Oil Spills On Other Commodities

Baffes, John

doi:10.1596/1813-9450-4333

Cited by 62 publications

(54 citation statements)

References 24 publications

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“…Gold is also considered as a safe haven in periods of heightened turbulence (Ciner et al, 2013). In agreement with this finding, Baffes (2007) documents lower pass-through from crude oil to prices of precious metals' prices than to prices of other commodities. Further, large swings in precious metals' prices are a source of changes in terms of trade (Aizenman et al, 2012;Pierdzioch et al, 2013), which by turn can trigger changes in the exchange rate (De Gregorio and Wolf, 1994).…”

Section: Return Spilloversmentioning

confidence: 61%

Dynamic spillovers between commodity and currency markets

Antonakakis

Kizys

2015

International Review of Financial Analysis

212

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In this study, we examine the dynamic link between returns and volatility of commodities and currency markets. Based on weekly data over the period from January 6, 1987 to July 22, 2014, we find the following empirical regularities. First, our results suggest that the information contents of gold, silver, platinum, and the CHF/USD and GBP/USD exchange rates can help improve forecast accuracy of returns and volatilities of palladium, crude oil and the EUR/CHF and GBP/USD exchange rates. Second, gold (CHF/USD) is the dominant commodity (currency) transmitter of return and volatility spillovers to the remaining assets in our model. Third, the analysis of dynamic spillovers shows time-and event-specific patterns. For instance, the dynamic spillover effects originating in gold and silver (platinum) returns and volatility intensified (degraded) in the period marked by the global financial crisis. After the global financial crisis, the net transmitting role of gold and silver (platinum) returns shocks weakened (strengthened), while the net transmitting role of gold, silver and platinum volatility shocks remained relatively high. Overall, our findings reveal that, while the static analysis clearly classifies the aforementioned variables into net transmitters and net receivers, the dynamic analysis denotes episodes wherein the role of transmitters and receivers of return (volatility) spillovers can be interrupted or even reversed. Hence, even if certain commonalities prevail in each identified category of commodities, such commonalities are time-and event-dependent.

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Section: Return Spilloversmentioning

confidence: 61%

Dynamic spillovers between commodity and currency markets

Antonakakis

Kizys

2015

International Review of Financial Analysis

212

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“…An inflation channel can well explain the theoretical underpinnings between the two assets: rising oil prices usually influence the aggregate price level (Hunt, 2006) and generate inflationary pressures that prompt hedging against inflation in the form of investments in gold (Narayan et al, 2010). 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: Introductionmentioning

confidence: 99%

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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“…Commodity markets came into prominence after the deep boombust cycle in commodity and oil prices resulting from the great global recession that began at the end of 2007. Several studies have assessed the interrelationship between the oil and commodity prices and some authors have concluded that the relationship is strong while others stated that it is weak (Baffes 2007;Baffes and Haniotis 2010;Pindyck and Rotemberg 1990;Plourde and Watkins 1998). In line with the previous studies, this paper looks at the strength of the relationship.…”

mentioning

confidence: 63%

The linkage between oil and agricultural commodity prices in the light of the perceived global risk

Gözgör¹,

Kablamaci²

2014

Agric. Econ. - Czech

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Th e paper examines a systematic interrelationship between the world oil and agricultural commodity prices, taking the role of the USD and the perceived global market risks into consideration for the period from January 1990 to June 2013. Th e authors initially determine the signifi cant cross-sectional dependence in a large balanced panel framework for 27 commodity prices, and then apply the second generation panel unit root (PUR) tests. Findings from the PUR tests clearly suggest that there is a strong unit root in agricultural commodity prices. In addition, the empirical fi ndings from the fi xed eff ects panel data, panel co-integration analysis, the Panel-Wald Causality tests, and the common correlated eff ects mean group estimations strongly show that the world oil price and the weak USD have positive impacts on almost all agricultural commodity prices. Th ere are also retained the adjuvant eff ects of the escalatory perceived global market risk upon most agricultural commodity prices.Key words: oil prices, panel data estimations, the VIX 1 The causality of prices affecting economic conditions -one must keep in mind that the cause and effect is never in one direction and mainly depends on a specific context.

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Oil Spills On Other Commodities

Cited by 62 publications

References 24 publications

Dynamic spillovers between commodity and currency markets

Dynamic spillovers between commodity and currency markets

Gold, oil, and stocks: Dynamic correlations

The linkage between oil and agricultural commodity prices in the light of the perceived global risk

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