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Long memory and structural breaks in modeling the return and volatility dynamics of precious metals

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  • Arouri, Mohamed El Hedi
  • Hammoudeh, Shawkat
  • Lahiani, Amine
  • Nguyen, Duc Khuong

Abstract

We investigate the potential of structural changes and long memory (LM) properties in returns and volatility of the four major precious metal commodities traded on the COMEX markets (gold, silver, platinum and palladium). Broadly speaking, a random variable is said to exhibit long memory behavior if its autocorrelation function is not integrable, while structural changes can induce sudden and significant shifts in the time-series behavior of that variable. The results from implementing several parametric and semiparametric methods indicate strong evidence of long range dependence in the daily conditional return and volatility processes for the precious metals. Moreover, for most of the precious metals considered, this dual long memory is found to be adequately captured by an ARFIMA–FIGARCH model, which also provides better out-of-sample forecast accuracy than several popular volatility models. Finally, evidence shows that conditional volatility of precious metals is better explained by long memory than by structural breaks.

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Bibliographic Info

Article provided by Elsevier in its journal The Quarterly Review of Economics and Finance.

Volume (Year): 52 (2012)
Issue (Month): 2 ()
Pages: 207-218

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Handle: RePEc:eee:quaeco:v:52:y:2012:i:2:p:207-218

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Web page: http://www.elsevier.com/locate/inca/620167

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Keywords: Precious metal prices; Long memory; Structural breaks; ARFIMA–FIGARCH;

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Citations

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Cited by:
  1. Mohamed El Hedi Arouri & Amine Lahiani & Duc Khuong Nguyen, 2013. "World gold prices and stock returns in China: insights for hedging and diversification strategies," Working Papers hal-00798038, HAL.
  2. repec:ipg:wpaper:9 is not listed on IDEAS
  3. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "How do OPEC news and structural breaks impact returns and volatility in crude oil markets? Further evidence from a long memory process," Energy Economics, Elsevier, vol. 42(C), pages 343-354.
  4. Philippe Charlot & Vêlayoudom Marimoutou, 2014. "On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree," Working Papers hal-00980125, HAL.
  5. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
  6. Demiralay, Sercan & Ulusoy, Veysel, 2014. "Value-at-risk Predictions of Precious Metals with Long Memory Volatility Models," MPRA Paper 53229, University Library of Munich, Germany.
  7. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: The importance of scheduled and unscheduled news announcements," International Review of Economics & Finance, Elsevier, vol. 30(C), pages 101-119.
  8. Sinha, Pankaj & Mathur, Kritika, 2013. "A study on the Price Behavior of Base Metals traded in India," MPRA Paper 47028, University Library of Munich, Germany.
  9. repec:ipg:wpaper:201409 is not listed on IDEAS
  10. Walid Chkili & Shawkat Hammoudeh & Duc Khuong Nguyen, 2013. "Long memory and asymmetry in the volatility of commodity markets and Basel Accord: choosing between models," Working Papers 2013-009, Department of Research, Ipag Business School.
  11. Brooks, Chris & Prokopczuk, Marcel & Wu, Yingying, 2013. "Commodity futures prices: More evidence on forecast power, risk premia and the theory of storage," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(1), pages 73-85.

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