The crude oil market and the gold market: Evidence for cointegration, causality and price discovery

Zhang, Yue‐Jun; Wei, Yi‐Ming

doi:10.1016/j.resourpol.2010.05.003

Cited by 347 publications

(177 citation statements)

References 25 publications

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“…Using a long span of annual data , Baffes (2007) shows that the prices of precious metals, including gold, strongly respond to the price of oil. A similar result is produced by Zhang and Wei (2010), who, based on daily data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008), find that a rising oil price drives up the price of gold, but they do not find a reverse link.…”

Section: Introductionsupporting

confidence: 73%

“…Further, gold is quite resistant with respect to inflation while 13 increases in oil prices usually affect the aggregate price level and lead to an increase in inflation, which makes investment in gold more attractive. Finally, when major oil deliveries are paid during periods of higher oil prices, producers might use excess proceeds to buy gold whose price would increase due to higher demand (Zhang and Wei, 2010).…”

Section: Structural Changes and Long-term Equilibrium Links: Cointegrmentioning

confidence: 99%

“…Hence, the Efficient Market Hypothesis (EMH) is the basis that motivates an analysis of cointegration among the assets under research. Zhang and Wei (2010) identify a cointegration link between two assets on daily data. Narayan et al (2010) improve on the cointegration approach and analyze the long-run relationship between gold and oil futures prices over the period 1963-2008 at different levels of maturity in order to gauge differences in hedging behavior.…”

Section: Introductionmentioning

confidence: 99%

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Gold, oil, and stocks: Dynamic correlations

Baruník

Kočenda

Vácha

2016

International Review of Economics & Finance

120

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investment horizons between pairs of assets is a dominant feature during times of economic downturn and financial turbulence for all three pairs of the assets under research. Heterogeneity prevails in correlations between gold and stocks. After the 2008 crisis, correlations among all three assets increase and become homogenous: the timing differs for the three pairs but coincides with the structural breaks that are identified in specific correlation dynamics. A strong implication emerges: during the period under research, and from a different-investment-horizons perspective, all three assets could be used in a well-diversified portfolio only during relatively short periods. Terms of use: Documents inJEL-Code: C010, C130, C580, F370, G110, G150.

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Section: Introductionsupporting

confidence: 73%

Section: Structural Changes and Long-term Equilibrium Links: Cointegrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gold, oil, and stocks: Dynamic correlations

Baruník

Kočenda

Vácha

2016

International Review of Economics & Finance

120

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“…Shafiee and Topal (2010) argue that between 1968 and 2008 there have been two jumps in oil prices, the first one in 1979-1980 and the second one in 2007-2008 which are both followed by jumps in gold prices. Zhang and Wei (2010) elucidate a unidirectional Granger causality from oil market to gold price volatility and report a very high significant positive correlation between the two markets over their sample period 2000-2008. Furthermore it is essential to analyze the reactions of assets to positive and negative shocks. As put forth by Hood and Malik (2013), volatility in stock markets is asymmetric which means that negative shocks display more profound effects on conditional volatility than positive shocks.…”

Section: Literature Reviewmentioning

confidence: 92%

Volatility Modeling and Forecasting of Istanbul Gold Exchange (IGE)

Gencer¹,

Musoglu²

2014

International Journal of Financial Research

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In this study, we model the volatility dynamics of Istanbul Gold Exchange with several GARCH models, which incorporate asymmetry and long-range dependence in the conditional volatility. We use daily spot prices of the gold exchange from January 4, 2006 to November 20, 2013. In addition, forecasting performances of the models are evaluated based on three commonly used measures. We examine the out-of-sample predictions for 1, 5 and 20-days ahead as the portfolio managers usually focus on longer horizons (20-days) as well as relatively short horizons (1 and 5-days ahead). The forecasting results point out the superior performance of EGARCH and CGARCH models. Hence our results provide useful information for investors and portfolio managers to consider asymmetric reactions to the news and long-range dependence property of the gold market in Turkey.

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“…Specifically, gold price returns do not explain much of oil price returns while oil price returns account for 1.7% of gold price returns. On examining the long-term causal and lead-and-lag relationship between oil and gold markets, Zhang et al [28] report a significant co-integrating relationship between the prices of the two strategic commodities. They indicate that percentage changes of crude oil price return significantly and linearly Granger cause the percentage change of gold price return.…”

Section: Review Of Related Empirical Literaturementioning

confidence: 99%

Effects of Crude Oil and Gold Prices on US Stock Market: Evidence for USA from ARDL Bounds Testing

Rahman

Mustafa

2018

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This paper explores the effects of changes in crude oil and gold prices on US Stock market movement. Daily data are used from the first business day of January, 1986 to December 30, 2016. Efficient unit root tests (DF-GLS and Ng-Perron) are applied to examine the time series property of the variables in terms of stationarity or non-stationarity. ARDL Bounds Testing is applied for co-integration. Both DF-GLS and Ng-Perron tests confirm non-stationarity of each variable and depict behavior of all the variables in log-levels, included in this study. The ARDL-Bounds testing confirms co-integration among the variables. There is evidence of long-run convergence among all these variables with very tepid adjustment towards the equilibrium. Short-run negative effects of changes in gold and crude oil prices on US stock market returns are observed. The effect is statistically significant from gold price changes, but insignificant from crude oil price changes.

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The crude oil market and the gold market: Evidence for cointegration, causality and price discovery

Cited by 347 publications

References 25 publications

Gold, oil, and stocks: Dynamic correlations

Gold, oil, and stocks: Dynamic correlations

Volatility Modeling and Forecasting of Istanbul Gold Exchange (IGE)

Effects of Crude Oil and Gold Prices on US Stock Market: Evidence for USA from ARDL Bounds Testing

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