• Journal of Korea Trade
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• v.27 no.5
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• pp.167-198
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• 2023

Purpose - This paper aims to compare and analyze the co-movements and information spillover effects between the international commodity futures markets and the South Korean stock markets during the COVID-19 and the 2008 financial crises. Design/methodology - The DCC-GARCH model is used in the co-movements analysis. In contrast, the BEKK-GARCH model is used to evaluate information spillover effects. The statistical data used is from January 1, 2005, to December 31, 2022. It comprises the Korea Composite Stock Price Index data and daily international commodity futures prices of natural gas, West Texas Intermediate crude oil, gold, silver, copper, nickel, soybean, and wheat. Findings - The results of the co-movement analysis were as follows: First, it was shown that the co-movements between the international commodity futures markets and the South Korean stock markets were temporarily strengthened when the COVID-19 and 2008 financial crises occurred. Second, the South Korean stock markets were shown to have high correlations with the copper, nickel, and crude oil futures markets. The results of the information spillover effects analysis are as follows: First, before the 2008 financial crisis, four commodity futures markets (natural gas, gold, copper, and wheat) were shown to be in two-way leading relationships with the South Korean stock markets. In contrast, seven commodity futures markets, except for the natural gas futures market, were shown to be in two-way leading relationships with the South Korean stock markets after the financial crisis. Second, before the COVID-19 crisis, most international commodity futures markets, excluding natural gas and crude oil future markets, were shown to have led the South Korean stock markets in one direction. Third, it was revealed that after the COVID-19 crisis, the connections between the South Korean stock markets and the international commodity futures markets, except for natural gas, crude oil, and gold, were completely severed. Originality/value - Useful information for portfolio strategy establishment can be provided to investors through the results of this study. In addition, it is judged that financial policy authorities can utilize the results as data for efficient regulation of the financial market and policy establishment.

• 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.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.4
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• pp.787-791
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• 2013

In this paper we present one factor model of commodity prices with a single jump regime-switching process. And we derive an analytic formula for pricing futures contracts when the parameters of commoditiy process have governed by a Markov regime-switching process.

• Bulletin of the Korean Mathematical Society
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• v.51 no.6
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• pp.1791-1804
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• 2014

A new approach to the commodity futures term structure model is introduced. The most salient feature of this model is that, once the interest rate model is given, the commodity futures price volatility is the only quantity that completely determines the model. As a consequence this model enables one to do away with the drudgeries of having to deal with the convenience yield altogether, which has been the most thorny point so far.

• Environmental and Resource Economics Review
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• v.9 no.2
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• pp.421-449
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• 2000

The objective of this paper is to examine whether it is feasible to introduce an crude oil futures contract on domestic commodity exchange in order to minimize the price risks of imported crude oil. In addition. this study suggests the policy issues to promote futures trading and the alternatives to use foreign energy compares the five criteria to evaluate the feasibility of crude oil futures trading on the domestic exchange. Related to the possibility of successful futures trading of imported crude oil on the domestic exchange, they are evaluated as follows: it is highly possible to succeed for the aspects of price volatility, potential market size or liquidity, and commodity homogeneity; but it is inappropriate for the aspects of deliverable amounts and market power or market structure. Therefore, it is concluded that trading a new futures contract for the underlying imported crude oil on the domestic exchange is inappropriate. For the policy issues and the hedging alternatives, first, it is urgent to establish an atmosphere for futures trading by promoting spot trading. Second, for the case of futures trading on the domestic exchange it is important to consider the simultaneous hedging of crude oil price and foreign exchange risks and mutual offsetting mechanism with major foreign exchanges. Third, for the case of futures trading on foreign exchanges it is reasonable to regard cooperation among concerned companies, government support for futures trading and direct participation into futures trading by the government.

• The Korean Journal of Financial Management
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• v.8 no.1
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• pp.199-212
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• 1991

주식옵션(stock options)에 대한 연구에 비교하여 상품 및 퓨처 옵션(commodity & futures options)에 대한 연구는 선진국에서도 지금 한참 연구를 하고 있는 단계에 있다. 우리나라에서도 이 분야에 대한 이론을 바탕으로 하는 제도를 곧 도입하려는 준비를 하고 있다. 본 연구는 블랙의 '블랙의 컴모디티 옵션의 가격모형(Black commodity option pricing model)'을 이용하여 재무성 장기채권의 퓨처의 균형가격을 예측하는데 있다. 이 블랙모형의 적용가능성을 검증해 본 것이다. 실제퓨처가격(observed futures prices)과는 달리 재무성 장기채권 퓨처 옵션에서의 묵시적 퓨처가격(futures prices implicit)은 시장효율성(market efficiencies)의 전제하에 성립되거나, 아니면 옵션가격모형을 사용하여서는 아니되거나 둘 중의 하나이거나 둘 다 섞이거나 일 것이다. 본 실증적인 연구, 즉 묵시적인 표준편차(implied standard deviations)를 사이멀테니어스(simultaneously)하게 계산한 묵시적인 퓨처가격(implied futures prices)을 사용한 실증적인 연구는 옵션모델에 의하여 퓨처가격을 계산하는 데에 문제가 있음을 발견하였다. 그 이유는 옵션가격결정모형을 이용하여 계산한 재무성 장기채권의 퓨쳐가격은 재무성 장기채권의 미래가격변동의 방향을 제시하는 지표로써 사용할 수 없기 때문일 것이다. 우리나라에서도 이 분야에 대한 이론과 제도를 곧 도입하는 입장에서 선행되는 문헌이 될 것이다.

• International Area Studies Review
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• v.14 no.1
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• pp.99-119
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• 2010

This study explores the natural gas market and the oil market in the U.S. and the European oil market. It focuses on two kinds of analyses; one is to confirm whether there is the predictive power between spot and futures within homogeneous commodity market(or inter-heterogeneous commodity market) through Granger-causality test in terms of the return rate and the volatility. The other is to examine the spot price stabilizing effect of futures price through regression analysis. When it comes to the predictive power of inter-commodity market, there was a conflicting aspect between the return rate of spot and futures. Overall, however, its statistical significance was low. With respect to the volatility, we found that the natural gas market has little influence on the oil market unlike the predictive power of oil market on natural gas market. Concerning the return rate of the predictive power within homogeneous commodity market, we found that the return rate of spot has the predictive power on futures only in the European market. In addition, we identified that there is feedback between spot and futures in the all commodity markets regarding volatility. As a result of the spot price stabilizing effect analysis of futures price, futures volatility increased the spot volatility.

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

• The Journal of Fisheries Business Administration
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• v.37 no.1 s.70
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• pp.95-110
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• 2006

Japan's frozen shrimp future market is the only fisheries future commodity market in the world. This empirical study examines the lead and lag relationship between Japan frozen shrimp spot and future markets using the daily prices from August 1, 2002 to December 31, 2005. Frozen shrimp future contract is listed on Japan Kansai Commodities Exchange. Japan imports approximately 250,000 tons of frozen shrimp annually, of which just under 70,000 tons, nearly 30%, are black tiger shrimp. Approximately 90% of black tiger shrimp are caught in Indonesia, India, Thailand and Vietnam, and the two largest consumers of these shrimp are Japan and the U.S.A. Kansai Commodities Exchange adopts the India black tiger shrimp as standard future commodity. We use unit root test, Johansen cointegration test, Granger causality test, Vector autoregressive analysis and Impulse response analysis. However, considering the long - term relationships between the level variables of frozen shrimp spot and futures, we introduced Vector Error Correction Model. We find that the price change of frozen shrimp futures with next 1, 2, 3, 4, 5 month maturity have a strong predictive power to the change of frozen shrimp spot and the change of frozen shrimp spot also have a predictive power to the change of frozen shrimp with next 1, 2, 3 month maturity. But, the explanatory power of the frozen shrimp futures is relatively greater than that of frozen shrimp spot.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.147-158
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• 2021

Due to COVID-19, the risk of price volatility in commodity and equity markets increases. The research and application of hedging is the most effective way to reduce the market risk. Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. We use K-means and hierarchical clustering methods to cluster companies and futures products respectively, and analyze the relationship between the number of hedging firms, regional distribution, nature of firms, capital distribution, company size, profitability, number of local Futures Commission Merchants (FCMs), regional location, and listing time. The study shows that listed companies with large scale and good profitability invest more money in hedging, while state-owned enterprises' participation in hedging is more likely to be affected by the company size and the number of local futures commission merchants, and private enterprises are more likely to be affected by the company profitability and the regional location. Listed companies are more willing to choose long-listed and mature futures products for hedging. We also provide policy advice based on our conclusion. So far, there is no study on the characteristics of hedging. This paper fills the gap. The results provide a basis and guidance for people's investment and risk management. Using clustering analysis in hedging study is another innovation of this paper.