing the CRB future index on the rent price will be meaningful.

• Journal of the Korea Convergence Society
• /
• v.8 no.11
• /
• pp.383-391
• /
• 2017

This study examines the relationship between sentiment of speculators and price movements in the futures markets of WTI crude oil, copper, and wheat during the period 2003~2014 using Granger causality tests. The results indicate that speculative positions overall has no predictive power for returns in each futures market. Rather, returns seem to have effects on speculators' sentiment especially during periods of both economic expansion and recovery. During a recession, meanwhile, changes of speculators' sentiment index in the WTI crude oil and copper markets provide predictive power for returns in a positive direction, suggesting that speculators' pessimistic sentiment aggravates declines in commodity prices. Since the effects of speculative positions on market prices are ambiguous, tight regulations on speculative trading are not advisable. In a bearish market, however, regulatory bodies should consider raising speculative position limits because large speculative short positions and (or) liquidation of index traders' long positions may lead steep price declines.

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