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Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling

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  • Bielak, Łukasz
  • Grzesiek, Aleksandra
  • Janczura, Joanna
  • Wyłomańska, Agnieszka

Abstract

Mining companies to properly manage their operations and be ready to make business decisions, are required to analyse potential scenarios for main market risk factors. The most important risk factors for KGHM, one of the biggest companies active in the metals and mining industry, are the price of copper (Cu), traded in US dollars, and the Polish zloty (PLN) exchange rate (USDPLN). The main scope of the paper is to understand the mid- and long-term dynamics of these two risk factors. For a mining company it might help to properly evaluate potential downside market risk and optimize hedging instruments. From the market risk management perspective, it is also important to analyse the dynamics of these two factors combined with the price of copper in Polish zloty (Cu in PLN), which jointly drive the revenues, cash flows, and financial results of the company. Based on the relation between analysed risk factors and distribution analysis, we propose to use two-dimensional vector autoregressive (VAR) model with the α-stable distribution. The non-homogeneity of the data is reflected in two identified regimes: first – corresponding to the 2008 crisis and second – to the stable market situation. As a natural implication of the model fitted to market assets, we derive the dynamics of the copper price in PLN, which is not a traded asset but is crucial for the KGHM company risk exposure. A comparative study is performed to demonstrate the effect of including dependencies of the assets and the implications of the regime change. Since for various international companies, risk factors are given rather in the national than the market currency, the approach is universal and can be used in different market contexts, like mining or oil companies, but also other commodities involved in the global trading system.

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  • Bielak, Łukasz & Grzesiek, Aleksandra & Janczura, Joanna & Wyłomańska, Agnieszka, 2021. "Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling," Resources Policy, Elsevier, vol. 74(C).
  • Handle: RePEc:eee:jrpoli:v:74:y:2021:i:c:s0301420721003184
    DOI: 10.1016/j.resourpol.2021.102308
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    as
    1. Ronald W. Cornew & Donald E. Town & Lawrence D. Crowson, 1984. "Stable distributions, futures prices, and the measurement of trading performance," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 4(4), pages 531-557, December.
    2. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    3. Michael Kateregga & Sure Mataramvura & David Taylor, 2017. "Parameter estimation for stable distributions with application to commodity futures log returns," Papers 1706.09756, arXiv.org.
    4. Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
    5. Reynolds, Douglas B., 1999. "The mineral economy: how prices and costs can falsely signal decreasing scarcity," Ecological Economics, Elsevier, vol. 31(1), pages 155-166, October.
    6. Hall, Joyce A. & Brorsen, B. Wade & Irwin, Scott H., 1989. "The Distribution of Futures Prices: A Test of the Stable Paretian and Mixture of Normals Hypotheses," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(1), pages 105-116, March.
    7. Hyun J. Jin, 2007. "Heavy‐tailed Behavior of Commodity Price Distribution and Optimal Hedging Demand," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(4), pages 863-881, December.
    8. W. David Walls, 1995. "An Econometric Analysis of the Market for Natural Gas Futures," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 71-84.
    9. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2015. "Structural breaks, dynamic correlations, asymmetric volatility transmission, and hedging strategies for petroleum prices and USD exchange rate," Energy Economics, Elsevier, vol. 48(C), pages 46-60.
    10. Rossen, Anja, 2015. "What are metal prices like? Co-movement, price cycles and long-run trends," Resources Policy, Elsevier, vol. 45(C), pages 255-276.
    11. Gallagher, Colin M., 2001. "A method for fitting stable autoregressive models using the autocovariation function," Statistics & Probability Letters, Elsevier, vol. 53(4), pages 381-390, July.
    12. F. Pozzi & T. Matteo & T. Aste, 2012. "Exponential smoothing weighted correlations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 85(6), pages 1-21, June.
    13. Baomin Dong & Xuefeng Li & Boqiang Lin, 2010. "Forecasting Long‐Run Coal Price in China: A Shifting Trend Time‐Series Approach," Review of Development Economics, Wiley Blackwell, vol. 14(3), pages 499-519, August.
    14. Miller, J. Isaac & Ni, Shawn, 2011. "Long-Term Oil Price Forecasts: A New Perspective On Oil And The Macroeconomy," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 396-415, November.
    15. Byrne, Joseph P. & Fazio, Giorgio & Fiess, Norbert, 2013. "Primary commodity prices: Co-movements, common factors and fundamentals," Journal of Development Economics, Elsevier, vol. 101(C), pages 16-26.
    16. Salles, Andre Assis de & Magrath, Raphael Sebastian & Malheiros, Matheus Manzani, 2019. "Determination of Copper Price Expectations in the International Market: Some Important Variables," MPRA Paper 95812, University Library of Munich, Germany, revised 31 Aug 2019.
    17. Gordon, R.B. & Bertram, M. & Graedel, T.E., 2007. "On the sustainability of metal supplies: A response to Tilton and Lagos," Resources Policy, Elsevier, vol. 32(1-2), pages 24-28.
    18. Baomin Dong & Xuefeng Li & Boqiang Lin, 2010. "Forecasting Long-Run Coal Price in China: A Shifting Trend Time-Series Approach," Review of Development Economics, Wiley Blackwell, vol. 14(s1), pages 499-519, August.
    19. Sadorsky, Perry, 2014. "Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat," Energy Economics, Elsevier, vol. 43(C), pages 72-81.
    20. Aleksander Janicki & Aleksander Weron, 1994. "Simulation and Chaotic Behavior of Alpha-stable Stochastic Processes," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook9401.
    21. Press, S. J., 1972. "Multivariate stable distributions," Journal of Multivariate Analysis, Elsevier, vol. 2(4), pages 444-462, December.
    22. Lee, Junsoo & List, John A. & Strazicich, Mark C., 2006. "Non-renewable resource prices: Deterministic or stochastic trends?," Journal of Environmental Economics and Management, Elsevier, vol. 51(3), pages 354-370, May.
    23. Liu, Chang & Hu, Zhenhua & Li, Yan & Liu, Shaojun, 2017. "Forecasting copper prices by decision tree learning," Resources Policy, Elsevier, vol. 52(C), pages 427-434.
    24. Paulauskas, V. J., 1976. "Some remarks on multivariate stable distributions," Journal of Multivariate Analysis, Elsevier, vol. 6(3), pages 356-368, September.
    25. Kausik Gangopadhyay & Abhishek Jangir & Rudra Sensarma, 2014. "Forecasting the price of gold: An error correction approach," Working papers 155, Indian Institute of Management Kozhikode.
    26. Janczura, Joanna & Orzeł, Sebastian & Wyłomańska, Agnieszka, 2011. "Subordinated α-stable Ornstein–Uhlenbeck process as a tool for financial data description," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4379-4387.
    27. Clinton Watkins & Michael McAleer, 2004. "Econometric modelling of non‐ferrous metal prices," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 651-701, December.
    28. Wyłomańska, Agnieszka & Chechkin, Aleksei & Gajda, Janusz & Sokolov, Igor M., 2015. "Codifference as a practical tool to measure interdependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 412-429.
    29. Kulshreshtha, Mudit & Parikh, Jyoti K., 2000. "Modeling demand for coal in India: vector autoregressive models with cointegrated variables," Energy, Elsevier, vol. 25(2), pages 149-168.
    30. Baldursson, Fridrik M., 1999. "Modelling the price of industrial commodities," Economic Modelling, Elsevier, vol. 16(3), pages 331-353, August.
    31. Szarek, Dawid & Bielak, Łukasz & Wyłomańska, Agnieszka, 2020. "Long-term prediction of the metals’ prices using non-Gaussian time-inhomogeneous stochastic process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    32. Roberts, Mark C., 2009. "Duration and characteristics of metal price cycles," Resources Policy, Elsevier, vol. 34(3), pages 87-102, September.
    33. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.
    34. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    35. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    36. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.
    37. M. Kateregga & S. Mataramvura & D. Taylor, 2017. "Parameter estimation for stable distributions with application to commodity futures log-returns," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1318813-131, January.
    38. R. Srinivasan, 1971. "On the Kuiper test for normality with mean and variance unknown," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 25(3), pages 153-157, September.
    39. Fasen, Vicky, 2013. "Statistical estimation of multivariate Ornstein–Uhlenbeck processes and applications to co-integration," Journal of Econometrics, Elsevier, vol. 172(2), pages 325-337.
    40. Joanna Nowicka-Zagrajek & Rafal Weron, 2002. "Modeling electricity loads in California: ARMA models with hyperbolic noise," HSC Research Reports HSC/02/02, Hugo Steinhaus Center, Wroclaw University of Technology.
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