On the Optimal Dynamic Hedging with Nonferrous Metals

Eric Martial Etoundi Atenga (Faculty of Economic Sciences and Management University of Yaoundé 2 Cameroon)

Abstract


This paper employs multivariate GARCH to model conditional correlations and to examine volatility spillovers and hedging possibilities with nonferrous metals traded on the London Metal Exchange (LME) market. Three different multivariate GARCH models (diagonal, CCC and DCC) are employed and contrasted. The nonferrous metals studied are copper, aluminum, tin, lead, zinc and nickel and span the period from January 6, 2000 to February 29, 2016. The multivariate DCC GARCH framework is found to fit the data in an appropriate design and provides results showing the strongest evidence of long-term persistence volatility spillovers between lead and aluminum. We also find that the Hurst exponents given by the R/S method are on average 0.94, indicating the existence of a strong degree of long-range dependence in conditional volatilities. On average, the cheapest hedge is a long position in lead and a short position in nickel. The most expensive hedge is long nickel and short copper.


Keywords


Conditional correlation; Spillovers; Portfolio weight

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References


[1] McAleer, M., and Watkins, C. Econometric modelling of nonferrousmetal prices [C]. Journal of Economic Surveys, 18(5): 651-701.

[2] Watkins, C., and McAleer , M. Pricing nonferrous metals futures on the London Metal Exchange [C]. Applied Financial Economics, 16: 853-880.

[3] Fama, E. F., and French, K.R. Business cycles and the behaviour of metals prices [C]. Journal of Finance, 43: 1075-1093.

[4] Brunetti , C., and Gilbert, C. Metals price volatility [C]. Resources policy, 21: 237-254.

[5] Brunetti, C., and Gilbert, C. Are metal's prices becoming more volatile [C] Proceedings of the Mineral Economics and Management: 58-72.

[6] Bracker, K., and Smith , K. Detecting and modelling changing volatility in the copper [C] furures market. Journal of Futures Markets, 19: 79-100.

[7] McMillan, D., and Speight, A. Nonferrous metals price volatility: A component analysis [C]. Resources Poolicy, 27: 199-207.

[8] Cochran, S., Mansur , I., and Odusami, B. Volatility perisistence in metal returns: A FIGARCH approach [C]. Journal of Economics and Business, 64: 287-305.

[9] Franses, P. H., and Kofman, P. An empirical test for parities between metal prices at the LME [C]. Journal of Futures Markets, 11: 729-736.

[10] Agbeyegbe, T. Common stochastic trends: evidence from the London Metal Exchange [C]. Bulletin of Economic Researchs, 44: 141-151.

[11] Todorova, N., Worthingtion, A., and Soucek, M. Realized volatility spillovers in the nonferrousmetal futures market [C]. Resources Policy, 39: 21-31.

[12] Shyy, G., and Butcher, B. Price equilibrium and transmission in a controlled economy: a case study of the metal exchange in China [C]. Journal of Futures Markets, 14: 877-890.

[13] Li, X., and Zhang, B. Prices linkages between Chinese and world copper futures markets [C]. Frontiers of Economics in China, 3, (3): 451-461.

[14] Li, X., and Zhang, B. Price discovery for copper futures in informationally linked markets [C]. Applied Economics Letters, 16(15): 1555-1558.

[15] Sinha, P., and Mathur, K.. Price, return and volatility linkages of base metal futures traded in India [C]. 2013. Retrieved from MPRA:http://mpra.ub.uni-muenchhen.de/47864

[16] Yue, Y.-D., Liu, D.-C., and Xu, S. Price linkage between Chinese and international nonferrous metals commodity markets based on VAR-DCC-GARCH models [C]. Transactions of Nonferrous Metals Society of China, 25: 1020−1026.

[17] Sadorsky, P. Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies [C]. Energy economics, 34: 248-255.

[18] Ling, S., and McAleer, M. Asymptotic theory for a vector ARMA-GARCH model [C]. Econometric Theory, 19: 278-308.

[19] Engle, R. Dynamic conditional correlation — A simple class of multivariate GARCH models [C]. Journal of Business and Economic Statistics, 20: 339-350.

[20] Hurst, H. E. Long term storage capacity of reservoirs [C]. Transactions of the American Society of Civil Engineers, 116: 770-808.

[21] Engle, R. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation [C]. Econometrica, 50: 987-1007.

[22] Kroner, K., and Sultan, J. Time dynamic varying distributions and dynamic hedging with foreign currency futures [C]. Journal of Financial and Quantitative Analysis, 2: 535-551.

[23] Kroner, K., and Ng, V. Modeling asymmetric movements of asset prices [C]. Review of Financial Studies, 11: 817-844.



DOI: https://doi.org/10.30564/jesr.v2i2.450

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