IDEAS home Printed from https://ideas.repec.org/r/bla/jecsur/v18y2004i5p651-701.html

Econometric modelling of non‐ferrous metal prices

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Su, Chi-Wei & Wang, Xiao-Qing & Zhu, Haotian & Tao, Ran & Moldovan, Nicoleta-Claudia & Lobonţ, Oana-Ramona, 2020. "Testing for multiple bubbles in the copper price: Periodically collapsing behavior," Resources Policy, Elsevier, vol. 65(C).
  2. Mikael Collan & Jyrki Savolainen & Pasi Luukka, 2017. "Investigating the effect of price process selection on the value of a metal mining asset portfolio," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 30(2), pages 107-115, July.
  3. Lien, Donald & Yang, Li, 2008. "Hedging with Chinese metal futures," Global Finance Journal, Elsevier, vol. 19(2), pages 123-138.
  4. Sergio Lehmann & David Moreno & Patricio Jaramillo, 2007. "China, Commodity Prices and Latin American Performance: A Few Stylized Facts," Working Papers Central Bank of Chile 424, Central Bank of Chile.
  5. Nidhi Choudhary & Girish K. Nair & Harsh Purohit, 2015. "Volatility In Copper Prices In India," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-26, December.
  6. 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).
  7. C. A. Tapia Cortez & J. Coulton & C. Sammut & S. Saydam, 2018. "Determining the chaotic behaviour of copper prices in the long-term using annual price data," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 4(1), pages 1-13, December.
  8. Zhu, Xuehong & Zhang, Hongwei & Zhong, Meirui, 2017. "Volatility forecasting using high frequency data: The role of after-hours information and leverage effects," Resources Policy, Elsevier, vol. 54(C), pages 58-70.
  9. Todorova, Neda & Worthington, Andrew & Souček, Michael, 2014. "Realized volatility spillovers in the non-ferrous metal futures market," Resources Policy, Elsevier, vol. 39(C), pages 21-31.
  10. Patricio Jaramillo & Sergio Lehmann & David Moreno., 2009. "China, Precios de Commodities y Desempeño de América Latina: Algunos Hechos Estilizados," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 46(133), pages 67-105.
  11. Qu, Qiushi & Wang, Limao & Cao, Zhi & Zhong, Shuai & Mou, Chufu & Sun, Yanzhi & Xiong, Chenran, 2019. "Unfolding the price effects of non-ferrous industry chain on economic development: A case study of Yunnan province," Resources Policy, Elsevier, vol. 61(C), pages 1-20.
  12. 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.
  13. Watkins, Clinton & McAleer, Michael, 2008. "How has volatility in metals markets changed?," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 237-249.
  14. Cifuentes, Sebastián & Cortazar, Gonzalo & Ortega, Hector & Schwartz, Eduardo S., 2020. "Expected prices, futures prices and time-varying risk premiums: The case of copper," Resources Policy, Elsevier, vol. 69(C).
  15. 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.
  16. Khoshalan, Hasel Amini & Shakeri, Jamshid & Najmoddini, Iraj & Asadizadeh, Mostafa, 2021. "Forecasting copper price by application of robust artificial intelligence techniques," Resources Policy, Elsevier, vol. 73(C).
  17. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2013. "Forecasting metal prices: Do forecasters herd?," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 150-158.
  18. Todorova, Neda, 2015. "The course of realized volatility in the LME non-ferrous metal market," Economic Modelling, Elsevier, vol. 51(C), pages 1-12.
  19. Han, Xuyuan & Liu, Zhenya & Wang, Shixuan, 2022. "An R-vine copula analysis of non-ferrous metal futures with application in Value-at-Risk forecasting," Journal of Commodity Markets, Elsevier, vol. 25(C).
  20. Tomas Brabenec & Josef Montag, 2016. "Criminals and the Price System: Evidence from Czech Metal Thieves," CERGE-EI Working Papers wp558, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
  21. K. Triantafyllopoulos, 2008. "Multivariate stochastic volatility with Bayesian dynamic linear models," Papers 0802.0214, arXiv.org.
  22. Cochran, Steven J. & Mansur, Iqbal & Odusami, Babatunde, 2012. "Volatility persistence in metal returns: A FIGARCH approach," Journal of Economics and Business, Elsevier, vol. 64(4), pages 287-305.
  23. Mohamed El Hedi Arouri & Fredj Jawadi & Prosper Mouak, 2013. "Testing the efficiency of the aluminium market: evidence from London metal exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 23(6), pages 483-493, March.
  24. Savolainen, Jyrki, 2016. "Real options in metal mining project valuation: Review of literature," Resources Policy, Elsevier, vol. 50(C), pages 49-65.
  25. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2013. "A note on forecasting the prices of gold and silver: Asymmetric loss and forecast rationality," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(3), pages 294-301.
  26. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
  27. Cummins, Mark & Dowling, Michael & Lucey, Brian M., 2015. "Behavioral influences in non-ferrous metals prices," Resources Policy, Elsevier, vol. 45(C), pages 9-22.
  28. Chen, Xiangyu & Tongurai, Jittima, 2022. "Spillovers and interdependency across base metals: Evidence from China's futures and spot markets," Resources Policy, Elsevier, vol. 75(C).
  29. Donghua Wang & Yang Xin & Xiaohui Chang & Xingze Su, 2021. "Realized volatility forecasting and volatility spillovers: Evidence from Chinese non‐ferrous metals futures," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2713-2731, April.
  30. Su, Chi Wei & Song, Xin Yue & Qin, Meng & Lobonţ, Oana-Ramona, 2024. "Is copper a safe haven for oil?," Resources Policy, Elsevier, vol. 91(C).
  31. K. Triantafyllopoulos, 2007. "Covariance estimation for multivariate conditionally Gaussian dynamic linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 551-569.
  32. Konstantinos Oikonomou & Dimitris Damigos, 2025. "Short term forecasting of base metals prices using a LightGBM and a LightGBM - ARIMA ensemble," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 37-49, March.
  33. Buncic, Daniel & Moretto, Carlo, 2015. "Forecasting copper prices with dynamic averaging and selection models," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
  34. Triantafyllopoulos, K. & Nason, G.P., 2007. "A Bayesian analysis of moving average processes with time-varying parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1025-1046, October.
  35. Chen, Xiangyu & Tongurai, Jittima, 2024. "Revisiting the interdependences across global base metal futures markets: Evidence during the main waves of the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 70(PB).
  36. Triantafyllopoulos, Kostas, 2006. "Multivariate discount weighted regression and local level models," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3702-3720, August.
  37. Todorova, Neda & Clements, Adam E., 2018. "The volatility-volume relationship in the LME futures market for industrial metals," Resources Policy, Elsevier, vol. 58(C), pages 111-124.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.