How has volatility in metals markets changed?

Watkins, Clinton; McAleer, Michael

doi:10.1016/j.matcom.2008.01.015

Cited by 38 publications

(16 citation statements)

References 17 publications

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“…In the EGARCH model, they found that only copper has asymmetric leverage effect, and in the CGARCH model the transitory component of volatility converges to equilibrium faster for copper than for gold and silver. Using a rolling AR(1)-GARCH, Watkins and McAleer (2008) showed that the conditional volatility for two nonferrous metals, namely aluminum and copper, is time-varying over a long horizon.…”

Section: Review Of the Literaturementioning

confidence: 99%

Precious metals–exchange rate volatility transmissions and hedging strategies

Hammoudeh

Yuan

McAleer

et al. 2010

International Review of Economics & Finance

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172

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This study examines the conditional volatility and correlation dependency and interdependency for the four major precious metals (that is, gold, silver, platinum and palladium), while accounting for geopolitics within a multivariate system. The implications of the estimated results for portfolio designs and hedging strategies are also analyzed. The results for the four metals system show significant short-run and long-run dependencies and interdependencies to news and past volatility. These results have become more pervasive when the exchange rate and FFR are included. Monetary policy also has a differential impact on the precious metals and the exchange rate volatilities.Finally, the applications of the results show the optimal weights in a two-asset portfolio and the hedging ratios for long positions.

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Section: Review Of the Literaturementioning

confidence: 99%

Precious metals–exchange rate volatility transmissions and hedging strategies

Hammoudeh

Yuan

McAleer

et al. 2010

International Review of Economics & Finance

Self Cite

172

View full text Add to dashboard Cite

show abstract

“…Moreover, while volatility may be persistent, there could be frequent and relatively unpredictable regime shifts which the standard model cannot account for (Glosten et al, 1993). Using a rolling AR(1)-GARCH, Watkins and McAleer (2008) show that the conditional volatility for two non-ferrous metals, namely aluminum and copper, is time-varying over a long horizon. Additionally, research has shown that the long-run forecast performance of the standard GARCH model is less satisfactory as the contrast between the insample and out-of-sample evaluations is widely observed (Sadorsky, 2006;Balaban, 2004;West et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment

2010

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“…The empirical findings suggest that the proposed model can be effectively applied to predict the volatility of many metal assets. Watkins and McAleer () apply a rolling AR‐GARCH model to predict the return volatility of aluminium and copper. Li and Li () combine the model averaging techniques and GARCH‐type models to predict the volatility of copper futures and find that the model averaging techniques can reduce the uncertainty of GARCH‐type models for volatility in the copper futures market.…”

Section: Literature Reviewmentioning

confidence: 99%

Structural breaks and volatility forecasting in the copper futures market

Gong

Lin

2017

Journal of Futures Markets

142

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This paper examines whether structural breaks contain incremental information for forecasting the volatility of copper futures. Considering structural breaks in volatility, we develop four heterogeneous autoregressive (HAR) models based on classical or latest HAR‐type models. Subsequently, we apply these models to forecast volatility in the copper futures market. The empirical results reveal that our models exhibit better in‐sample and out‐of‐sample performances than classical or latest HAR‐type models. This suggests that structural breaks contain incremental prediction information for the volatility of copper futures. More importantly, we argue that considering structural breaks can improve the performances of most of existing HAR‐type models. Highlights There are many structural break points in return volatility of the copper futures. We propose 12 new heterogeneous autoregressive models. Our models outperform the existing heterogeneous autoregressive models. Structural breaks contain additional ex ante information for volatility forecasting. The ex ante information is obvious in forecasting mid‐ and long‐term volatilities.

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How has volatility in metals markets changed?

Cited by 38 publications

References 17 publications

Precious metals–exchange rate volatility transmissions and hedging strategies

Precious metals–exchange rate volatility transmissions and hedging strategies

Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment

Structural breaks and volatility forecasting in the copper futures market

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