Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment

Choi, Kyongwook; Hammoudeh, Shawkat

doi:10.1016/j.enpol.2010.03.067

Cited by 300 publications

(142 citation statements)

References 34 publications

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“…Thus, oil (the EUR/USD exchange rate) has the highest (lowest) mean absolute return (3.7376%, 1.0836%). The particulary high volatility of oil returns can hinder the interpretaton of market signals and may restrain new investment (Choi and Hammoudeh, 2010). The mean absolute return for gold is relatively low (1.5652%).…”

Section: Datamentioning

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: Datamentioning

confidence: 99%

Dynamic spillovers between commodity and currency markets

Antonakakis

Kizys

2015

International Review of Financial Analysis

212

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“…By using a vector autoregressive (VAR) framework, they focus on the role of the nonoil trade balance in offsetting oil trade changes and on the effects of shocks (trade channel) on the value of gross foreign assets and liabilities (valuation channel). They show that (i) the source of the shocks matters insofar as oil-supply and oil-demand shocks have different effects on external accounts 3 , and (ii) trade and valuation channels exert a significant influence on 2 See, e.g., Choi and Hammoudeh (2010), Dwyer et al(2011), Vivian andWohar (2012), Creti et al (2013), and Silvennoinen and Thorp (2013).…”

Section: Introductionmentioning

confidence: 99%

Oil price shocks and global imbalances: Lessons from a model with trade and financial interdependencies

2015

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The aim of this paper is to investigate oil price shocks' effects and their associated transmission channels on global imbalances. To this end, we rely on a Global VAR approach that allows us to account for trade and financial interdependencies between countries. Considering a sample of 30 oil-exporting and importing economies over the 1980-2011 period, we find that the nature of the shock-demand-driven or supply-drivenmatters in understanding the effects of oil price shocks on global imbalances. In addition, we show that the main adjustment mechanism to oil shocks is based on the trade channel, the valuation channel being at play only on the short run.JEL Classification: C32, F32, Q43.

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“…Zarour (2006) also applied VAR as the methodology in discovering the relationship between oil prices and stock markets in the Persian Gulf Countries, and the results that study suggested that the reaction of stock markets in the Persian Gulf Countries increased as the price of oil rose. By using different methodologies, such as the GARCH model, several scholars found that an increase in the price of oil would lead to lower stock returns mainly in oil and oil-related industries (Choi & Hammoudeh, 2010;Hammoudeh, Yuan, Chiang, & Nandha, 2010). A number of scholars have examined the firm-specific responses to oil price fluctuations (Al-Mudhaf & Goodwin, 1993;Boyer & Filion, 2007;Sadorsky, 2001).…”

Section: Introductionmentioning

confidence: 99%