Kalman Filtering-Based Approach for Project Valuation of an Iron Ore Mining Project Through Spot Price and Long-Term Commitment Contracts
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Iron ore was traditionally traded using long-term commitment (LTC) contracts. In the last decade, with the surging demand from China, a futures market was created for iron ore. In this paper, using historical information from this futures market, we focus on modeling market dynamics of Iron Fine 62% Fe—CFR Tianjin Port (China) futures contracts to determine optimal parameter values of the Schwartz (J Financ 52:923–973, 1997) two-factor model. A new approach using LTC and futures contracts is proposed to assess the Net Present Value (NPV) of an iron ore mining project. We apply Kalman filtering techniques to calibrate the two-factor commodity model to iron ore futures for the January 2014–November 2016 period. The Kalman filter is useful to infer unobservable variables from noisy measurements. In the Schwartz (1997) two-factor model, the unobservable spot price and convenience yield are inferred from futures contracts transactions. Model parameters are fitted using maximum likelihood optimization. Using parameters derived from the Kalman filtering and the maximum likelihood approach, spot price simulations for the next 7 years are made for three scenarios. The NPV of a mining project is calculated for each scenario. Then, both LTC and futures markets are treated separately and the mining company can choose which proportion of its production to sell in each market. Results show that the calibration and NPV simulation workflow can be effectively used to assess the profitability of a mining project, accounting for the exposure to futures markets.
KeywordsKalman filter Iron ore futures Maximum likelihood optimization Schwartz (1997) model Commodity prices NPV valuation
The authors gratefully thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for support (Project No. 435661).
- Chicago Mercantile Exchange. (2015). Futures and options trading for risk management. Retrieved January 4, 2017 from www.cmegroup.com.
- Erb, P., Luethi, D., Hinz, J., & Otziger, S. (2014). schwartz97: A package on the Schwartz two-factor commodity model. R package version 0.0.6.Google Scholar
- Geman, H. (2009). Commodities and commodity derivatives: Modeling and pricing for agriculturals, metals and energy. The Wiley finance series. West Sussex: Wiley.Google Scholar
- marketrealist.com. (2015). China’s steel production is greatly influencing iron ore prices.Google Scholar
- Masteika, S., Rutkauskas, A. V., & Alexander, J. A. (2012). Continuous futures data series for back testing and technical analysis. In Conference proceedings, 3rd international conference on financial theory and engineering (Vol. 29, pp. 265–269).Google Scholar
- McTaggart, R., Daroczi, G., & Leung, C. (2015). Quandl: API Wrapper for Quandl.com. R package version 2.7.0.Google Scholar
- Platts. (2016). The Steel Index. Retrieved September 28, 2016 from https://www.thesteelindex.com.
- R Core Team. (2015). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical ComputingGoogle Scholar
- Ribeiro, D. R. & Hodges, S. D. (2004). A two-factor model for commodity prices and futures valuation. EFMA 2004 Basel Meetings Paper.Google Scholar
- Rudenno, V. (1998). The mining valuation handbook. Elsternwick: Wrightbooks.Google Scholar
- U.S. Commodity Futures Trading Commission. (2015). Weekly commitment of traders reports. Retrieved March 16, 2016 from www.cftc.gov.