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ECONOMY, ORGANIZATION AND MANAGEMENT
ArticleName Risk-management system for gold mining companies
DOI 10.17580/gzh.2019.08.08
ArticleAuthor Nazarova V. V., Bakharev V. V., Kapustina I. V., Chargazia G. G.
ArticleAuthorData

National Research University Higher School of Economics, Saint-Petersburg, Russia:

V. V. Nazarova, Associate Professor, Candidate of Economic Sciences, nvarvara@list.ru

 

Peter the Great Saint-Petersburg Polytechnic University, Saint-Petersburg, Russia:

V. V. Bakharev, Associate Professor, Candidate of Economic Sciences
I. V. Kapustina, Associate Professor, Candidate of Economic Sciences
G. G. Chargazia, Associate Professor, Candidate of Economic Sciences

Abstract

Russian gold mining industry is one of the world’s top gold producers. Under conditions of globalization, gold mining has stepped out of the country limits. The Russian market of gold follows the global trends. This article completes a quantitative assessment of risks in the gold mining industry, and proposes a risk management system capable to minimize after-effects of the key risks. The theoretical relevance of this study is governed by the fact that risk assessment in mining and, in particular, in gold mining is carried out through the analysis of individual risks while this article suggest an integrated risk management system. The study has revealed that the major influence on the finance result of gold mining companies in Russia is exerted by the U.S. dollar to Russian ruble rates (major risk), market spot price per ounce of gold in terms of the U.S. dollar and the refinance rate set by the Central Bank. The research finds that the gold price development has greatly and proportionally influences earnings before interests and taxes and amortization. The rise of the average annual dollar rate has positive effect on the earnings of a god mining company before interests and taxes and amortization. The calculations show that the currency risk is the basis risk of gold mining companies, even outrunning risk of fall in gold prices. The analysis of the risk management philosophy in the gold mining industry yields a conclusion on the required currency risk hedging. It is proved that the effect of gold price hedging by means of forward contracts is overestimated; this strategy has the inverse effect on risks of a gold mining company by increasing influence of gold price fluctuations on profit return. The most effective risk management strategy is assumed to use gold options and to set limit prices for asset sales. Sometimes, it is possible to reduce risks by using cross currency and interest rate option. The latter financial instrument can mitigate adverse effect of the interest rate risk but can increase the currency risk at the same time.

keywords Risk management, gold mining industry, gold market, derivatives, non-financial companies
References

1. Kotlyarov I. D., Petrov S. V. Risk assessment procedure for economicgeological and cost estimate of mineral deposits. Gornyi Zhurnal. 2014. No. 9. pp. 94–99.
2. Petrov S. V., Kotlyarov I. D., Katsnelson A. B., Sen’ M. S. Forecasting the price of gold in ground. Obogashchenie Rud. 2016. No. 2. pp. 3–8. DOI: 10.17580/or.2016.02.01
3. Review of Russian gold-mining branch for 2015–2016. Available at : https://www.ey.com/Publication/vwLUAssets/ey-gold-survey-2017/%24File/ey-gold-survey-2017.pdf (accessed: 31.01.2019).
4. Shvets S. K. Integrated metrics of risk assessment of the non-financial company. Izvestiya Sankt-Peterburgskogo gosudarstvennogo ekonomicheskogo universiteta. 2015. No. 5. pp. 72–77.
5. Ng Ghim Hwee, Tiong R. L. K. Model on cash flow forecasting and risk analysis for contracting firms. International Journal of Project Management. 2002. Vol. 20, Iss. 5. pp. 351–363.
6. Andrén N., Jankensgård Н., Oxelheim L. Exposure-Based Cash-Flow-at-Risk for Value-Creating Risk Management under Macroeconomic Uncertainty : IFN Working Paper. 2010. No. 843. Available at: https://www.ifn.se/wfiles/wp/wp843.pdf (accessed: 19.04.2019).
7. Andrén N., Jankensgård Н., Oxelheim L. Exposure‐Based Cash‐Flow‐at‐Risk: An Alternative to VaR for Industrial Companies. Journal of Applied Corporate Finance. 2005. Vol. 17, Iss. 3. pp. 76–86.
8. DeFond M. L., Hung M. An empirical analysis of analysts’ cash flow forecasts. Journal of Accounting and Economics. 2003. Vol. 35, Iss. 1. pp. 73–100.
9. Kaplan S. N., Rubak R. S. The Valuation of Cash Flow Forecasts: An Empirical Analysis. The Journal of Finance. 1995. Vol. 50, No. 4. pp. 1059–1093.
10. Gaur A., Bansal M. A Comparative Study of Gold Price Movements in Indian and Global Markets. Indian Journal of Finance. 2010. Vol. 4, Iss. 2. pp. 32–37.
11. Junying Liu, Feng Jin, Qunxia Xie, Skitmore М. Improving risk asse ssment in financial feasibility of international engineering projects: A risk driver perspective. International Journal of Project Management. 2017. Vol. 35, Iss. 2. pp. 204–211.
12. Fang V., Chien-Ting Lin, Poon W. An examination of Australian gold mining firms’ exposure over the collapse of gold price in the late 1990s. International Journal of Accounting & Information Management. 2007. Vol. 15, No. 2. pp. 37–49.
13. Cherkasova V. A. Selection of an efficient strategy for hedging the currency risks of an oil company. Finansovaya analitika: problemy i resheniya. 2013. No. 26(164). pp. 26–33.
14. Berganza J. C., Broto C. Flexible inflation targets, forex interventions and exchange rate volatility in emerging countries. Journal of International Money and Finance. 2012. Vol. 31, Iss. 2. pp. 428–444.
15. Barry C. B., Mann S. C., Mihov V., Rodríguez M. Interest rate changes and the timing of debt issues. Journal of Banking & Finance. 2009. Vol. 33, Iss. 4. pp. 600–608.
16. Galvao A. F., Montes-Rojas G., Sosa-Escudero W., Liang Wang. Tests for skewness and kurtosis in the one-way error component model. Journal of Multivariate Analysis. 2013. Vol. 122. pp. 35–52.
17. System. Interfaks, 2019. Available at: http://www.spark-interfax.ru/ru/about (accessed: 12.04.2019).
18. Project. Interfaks-TsRKI, 2019. Available at: https://www.e-disclosure.ru/o-proekte/o-proekte (accessed: 01.04.2019).
19. Indices. Moscow Exchange. Available at: https://www.moex.com/ru/indices (accessed: 22.04.2019).
20. Efficiency of the Russian economy. Federal State Statistic s Service. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/efficiency/ (accessed: 31.01.2019).
21. Adam T. R., Fernando C. S., Salas J. M. Why do firms engage in selective hedging? Evidence from the gold mining industry. Journal of Banking & Finance. 2017. Vol. 77. pp. 269–282.
22. Dönmez D., Grote G. Two sides of the same coin – how agile software development teams approach uncertainty as threats and opportunities. Information and Software Technology. 2018. Vol. 93. pp. 94–111.
23. Lamanda G., Võneki Z. T. Hungry for Risk. A risk appetite framework for operational risks. Public Finance Quarterly. 2015. Vol. 60, Iss. 2. pp. 212–225.
24. Mulcahy M. B., Boylan C., Sigmann S., Stuart R. Using bowtie methodology to support laboratory hazard identification, risk management, and incident analysis. Journal of Chemical Hea lth and Safety. 2017. Vol. 24, Iss 3. pp. 14–20.

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