On the links between stock and commodity markets' volatility

Cretì, Anna; Joëts, Marc; Mignon, Valérie

doi:10.1016/j.eneco.2013.01.005

Cited by 462 publications

(220 citation statements)

References 22 publications

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“…Our results are also consonant with Ewing and Malik (2013), who ascribe the increased incidence in volatility transmission between gold and oil futures to cross-market hedging. Moreover, volatility of commodity returns itself can play an important role in designing optimal hedging strategies (Creti et al, 2013). Further, volatility of asset returns depends on the rate of information flow; as a result, information from one market can be incorporated into the volatility generating process of another market Ross (1989).…”

Section: Summary and Concluding Remarksmentioning

confidence: 99%

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: Summary and Concluding Remarksmentioning

confidence: 99%

Dynamic spillovers between commodity and currency markets

Antonakakis

Kizys

2015

International Review of Financial Analysis

212

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“…Arouri [2011] analyses oil prices and stock returns in Europe. Similarly, Śmiech and Papież [2013] analyse fossil fuel prices, exchange rate, stock market on the European market, whereas Creti, Joets and Mignon [2013] examine the links between commodities returns and stock returns on the U.S. data. Souček [2013] compares market activity for three of the major futures contracts in the U.S.: the stock index S&P 500, light sweet crude oil, and gold.…”

Section: Causality In Distribution Between European Stock Markets Andmentioning

confidence: 99%

“…Park and Ratti [2008] use a VAR model. Many authors use the family of GARCH models, specifically bivariate GARCH [Cifarelli, Paladino 2010;Arouri, Lahiani, Nguyen 2011;Arouri 2011;Papież, Śmiech 2012], generalised VAR-GARCH [Arouri, Jouini, Nguyen 2012;Mensi et al 2013] and multivariate GARCH (Creti et al (2013) use dynamic conditional correlation (DCC) GARCH).…”

Section: Causality In Distribution Between European Stock Markets Andmentioning

confidence: 99%

Causality in distribution between European stock markets and commodity prices: using independence test based on the empirical copula

Wanat¹,

Papież²,

Śmiech³

2015

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

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“…Other such studies that examine the interdependence between equity markets and oil markets include Hammoudeh and Aleisa (2004), El-Sharif et al (2005), Filis et al (2011). Several related studies investigate a similar question from the perspective of volatility transmission and spillover (Malik and Hammoudeh, 2007, Arouri, 2011, Creti et al, 2013.…”

Section: Introductionmentioning

confidence: 99%

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

Cummins

Dowling

Kearney

2016

International Review of Financial Analysis

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We formally compare fundamental factor and latent factor approaches to oil price modelling. Fundamental modelling has a long history in seeking to understand oil price movements, while latent factor modelling has a more recent and limited history, but has gained popularity in other financial markets. The two approaches, though competing, have not formally been compared as to effectiveness. For a range of short-medium-and long-dated WTI oil futures we test a recently proposed five-factor fundamental model and a Principal Component Analysis latent factor model. Our findings demonstrate that there is no discernible difference between the two techniques in a dynamic setting. We conclude that this infers some advantages in adopting the latent factor approach due to the difficulty in determining a well specified fundamental model.

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On the links between stock and commodity markets' volatility

Abstract: On the links between stock and commodity markets' volatility

Cited by 462 publications

References 22 publications

Dynamic spillovers between commodity and currency markets

Dynamic spillovers between commodity and currency markets

Causality in distribution between European stock markets and commodity prices: using independence test based on the empirical copula

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

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