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The course of realized volatility in the LME non-ferrous metal market

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  • Todorova, Neda

Abstract

This study analyzes the volatility of LME futures contracts on aluminum, copper, lead, nickel and zinc using high-frequency data. The dynamics of realized volatility is studied over the period from January 2004 to September 2012 and is shown to be well captured with a rolling HAR-GARCH model. An increasing importance of short-term volatility components is evident across all metals in the aftermath of the global financial crisis and the related sharp decline in industrial metal prices. The heavier impact of short-term volatility corresponds consistently to a decreasing persistence of the volatility of realized volatility. In terms of predictive accuracy, even though leverage effects or GARCH-terms related to the volatility of realized volatility are characterized for the most part by significant in-sample parameters, the plain HAR model cannot be consistently outperformed by explicitly accounting for these stylized facts.

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  • Todorova, Neda, 2015. "The course of realized volatility in the LME non-ferrous metal market," Economic Modelling, Elsevier, vol. 51(C), pages 1-12.
    • Handle: RePEc:eee:ecmode:v:51:y:2015:i:c:p:1-12
    DOI: 10.1016/j.econmod.2015.07.005
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    3. 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.
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    9. Klein, Tony & Todorova, Neda, 2019. "Night Trading with Futures in China: The Case of Aluminum and Copper," QBS Working Paper Series 2019/06, Queen's University Belfast, Queen's Business School.
    10. 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.
    11. Narayan, Paresh Kumar & Sharma, Susan Sunila & Phan, Dinh Hoang Bach, 2016. "Asset price bubbles and economic welfare," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 139-148.
    12. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2021. "Economic drivers of commodity volatility: The case of copper," Resources Policy, Elsevier, vol. 73(C).
    13. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2020. "A random walk through the trees: Forecasting copper prices using decision learning methods," Resources Policy, Elsevier, vol. 69(C).
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    18. Ma, Richie Ruchuan & Xiong, Tao, 2021. "Price explosiveness in nonferrous metal futures markets," Economic Modelling, Elsevier, vol. 94(C), pages 75-90.
    19. Huang, Zhuo & Liu, Hao & Wang, Tianyi, 2016. "Modeling long memory volatility using realized measures of volatility: A realized HAR GARCH model," Economic Modelling, Elsevier, vol. 52(PB), pages 812-821.
    20. Shi, Wendong & Sun, Jingwei, 2016. "Aggregation and long-memory: An analysis based on the discrete Fourier transform," Economic Modelling, Elsevier, vol. 53(C), pages 470-476.
    21. Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2020. "Spillovers, integration and causality in LME non-ferrous metal markets," Journal of Commodity Markets, Elsevier, vol. 17(C).
    22. Xuan Yao & Xiaofeng Hui & Kaican Kang, 2021. "Can night trading sessions improve forecasting performance of gold futures' volatility in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 849-860, August.
    23. Liu, Yanqiong & Guo, Yaoqi & Wei, Qing, 2024. "Time-varying and multi-scale analysis of copper price influencing factors based on LASSO and EMD methods," Journal of Commodity Markets, Elsevier, vol. 34(C).

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    More about this item

    Keywords

    Industrial metals; High-frequency data; HAR-GARCH model; Rolling estimation; Volatility forecasting;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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