Volatility persistence in metal returns: A FIGARCH approach

Cochran, Steven J.; Mansur, Iqbal; Odusami, Babatunde

doi:10.1016/j.jeconbus.2012.03.001

Cited by 71 publications

(47 citation statements)

References 46 publications

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“…In absolute terms, the coefficient pertaining to oil is the greatest while that pertaining to natural gas is the smallest. The finding of a negative relationship between commodity returns and exchange rate movements is consistent with those in the literature (see, e.g., Tully and Lucey (2007) and Cochran et al (2012)). …”

Section: Single Regime Garch Resultssupporting

confidence: 91%

“…The negative coefficients are consistent with the findings ofBhar and Malliaris (2011), Sari et al (2011), and Cochran et al (2012. It appears that an increase in uncertainty in the equity market results in a higher degree of uncertainty concerning the global economic outlook which, in turn, decreases the demand for energy.…”

supporting

confidence: 86%

“…Sadorsky (2006), Tully and Lucey (2007), Narayan and Narayan (2007), Yuan (2008), andCochran et al (2012), among others, have employed the basic GARCH model and several of its variations in the modeling of commodity (primarily oil) returns and return volatility in a single regime framework.…”

Section: Why Use Threshold Models?mentioning

confidence: 99%

“…InCochran et al (2012), the variables used are the market return, the exchange rate, the term spread, and the level of VIX. In this study, the change in the level of VIX, instead of the level of VIX, is used.…”

mentioning

confidence: 99%

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Equity market implied volatility and energy prices: A double threshold GARCH approach

2015

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Section: Single Regime Garch Resultssupporting

confidence: 91%

supporting

confidence: 86%

Section: Why Use Threshold Models?mentioning

confidence: 99%

mentioning

confidence: 99%

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Equity market implied volatility and energy prices: A double threshold GARCH approach

2015

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“…Morales and Andreosso-O'Callaghan (2011) find that in terms of volatility spillovers, an asymmetric effect is observed; gold tends to dominate the markets and the evidence favouring the case of other precious metals influencing the gold market is weak. Cochran et al (2012) show that events taking place during the post-September 2008 period increased the volatility in gold, platinum, and silver returns. Sensoy (2013) claims that the turbulent year of 2008 had no significant effect on the volatility levels of gold and silver, although caused an upward shift in the volatility levels of palladium and platinum.…”

Section: Introductionmentioning

confidence: 94%

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

Wanat

Papież

Śmiech

2015

KREM

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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.

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A separate reduced‐form volatility forecasting model for nonferrous metal market: Evidence from copper and aluminum

Zhang

Lee

2018

Journal of Forecasting

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This article extends the HAR‐CJN model proposed by Andersen, Bollerslev, and Huang (Journal of Econometrics, 2011, 160, 176–189) and explores the role of overnight information and leverage effects in improving volatility forecasting. To explore the interaction between different components of daily volatility, this paper attempts to separately model the dynamics of continuous variation, the discontinuous jump, and the overnight return variance by including leverage effects. The findings show that lagged continuous and discontinuous jump variations generate significant impacts on future continuous segments, discontinuous jump segments, and the overnight return variance. Furthermore, in addition to the usual leverage effects, additional leverage effects with respect to overnight returns are found to play a significant role in volatility forecasting. Finally, out‐of‐sample forecasts are investigated; the results show that the new HAR‐CJN model can describe and predict daily volatility more accurately than other HAR models.

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Volatility persistence in metal returns: A FIGARCH approach

Cited by 71 publications

References 46 publications

Equity market implied volatility and energy prices: A double threshold GARCH approach

Equity market implied volatility and energy prices: A double threshold GARCH approach

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

A separate reduced‐form volatility forecasting model for nonferrous metal market: Evidence from copper and aluminum

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