Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2015 | 4(940) | 19-33

Article title

The Conditional Dependence Structure between Precious Metals: A Copula-GARCH Approach

Title variants

PL
Modelowanie warunkowej zależności między metalami szlachetnymi z wykorzystaniem modeli copula-GARCH

Languages of publication

EN

Abstracts

EN
The aim of the paper is to analyse the conditional dependence structure between precious metal returns using a copula-DCC-GARCH approach. Conditional correlation matrices are used to identify the states of the precious metals market by assuming that a given state of the market corresponds to a typical pattern of the conditional dependence structure. Cluster analysis allows for pointing at transition points between the market states, that is the points of drastic change in the conditional dependence structure. The application of the methodology described above to the period between 1997 and 2013 indicates three market states of four major precious metals (gold, silver, platinum and palladium). The results obtained reveal a sudden increase in dependencies between precious metals at the turn of April and May 2004.
PL
W pracy analizowano warunkową strukturę zależności na rynku metali szlachetnych z wykorzystaniem modeli copula-DCC-GARCH. Na podstawie warunkowych macierzy korelacji rozpoznano stany rynku metali szlachetnych. W tym celu przyjęto, że określonemu stanowi rynku odpowiada typowy wzór warunkowej struktury zależności. Momenty przejścia pomiędzy poszczególnymi stanami odpowiadające nagłym (drastycznym) zmianom w warunkowej strukturze zależności zidentyfikowano, wykorzystując metody grupowania. Zastosowanie opisanej metodologii pozwoliło w okresie od 1997 r. do 2013 r. wyodrębnić trzy stany rynku czterech metali szlachetnych (złoto, srebro, platyna, pallad). Badania wskazały także na znaczny wzrost zależności między rozważanymi metalami na przełomie kwietnia i maja 2004 r.

Contributors

  • Uniwersytet Ekonomiczny w Krakowie, Katedra Statystyki, ul. Rakowicka 27, 31-510 Kraków, Poland
  • Uniwersytet Ekonomiczny w Krakowie, Katedra Statystyki, ul. Rakowicka 27, 31-510 Kraków, Poland
  • Uniwersytet Ekonomiczny w Krakowie, Katedra Statystyki, ul. Rakowicka 27, 31-510 Kraków, Poland

References

  • Aloui R., Ben Aïssa M. S., Nguyen D. K. (2013), Conditional Dependence Structure between Oil Prices and Exchange Rates: A Copula-GARCH Approach, “Journal of International Money and Finance”, vol. 32: 719–738.
  • Ciner C. (2001), On the Long-run Relationship between Gold and Silver Prices: A Note, “Global Finance Journal”, vol. 12: 299–303, http://dx.doi.org/10.1016/S1044-0283(01)00034-5.
  • Cochran S. J., Mansur I., Odusami B. (2012), Volatility Persistence in Metal Returns: A FIGARCH Approach, “Journal of Economics and Business”, vol. 64: 287–305.
  • Doman R. (2011), Zastosowanie kopuli w modelowaniu dynamiki zależności na rynkach finansowych, Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, Poznań.
  • Engle R. (2002), Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models, “Journal of Business & Economic Statistics”, vol. 20: 339–350.
  • Hammoudeh S., Yuan Y. (2008), Metal Volatility in Presence of Oil and Interest Rate Shocks, “Energy Economics”, vol. 30: 606–620, http://dx.doi.org/10.1016/j.eneco. 2007.09.004.
  • Hammoudeh S., Yuan Y., McAleer M., Thompson M.A. (2010), Precious Metals-exchange Rate Volatility Transmissions and Hedging Strategies, “International Review of Economics & Finance”, vol. 19: 633–647.
  • Joe H. (1997), Multivariate Models and Dependence Concepts, Chapman-Hall, London.
  • Lee T.-H., Long X. (2009), Copula-based Multivariate GARCH Model with Uncorrelated Dependent Errors, “Journal of Econometrics”, vol. 150: 207–218, http://dx.doi.org/10.1016/j.jeconom.2008.12.008.
  • Li M., Yang L. (2013), Modeling the Volatility of Futures Return in Rubber and Oil – A Copula-based GARCH Model Approach, “Economic Modelling”, vol. 35: 576–581.
  • Lucey B. M. and Tully E. (2006), The Evolving Relationship between Gold and Silver 1978–2002: Evidence from a Dynamic Cointegration Analysis: A Note, “Applied Financial Economics Letters”, vol. 2: 47–53.
  • Morales L., Andreosso-O’Callaghan B. (2011), Comparative Analysis on the Effects of the Asian and Global Financial Crises on Precious Metal Markets, “Research in International Business and Finance”, vol. 25: 203–227.
  • Münnix M. C., Shimada T., Schäfer R., Leyvraz F., Seligman T. H., Guhr T., Stanley H. E. (2012), Identifying States of a Financial Market, “Scientific Reports”, vol. 2, http://dx.doi.org/10.1038/srep00644.
  • Nelsen R. B. (1999), An Introduction to Copulas, Springer-Verlag, New York.
  • Papież M., Śmiech S. (2012), Causality in Mean and Variance Between Returns of Crude Oil and Metal Prices, Agricultural Prices and Financial Market Prices (in:) J. Ramík, D. Stavárek (eds), Proceedings of 30th International Conference Mathematical Methods in Economics, Silesian University, School of Business Administration, Karviná: 675–680.
  • Patton A. J. (2006), Modelling Asymmetric Exchange Rate, “International Economic Review”, vol. 47: 527–556.
  • Patton A. J. (2012), A Review of Copula Models for Economic Time Series, “Journal of Multivariate Analysis”, vol. 110: 4–18, http://dx.doi.org/10.1016/j.jmva.2012.02.021.
  • Philippas D., Siriopoulos C. (2013), Putting the “C” into Crisis: Contagion, Correlations and Copulas on EMU Bond Markets, “Journal of International Financial Markets, Institutions and Money”, vol. 27: 161–176.
  • Reboredo J. C. (2013a), Is Gold a Hedge or Safe Haven Against Oil Price Movements? “Resources Policy”, vol. 38: 130–137.
  • Reboredo J. C. (2013b), Is Gold a Safe Haven or a Hedge for the US Dollar? Implications for Risk Management, “Journal of Banking and Finance”, vol. 37: 2665–2676.
  • Sari R., Hammoudeh S., Ewing B. T. (2007), Dynamic Relationships between Oil and Metal Commodity Futures Prices, “Geopolitics of Energy”, vol. 29: 2–13.
  • Sari R., Hammoudeh S., Soytas U. (2010), Dynamics of Oil Price, Precious Metal Prices, and Exchange Rate, “Energy Economics”, vol. 32: 351–362, http://dx.doi.org/10.1016/j.eneco.2009.08.010.
  • Sensoy A. (2013), Dynamic Relationship between Precious Metals, “Resources Policy”, vol. 38: 504–511, http://dx.doi.org/10.1016/j.resourpol.2013.08.004.
  • Serban M., Brockwell A., Lehoczky J., Srivastava S. (2007), Modelling the Dynamic Dependence Structure in Multivariate Financial Time Series, “Journal of Time Series Analysis”, vol. 28: 763–782, http://dx.doi.org/10.1111/j.1467-9892.2007.00543.x.
  • Silvennoinen A., Thorp S. (2013), Financialization, Crisis and Commodity Correlation Dynamics, “Journal of International Financial Markets, Institutions and Money”, vol. 24: 42–65.
  • Śmiech S., Papież M. (2012), A Dynamic Analysis of Causality between Prices on the Metals Market (in:) M. Reiff (ed.),Proceedings of the International Conference Quantitative Methods in Economics (Multiple Criteria Decision Making XVI), Bratislava: 221–225.
  • Wanat S. (2012), Modele zależności w agregacji ryzyka ubezpieczyciela, Wydawnictwo Uniwersytetu Ekonomicznego w Krakowie, Kraków.
  • Wu C. C., Chung H., Chang Y. H. (2012), The Economic Value of Co-movement between Oil Price and Exchange Rate using Copula-based GARCH Models, “Energy Economics”, vol. 34: 270–282, http://dx.doi.org/10.1016/j.eneco.2011.07.007.
  • Zolotko M., Okhrin O. (2014), Modelling the General Dependence between Commodity Forward Curves, “Energy Economics”, vol. 43: 284–296, http://dx.doi.org/10.1016/j.eneco.2014.02.019.

Document Type

Publication order reference

YADDA identifier

bwmeta1.element.desklight-470b08d2-b6e9-4ef6-9df5-7278857fb7fb
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.