• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.64-73
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• 2011

Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply- or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC.

• Korean System Dynamics Review
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• v.10 no.3
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• pp.105-124
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• 2009

The situation of the global food markets has been being rapidly restructured and entering on a new phase by new dynamic and driving forces. The factors such as economic growth and income increase, high energy price, globalization, urbanization, and global climate change are transforming patterns of food consumption, production, and markets. The prices and markets of world food and energy are getting increasingly linked each other. Food and fuel are the global dilemma issues associated with the risk of diverting farmland or of consuming cereals for biofuel production in detriment of the cereals supply to the global food markets. An estimated 100 million tons of grain per year are being redirected from food to fuel. Therefore, the objectives of this study are as follows: Firstly, the study examines situations of the world food and energy resources, analyzes the trends of prices of the crude oil and biofuel, and formulates the food-energy links mechanism. Secondly, the study builds a simulation model, based on system dynamics approach, for not only analyzing the global cereals market and energy market but also forecasting the global production, consumption, and stock of those markets by 2030 in the future. The model of this study consists of four sectors, i.e., world population dynamics sector, global food market dynamics sector, global energy market dynamics sector, scenario sector of world economic growth and oil price.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.4
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• pp.33-53
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• 2010

When purchase prices of a raw material fluctuate over time, the total purchasing cost is mainly affected by reordering time. Existing researches focus on deciding the right time when the demand for each period is replenished at the lowest cost. However, the decision is based on expected future prices which usually turn out to include some error. This discrepancy between expected prices and actual prices deteriorates the performance of inventory models dealing with fluctuating purchase prices. In this paper, we propose a new inventory model which incorporates not only cost but also risk into making up a replenishment schedule to meet each period's demand. For each replenishment schedule, the risk is defined to be the variance of its total cost. By introducing the risk into the objective function, the variability of the total cost can be mitigated, and eventually more stable replenishment schedule will be obtained. According to experimental results from crude oil inventory management, the proposed model showed better performance over other models in respect of variability and cost.

• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.119-131
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• 2021

Energy is an essential element in economic activity and people's lives, an important resource used by various industries, and the financialization of commodity markets has led to the growing importance of crude oil turning into the same asset as other assets. Accordingly, studies analyzing the correlation between energy prices and investor sentiment explain that investor sentiment affects oil prices through economic factors and speculation. In this study, we wanted to analyze whether the impact of the most representative changes in oil prices affects investor decision making, affecting investor sentiment, and applying wavelet consistency analysis to determine how energy prices relate to investor sentiment. Studies show that policies should be focused on policy and market changes because energy prices differ by time scale and investment sentiment should be more influential in the long term than in the short term.

• Journal of Information Processing Systems
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• v.20 no.1
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• pp.93-104
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• 2024

China is playing more predominant role in the liquefied natural gas (LNG) market worldwide and LNG import price is subject to various factors both at home and abroad. Nevertheless, previous studies rarely heed a multiple of factors. A time-varying parameter factor augmented vector auto-regression (TVP-FAVAR) model is adopted to discover the determinants of China's LNG import price and their dynamic impacts from January 2012 to December 2021. According to the findings, market fundamentals have a greater impact on the import price of natural gas in China than overall economic demand, financial considerations, and world oil prices. The primary determinants include domestic gas consumption, consumer confidence and other demand-side information. Then, there are diverse and time-varying spillover effects of the four common determinants on the volatility of China's LNG import price at different intervals and time nodes. The price volatility is more sensitive and long-lasting to domestic natural gas pricing reform than other negative shocks such as the Sino-US trade war and the COVID-19 pandemic. The results in this study further proves the importance of domestic natural gas market liberalization. China ought to do more to support the further marketization of natural gas prices while working harder to guarantee natural gas supplies.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.137-138
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• 2012

Domestic construction materials market was about 65 trillion won and it occupied 45% level of total construction cost by 2007. In addition, due to the recent rapid rise of crude oil and iron ore price, fluctuation of raw material cost has a great influence to the cost of construction industry. This means that smooth performance is closely related to construction materials. And among them, because of high putting rate of metal materials, it can be seen that the fluctuation of metal material prices is an important variables. So in this study, for the pre-study to analyze the impact of metallic material prices to construction cost, the researcher analyzed a causal relationship between metal material prices and construction cost.

• Information Systems Review
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• v.26 no.2
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• pp.45-57
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• 2024

This study develops a deep learning-based methodology for predicting Crude Palm Oil (CPO) prices. Palm oil is an essential resource across various industries due to its yield and economic efficiency, leading to increased industrial interest in its price volatility. While numerous studies have been conducted on palm oil price prediction, most rely on time series forecasting, which has inherent accuracy limitations. To address the main limitation of traditional methods-the absence of stationarity-this research introduces a novel model that uses the ratio of future prices to current prices as the dependent variable. This approach, inspired by return modeling in stock price predictions, demonstrates superior performance over simple price prediction. Additionally, the methodology incorporates the consideration of lag values of independent variables, a critical factor in multivariate time series forecasting, to eliminate unnecessary noise and enhance the stability of the prediction model. This research not only significantly improves the accuracy of palm oil price prediction but also offers an applicable approach for other economic forecasting issues where time series data is crucial, providing substantial value to the industry.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1037-1046
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• 2008

Recently, the oil suppliers who supply the tax-exempt oil to the fishery are confronted with big trouble in their supply and demand system due to the unstable global oil prices. We applied the seasonal ARIMA(SARIMA) model to the low-sulfur and high-sulfur crude oil which are in great request and developed forecasting systems for them. Since there are many parameters in SARIMA, it is difficult to estimate the optimal parameters, but it is overcome by using simulation looping program. In conclusion, we found that the obvious seasonality in demand of low-sulfur and these demands are tending downwards gradually.

• Environmental and Resource Economics Review
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• v.18 no.1
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• pp.139-158
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• 2009

With the sample of Middle East crude oil imported to South Korea, this study empirically analyzes the effectiveness of the risk management strategies using derivatives such as futures and options. Assuming the hedging period of one to twelve months, it considers a spot purchasing strategy, 1 : 1 futures hedge strategy, OLS-based minimum-variance futures hedge strategy, buying call option strategy, and collar transaction strategy. According to the ex-ante result, using the derivatives of futures or options makes lower the procurement costs when the crude oil prices is increasing. With the hedging period less than or equal to six months, the hedging strategy using futures turns out to be superior in terms of procurement cost reduction and hedging effectiveness improvement. In contrast, the hedging strategies of buying call option and collar transaction would generate better results when the hedging program last over six months.