Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets

Kang, Sang Hoon; McIver, Ron; Yoon, Seong‐Min

doi:10.1016/j.eneco.2016.12.011

Cited by 467 publications

(196 citation statements)

References 73 publications

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“…We find the net total connectedness of the lead futures is 34.6%, and zinc futures have a connectedness of 30.2%, indicating that these metal futures are net transmitters of shocks to other futures. The results imply that metal futures market is in a significant and leading position in commodity futures markets, which is consistent to the findings in the literature (Kang et al, ; Labys et al, ). Labys et al () show that metal market is an information origin and pricing standard for other commodity markets.…”

Section: Resultssupporting

confidence: 92%

“…Labys et al () show that metal market is an information origin and pricing standard for other commodity markets. Kang et al () find that both gold and silver are information transmitters to other commodity futures markets, whereas the remaining four commodity futures are receivers of spillovers during some periods of financial stress. In addition, lead and zinc play an important role in metal markets.…”

Section: Resultsmentioning

confidence: 99%

“…The commodities futures market plays an important role in the global economy. Due to low correlations with traditional financial assets and positive comovements of commodity prices with inflation, commodity futures, as a profitable hedging and speculation asset, can help investors optimize asset allocation and diversify portfolio risks (Chong & Miffre, ; Kang, McIver, & Yoon, ; Silvennoinen & Thorp, ). There are three main types of commodity futures—metal futures, energy futures, and agriculture futures.…”

Section: Introductionmentioning

confidence: 99%

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Estimating the connectedness of commodity futures using a network approach

Xiao

Fang

et al. 2019

Journal of Futures Markets

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Using a network approach of variance decompositions, we measure the connectedness of 18 commodity futures and characterize both static and dynamic connectedness. Our results show that metal futures are net transmitters of shocks to other futures, and agricultural futures are vulnerable to shocks from the others. Furthermore, almost two‐thirds of the volatility uncertainty for commodity futures are due to the connectedness of shocks across the futures market. Dynamically, we find connectedness always increases in times of turmoil. An analysis of connectedness networks suggests that investors could be forewarned that the connectedness of various classes of futures could threaten their portfolios.

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Section: Resultssupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating the connectedness of commodity futures using a network approach

Xiao

Fang

et al. 2019

Journal of Futures Markets

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“…Finally, yet importantly, these indexes are constructed based on futures rather than spot prices. 5 As far as we know, Kang et al (2017) and Liu et al (2019) are the only paper that addresses the dynamic spillover between commodity markets, resorting to futures contract prices. 4 http://www.rogersrawmaterials.com/documents/RICIHndbk_01.31.19.pdf 5 Investigating the co-movement and extreme dependencies between futures prices have multiple advantages, as it allows for the mitigation of stale quote and non-synchronous problems existing on the spot markets, while the noise in the futures prices is constant in average (for a detailed discussion, please refer to Albulescu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Copula-Based Local Dependence Between Energy, Agriculture and Metal Commodity Markets

Albulescu

Tiwari

2020

SSRN Journal

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This paper studies the extreme dependencies between energy, agriculture and metal commodity markets, with a focus on local co-movements, allowing the identification of asymmetries and changing trend in the degree of co-movements. More precisely, starting from a non-parametric mixture copula, we use a novel copula-based local Kendall's tau approach to measure nonlinear local dependence in regions. In all pairs of commodity indexes, we find increased co-movements in extreme situations, a stronger dependence between energy and other commodity markets at lower tails, and a 'V-type' local dependence for the energy-metal pairs. The three-dimensional Kendall's tau plot for upper tails in quantiles shows asymmetric comovements in the energy-metal pairs, which tend to become negative at peak returns. Therefore, we show that the energy market can offer diversification solutions for risk management in the case of extreme bull market events.

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“…To overcome the drawback of GARCH-class models, Engle, Ghysels, and Sohn, (2013) input realized volatility (RV) and the economic fundamentals into the GARCH model, forming the GARCH-MIDAS model, which prove macroeconomic fundamentals play a significant role in forecasting. In recent years, increasing numbers of researchers have used this model or modified it to analyze and forecast asset price volatility (Deng, Girardin, & Joyeux, 2018;Kang, McIver, & Yoon, 2017;Mo, Gupta, Li, & Singh, 2018;Pan et al, 2017;Wei, Liu, Lai, & Hu, 2017). Wei et al, (2017) use the GARCH-MIDAS model and dynamic model averaging combination method to forecast oil price volatility.…”

Section: Introductionmentioning

confidence: 99%

Oil financialization and volatility forecast: Evidence from multidimensional predictors

Pan

2019

Journal of Forecasting

129

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Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk.

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Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets

Cited by 467 publications

References 73 publications

Estimating the connectedness of commodity futures using a network approach

Estimating the connectedness of commodity futures using a network approach

Copula-Based Local Dependence Between Energy, Agriculture and Metal Commodity Markets

Oil financialization and volatility forecast: Evidence from multidimensional predictors

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