A STATISTICAL APPROACH TO THE PRICES VOLATILITY OF NON-FERROUS METALS

Tiago Silveira Gontijo, Alexandre de Cássio Rodrigues, Andressa Amaral de Azevedo

Resumen


Analyze the Aluminum, Copper, Nickel and Zinc behavior in terms of price variation, has a notable relevance. In order to capture the conditional volatility terms and identify its reaction mechanism and persistence against shocks, the volatility asymmetry and the leverage effect it was estimated the GARCH, TARCH and EGARCH. The sum of the reaction coefficients (ARCH) with the volatility persistence coefficient (GARCH), resulted in values close to 1.0 to all the commodities, indicating that volatility shocks in prices will last for a long time. According to the TARCH results it is possible to see that the conditional variance it is not asymmetric to Aluminum and Copper. As it is possible to verify that τ it is statistically different from 0 to Nickel and Zinc, so, they have an asymmetric conditional variance. Positive shocks in Nickel and Zinc prices imply a lower volatility in comparison with negative shocks with same magnitude. Specifically to the EGARCH obtained results it possible to perceive that the Aluminum and Copper had a τ coefficient not statistically different from 0, so is does not exist asymmetry in volatility, corroborating the obtained results by the TARCH model. The Nickel and Zinc commodities presented a τ coefficient statistically different from 0 showing an asymmetric conditional variance. Accordingly, exists a different impact um by negative and positive shocks on volatility. Finally, it was not possible to verify the leverage effect in the analyzed commodities.

Palabras clave


volatility; prices; non-ferrous metals

Texto completo:

PDF (English)

Referencias


(1) TODOROVA, N., WORTHINGTON, A., & Souček, M. (2014). Realized volatility spillovers in the non-ferrous metal futures market. Resources Policy, 39, 21-31.

(2) NAKAMURA, T. (2015). Resource Recycling of Non-Ferrous Metals. In Topical Themes in Energy and Resources (pp. 229-243). Springer Japan.

(3) BOULAMANTI, A., & MOYA, J. A. (2016). Production costs of the non-ferrous metals in the EU and other countries: Copper and zinc. Resources Policy, 49, 112-118.

(4) ALOM, F., WARD, B., & HU, B. (2011). Cross country mean and volatility spillover effects of food prices: multivariate GARCH analysis. Economics Bulletin, 31(2), 1439-1450.

(5) BRUNETTI, C., & GILBERT, C. L. (1995). Metals price volatility, 1972–1995. Resources Policy, 21(4), 237-254.

(6) MCMILLAN, D. G., & SPEIGHT, A. E. (2001). Non-ferrous metals price volatility: a component analysis. Resources Policy, 27(3), 199-207.

(7) WATKINS, C., & MCALEER, M. (2006). Pricing of non-ferrous metals futures on the London Metal Exchange. Applied Financial Economics, 16(12), 853-880.

(8) DO, G. Q., MCALEER, M., & SRIBOONCHITTA, S. (2009). Effects of international gold market on stock exchange volatility: evidence from ASEAN emerging stock markets. Economics Bulletin, 29(2), 599-610.

(9) GONTIJO, T. S., FERNANDES, E. A., & SARAIVA, M. B. (2011). Análise da volatilidade do retorno da commodity dendê: 1980-2008. Revista de Economia e Sociologia Rural, 49(4), 857-874.

(10) IMF. International Monetary Fund. Data and Statistics. 2017.

(11) BLS. U.S. Bureau Labor Service. Databases and Tables. 2017.

(12) MORETTIN, P. A., & TOLOI, C. M. (2004). Análise de Séries Temporais. São Paulo, ABE.

(13) ENGLE, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.

(14) NEWEY, W.K.; WEST, K.D. A simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3):703–708, 1987.

(15) BREUSCH, T. S., & GODFREY, L. G. (1981). A review of recent work on testing for autocorrelation in dynamic linear models. Macroeconomic Analysis: Essays in Macroeconomics and Macroeconometrics (ed. DA Currie, R. Nobay and D. Peel). London.

(16) GREENE, W. N. (2002). Econometric Analysis. WH Greene.

(17) JARQUE, C. M., & BERA, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics letters, 6(3), 255-259.

(18) CAMPOS, K. C., PIACENTI, C. A., & SILVA JÚNIOR, A. G. D. (2007). Agroenergia: a questão da volatilidade de preços e o efeito alavancagem dos produtos agrícolas. Revista de Política Agrícola, 16(3), 34-48.


Enlaces refback

  • No hay ningún enlace refback.


Iberoamerican Journal of Project Management (IJoPM). ISSN 2346-9161(Online). www.ijopm.org. Correo: journal.ijopm@gmail.com.

Recomendamos utilizar el navegador Google Chrome. Recomendamos o uso do navegador Google Chrome. Recommend using the Google Chrome browser.