Volatility transmission between gold and oil futures under structural breaks

Ewing, Bradley T.; Malik, Farooq

doi:10.1016/j.iref.2012.06.008

Cited by 256 publications

(135 citation statements)

References 40 publications

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“…This high level of volatility persistence is consistent with early studies using somewhat high frequency data. (Ewing & Malik, 2013) Both oil and base metals volatilities are significantly affected and volatility in its own market in the previous period, which is consistent with univariate GARCH models. Furthermore, the volatility in either oil or base metals is directly affected by volatility from the other market, which is somewhat strong result against the Ewing & Malik (2013).…”

Section: Resultssupporting

confidence: 77%

“…Ewing & Malik (2013) found that the correlation between both the returns series of oil and gold was 0.20. It is found that the correlation between both the returns series of oil and base metals futures over sample period is somewhat higher of range between 0.25 of tin and 0.35 of copper.…”

Section: Datamentioning

confidence: 98%

“…Both oil and base metals futures markets have been attracted commodity funds such as CTAs (Commodity Trading Advisors) and even some hedge funds. Ewing & Malik (2013) examined the linkage that might exist between the volatilities in oil and gold in a way of the volatility dynamics between two major commodities to support the idea of cross-market hedging. They found strong evidence of significant transmission of volatilities between in these asset prices.…”

Section: Introductionmentioning

confidence: 99%

“…They found strong evidence of significant transmission of volatilities between in these asset prices. Given that Ewing & Malik (2013) result, the question arises of whether there has been volatility transmission from oil prices to base metals. In particular, oil markets often trigger a cost-push in inflation front.…”

Section: Introductionmentioning

confidence: 99%

“…Oil prices are also complements and substitutes in consumption, and inputs in production of others (Hammoudeh & Yuan, 2008). While Ewing & Malik (2013) focused on gold of inflation hedge characteristics, this paper concentrates on base metals of industry activity itself, paying attention to which volatility in base metals prices impacted by external shocks from oil prices. This paper studies the volatility dynamics of oil and LME 2 futures to identify the statistical linkage between the volatilities in base metals prices and oil prices using daily data from January 4, 2000 to December 30, 2016.…”

Section: Introductionmentioning

confidence: 99%

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Volatility Transmission between Oil and LME Futures

Park¹

2018

AEF

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This paper investigates the volatility transmission between oil and base metals to assess the possibility of hedge strategy across commodity markets. In order to identify the volatility linkage of oil to the base metals, the bivariate GARCH model is applied using daily returns data period over 2000-2016. It is found that evidence of volatility transmission between oil and base metals is somewhat strong with a 1% significant level. This result suggests the investment idea of commodity hedging strategy of cross-market is important.

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Section: Resultssupporting

confidence: 77%

Section: Datamentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Volatility Transmission between Oil and LME Futures

Park¹

2018

AEF

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Forecasting ethanol price volatility under structural breaks

Bouri

Dutta

Saeed

2020

Biofuels Bioprod Bioref

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The use of ethanol as a vehicle fuel has reduced greenhouse gas emissions significantly. The introduction of ethanol has also led to a decrease in crude oil prices. Considering the economic and environmental significance of the biofuel markets, a strand of literature investigates the price and volatility dynamics of US ethanol prices. In this paper, in contrast to previous studies, we investigate whether information on structural breaks plays an important role in predicting US ethanol market volatility. Our findings reveal that generalized autoregressive conditional heteroskedasticity (GARCH) models incorporating these breaks improve the prediction of US ethanol market volatility. Furthermore, the persistence of volatility tends to decline when structural breaks are included in the GARCH models. We further note that the influence of good and bad news is properly assessed under such breaks. Our results suggest that ignoring such breaks could mislead the risk assessment procedure for the US biofuel industry. © 2020 Society of Industrial Chemistry and John Wiley & Sons Ltd

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Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint

Geng,

Hao,

Wang

2023

Journal of Forecasting

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Parameter instability and model uncertainty are two key problems affecting forecasting outcomes. In this paper, we propose a time‐dependent weighted least squares with ridge constraint (TWLS‐Ridge) to solve the above two problems in the forecasting procedure. The new TWLS‐Ridge approach is applied to the heterogenous autoregressive realized volatility model and its various extensions. The empirical results suggest that TWLS‐Ridge produces more accurate volatility forecasts than several alternative models dealing with parameter instability and model uncertainty. The superior performance of TWLS‐Ridge is robust under different forecast horizons, evaluation periods, and loss functions. An investor with mean–variance preference can improve utility using TWLS‐Ridge forecasts of oil volatility instead of ordinary least squares model forecasts.

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Volatility transmission between gold and oil futures under structural breaks

Cited by 256 publications

References 40 publications

Volatility Transmission between Oil and LME Futures

Volatility Transmission between Oil and LME Futures

Forecasting ethanol price volatility under structural breaks

Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint

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