# Testing the correlation between mean reversion process and grey system theory for metal price forecasting

## Test korelacije između procesa povratka na srednju vrednost i teorije sivog sistema u svrhu prognoze cene metala

Keywords:
metal price; uncertainty; Mean Reversion Process; Grey System Theory; Intra-class Correlation Coefficient

### Abstract

There are typically many of variables, which are directly or indirectly associated with the value of underground mine project. Having the ability to plan for uncertainties of input variables is increasingly recognized as critical to longterm mining project success. Large capital intensive projects, such as those in the mineral resource industry, are often associated with diverse sources of both internal and external uncertainties. One of the most external influencing uncertainties is related to the future states of metal price. There are many methods which are applied to forecast the future metal prices, but Mean Reversion Process is one of the most applying methods. This paper analyzes the possibility of using of Grey System Theory to metal price forecasting by examining the correlation between results obtained by these two methods. Intra-class Correlation Coefficient is used as a measure of reliability.### References

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KAYACAN, E. et al. (2010), Grey system theory-based models in time series prediction. Expert Systems with Applications, 37, pp.1784-1789.

KAZAKIDIS, V.N. and SCOBLE, M. (2003), Planning for flexibility in underground mine production system. SME Mining Engineering Journal, 55(8), pp.16-21.

LEE, J. et al. (1989) Statistical evaluation of agreement between two methods for measuring a quantitative variable. Comput. Biol. Med., 19, pp.61-70.

LIU, S. and LIN, Y. (1998), An introduction to grey systems: Foundations, Methodology and Applications. New York: IIGSS Academic Publisher.

SCHWARTZ, E. (1997), The Stochastic Behaviour of Commodity Prices: Implications for Valuation and Hedging. The Journal of Finance, 52(3), pp.923-973.

YANG, Y.W. et al. (2012) A Fuzzy-Grey Model for Non-stationary Time series Prediction. Applied Mathematics and Information Sciences, 6(2S), pp.445S-451S.

DENG, J.L. (1982) Control problems of grey system. Systems and Control Letters, 1(5), pp.211-215.

DENG, J.L. (1989) Introduction to grey system theory. The Journal of Grey System, 1, pp.1-24.

DIXIT, A.K. and PINDYCK, R.S. (1994) Investment under Uncertainty. Princeton: Princeton University Press.

DOROS, G. and LEW, R. (2010) Design Based on Intra-Class Correlation Coefficients. American Journal of Biostatistics, 1 (1), pp.1-8.

INDRAYAN, A. (2013), Clinical Agreement in Quantitative Measurments: Limits of Disagreement and the Intraclass Correlation. In: DOI, S.A.R. and

WILLIAMS, G.M. (eds.), Methods of Clinical Epidemiology. Berlin Heilderberg: Springer-Verlag, pp.17-27.

KAYACAN, E. et al. (2010), Grey system theory-based models in time series prediction. Expert Systems with Applications, 37, pp.1784-1789.

KAZAKIDIS, V.N. and SCOBLE, M. (2003), Planning for flexibility in underground mine production system. SME Mining Engineering Journal, 55(8), pp.16-21.

LEE, J. et al. (1989) Statistical evaluation of agreement between two methods for measuring a quantitative variable. Comput. Biol. Med., 19, pp.61-70.

LIU, S. and LIN, Y. (1998), An introduction to grey systems: Foundations, Methodology and Applications. New York: IIGSS Academic Publisher.

SCHWARTZ, E. (1997), The Stochastic Behaviour of Commodity Prices: Implications for Valuation and Hedging. The Journal of Finance, 52(3), pp.923-973.

YANG, Y.W. et al. (2012) A Fuzzy-Grey Model for Non-stationary Time series Prediction. Applied Mathematics and Information Sciences, 6(2S), pp.445S-451S.

Published

2017-03-19

How to Cite

*Podzemni Radovi*, (24), 61-71. https://doi.org/10.5937/podrad1424061G

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Articles