[논문]유연탄 가격 예측 모형 개발에 관한 연구

김영진; 강희재

doi:10.18807/jsrs.2016.6.4.075

[국내논문] 유연탄 가격 예측 모형 개발에 관한 연구
Development of a Forecast Model for Thermal Coal Price 원문보기

서비스연구 = Journal of service research and studies, v.6 no.4, 2016년, pp.75 - 85

김영진 (서울대학교 경영대학원 생산관리 전공) , 강희재 (서울대학교 경영대학원 생산관리 전공)

초록
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석탄은 크게 유연탄과 무연탄으로 나눌 수 있다. 유연탄의 가격은 기본적으로 수요와 공급에 의해 영향을 받으며, 환율, 경제성장률과 같은 경제적 여건과 관련된 많은 요인들에 의해 좌우된다. 본 연구에서는 시스템 다이내믹스 방법을 이용하여 유연탄 가격을 예측하는 모델을 개발하였다. 이에 따라 변수들 간의 상호의존적 관계를 고려함으로서 실세계를 충분히 반영한 결과를 도출하였다. 본 연구에서는 유연탄의 가격을 결정하는 핵심 요인 8개를 분석하였으며, 대부분의 데이터는 2011년 5월부터 2013년 8월까지 2년 4개월간의 블룸버그 데이터베이스를 활용하였다. 인과지도는 회귀분석을 이용하였다.

Abstract ▼ AI-Helper

Coal can be divided into thermal coal and coking coal. The price of thermal coal is basically affected by demand and supply. However, many other factors with regard to economic condition such as exchange rate, economy growth rate also make an influence on the price. This study is targeted to develop a forecast model for thermal coal price by using System Dynamics Method. System dynamics provides results that better reflect the real world by employing an inter-dependent system of variables. This study found out that 8 factors have important influence on the thermal coal price. Most of the data of the variables were acquired from the Bloomberg Database. The period extends to 2 years and 4 months, from May of 2011 to August of 2013. The causal relations among the variables were acquired by regression analysis

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문제 정의

Because thermal coal market size occupies more than 80% of the coal market and the two coals differ from market transaction, it is very reason to promote a separate study. Therefore, this study focuses on a methodology to estimate the purchase price of thermal coal only.
In this study, domestic power generation companies are to buy directly, the purchase price of thermal coal, which accounts for a high proportion of imported coking coal market to take advantage of modeled system dynamics method. To this end, this paper defines the factors that affect the international coal market and to take advantage of the system is to identify the model mix wife and their relationships to establish a model that can predict the international coal price.
In addition, the congruency of the data is one of the important criteria as the system dynamics analysis involves data collection and its analysis. This study is targeted to forecast the monthly change of the thermal coal price, for which the data should have monthly or more frequent records for the model.
The synchronous movement is expected to be strengthened due to the increase of the international liquidity and the development of the spot market. This study reflects the price synchronization phenomena by including the crude oil price as well as that of copper and liquefied natural gas. Moreover, the difference between the spot and future prices of alternative energy is included as well in order to predict the variability of the thermal coal price in the future.

가설 설정

③ Currency exchange rate: Currency exchange rate is not directly related with the thermal coal price. However, the transactions in the world market involve the US dollar, as a key currency, and local currencies, and the exchange rate against the US dollar is one of the variables that affects the export amount of thermal coal.

제안 방법

In this study, domestic power generation companies are to buy directly, the purchase price of thermal coal, which accounts for a high proportion of imported coking coal market to take advantage of modeled system dynamics method. To this end, this paper defines the factors that affect the international coal market and to take advantage of the system is to identify the model mix wife and their relationships to establish a model that can predict the international coal price.
In this study, we establish a simulation model based on a system dynamics method to estimate the market price of thermal coal.
The analysis on the data to maintain their congruency excluded the world supply and the import on bituminous coal, the precipitation by regions, and the amount of alternative energy due to the difficulties in data acquisition, and selected eight variables for the system dynamics model.
In order to establish a model to predict the price of thermal coal, this study built a study model that forecasts the price by employing the system dynamics technique based on the previous studies that predicted the price of major raw materials such as iron ore and nickel.
Variables that may affect the price of thermal coal were evaluated, from which eight variables were selected that directly influence the price and are in accessible data forms, based on the importance and data congruency of the variables. Finally, the system dynamics model was built while considering the causal relations among the variables and compared the predicted results with the real data collected in the duration of 2 years and 4 months.
The study used the system dynamics technique in order to make a prediction model of the thermal coal price from the causal relations among the variables. Various variables were evaluated and shortlisted to see their influence on the thermal coal price, which are then analyzed to see the causal relations.
Though the model did not reflect the influence of the price changes in other countries, it substituted the influence by calculating the amount of imported bituminous coal from the electricity supply of each country. This study targeted to predict a monthly change of the thermal coal price, but could not reflect the price change due to the variations in the market condition. It did not include the meteorological data which directly influences the supply and amount of export of thermal coal due to the difficulties in collecting accurate data on the precipitation.
The imported amount of bituminous coal and electricity supply of Korea were acquired from the website of the Korea Energy Statistics Information System. Data sets recorded in a daily basis, such as the currency exchange rates, the stock indexes, the alternative energy price, and the freight charge, were converted into a monthly basis by a monthly average. The changes in the world economy and the alternative energy price were modified to their change ratios, where the change of the world economy was represented by the ratio over the past month and that of the alternative energy price by the change ratio of the spot price over the future price.

대상 데이터

Preliminary qualitative evaluation on the variables relevant to the price was carried out with the discussion with the experts at the power generation companies for purchasing coal, where twelve variables were selected among them. The analysis on the data to maintain their congruency excluded the world supply and the import on bituminous coal, the precipitation by regions, and the amount of alternative energy due to the difficulties in data acquisition, and selected eight variables for the system dynamics model.

데이터처리

The causal relations among the variables were acquired by regression analysis.

후속연구

Thermal coal is mostly used for power generation whereas coking coal is mainly used in the steel industry. Because thermal coal market size occupies more than 80% of the coal market and the two coals differ from market transaction, it is very reason to promote a separate study. Therefore, this study focuses on a methodology to estimate the purchase price of thermal coal only.

참고문헌 (7)

Kwak, S. M., "Development of a System Dynamics Model for Forecasting the Automobile Market", Journal of Korean System Dynamics Research, Vol. 3, No. 1 (2002), pp. 79-104
Jeong, S. J., J. H. Song, K. S. Kim, and J. W. Park. "The Effect of the Carbon Tax on Steel Industry using System Dynamics", Journal of Korean System Dynamics Research, Vol. 8, No. 2 (2007) pp.115-140
Jung, J. H., "A System Dynamics Model for Basic Material Price and Fare Analysis and Forecasting", Journal of Korean System Dynamics Research, Vol. 10, No. 1 (2009), pp.61-76
Jung, J. H., "Resource Demand/Supply and Price Forecasting -A Case of Nickel-", Journal of Korean System Dynamics Research, Vol. 9, No. 1 (2008), pp.125-141
Analysis of Import and Export of 6 Strategic Minerals, Korea Trade Statistics Promotion Institute (2013)
International coal market trends and outlook, Korea Energy Economics Institute (2009)
Analysis of coal market structure change and response strategies, Korea Energy Economics Institute (2011)

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