Volatility spillovers and cross-hedging between gold, oil and equities: Evidence from the Gulf Cooperation Council countries

Maghyereh, Aktham; Awartani, Basel; Tziogkidis, Panagiotis

doi:10.1016/j.eneco.2017.10.025

Cited by 126 publications

(74 citation statements)

References 66 publications

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“…The lowest DCC coefficient is recorded for Qatar below −0.3. This finding of negative correlations between oil shocks and stock returns is consistent with ex-ante expectations, and the general conclusions from the literature (see, for instance, Guesmi and Fattoum 2014;Delcoure and Singh 2018;Hammoudeh et al 2013;Maghyereh et al 2017). Figure 1B illustrates the conditional correlations between the gold market and the GCC stock markets.…”

Section: J Risk Financial Manag 2020 13 X For Peer Review 7 Of 17supporting

confidence: 88%

“…1 See, inter alia, Guesmi and Fattoum (2014) and Maghyereh et al (2017) for the sampled application of DCC-GARCH in similar studies.…”

Section: Methodsmentioning

confidence: 99%

“…While this paper is closely related to Malik and Hammoudeh (2007) and Maghyereh et al (2017), who also studied conditional correlations in the context of the GCC region, there are some crucial differences between this study and the past evidence. First, this study considers the shocks in both the gold market and the international equity market-the first of which is absent in Malik and Hammoudeh (2007), and the latter is lacking in Maghyereh et al (2017). Second, this study adopts the DCC-GARCH framework over the simple BEKK-GARCH model used in Malik and Hammoudeh (2007) to model dynamic correlations.…”

Section: Literature Reviewmentioning

confidence: 97%

“…In addition, the highest conditional correlations appear between the S&P 500 and the gold index and between the S&P 500 and WTI index. Moreover, using a DCC-GARCH model and daily data from January 2004 to May 2016, Maghyereh et al (2017) found significant volatility spillover from crude oil to equity markets, and insignificant spillover from gold to equity markets in the Gulf Cooperation Council (GCC) region. By using various multivariate GARCH models, Pandey (2018) recently found that there are significant post-crisis volatility spillovers from both oil and gold markets on BRICS stock markets.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Spillovers between Gulf Cooperation Council’s Stocks, VIX, Oil and Gold Volatility Indices

Alqahtani

Chevallier

2020

JRFM

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This paper analyzes the conditional correlations between the stock market returns of countries that are members of the Gulf Cooperation Council (GCC). The innovative aspects of the paper consist of focusing on three volatility indices: the oil (OVX), gold (GVZ), and S&P500 (VIX) markets (considered in log-difference). We use weekly data and resort to DCC-GARCH modeling. The novelty of the paper consists in revealing that: (i) GCC stock market returns are negatively correlated with each of the volatility measures, and the correlations are stronger during crisis periods; (ii) GCC stock returns are mostly correlated with oil shocks; and (iii) Saudi Arabia and Qatar are the most responsive to all shocks among the GCC countries, while Bahrain correlates weakly to shocks in oil, gold, and VIX. The most striking results feature extra sensitivity of Saudi Arabia and Qatar in terms of volatility indices, which should be the foremost concern of policymakers and banking analysts.

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Section: J Risk Financial Manag 2020 13 X For Peer Review 7 Of 17supporting

confidence: 88%

“…1 See, inter alia, Guesmi and Fattoum (2014) and Maghyereh et al (2017) for the sampled application of DCC-GARCH in similar studies.…”

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Spillovers between Gulf Cooperation Council’s Stocks, VIX, Oil and Gold Volatility Indices

Alqahtani

Chevallier

2020

JRFM

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“…Asai, McAleer, and Yu (2006) provide a detailed discussion of the SV model and its relation to GARCH models. Prior research demonstrates that the DCC‐GARCH model outperforms several alternatives in forecasting volatility and dynamic correlations (Ding & Vo, 2012; Jain & Biswal, 2016; Sari, Hammoudeh, & Soytas, 2010; Maghyereh, Awartani, & Tziogkidis, 2017; Mensi, Hammoudeh, Nguyen, & Yoon, 2014; Singhal & Ghosh, 2016).…”

Section: Introductionmentioning

confidence: 99%

Optimal futures hedging for energy commodities: An application of the GAS model

Lien

2020

Journal of Futures Markets

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This paper applies generalized autoregressive score-driven (GAS) models to futures hedging of crude oil and natural gas. For both commodities, the GAS framework captures the marginal distributions of spot and futures returns and corresponding dynamic copula correlations. We compare within-sample and out-of-sample hedging effectiveness of GAS models against constant ordinary least square (OLS) strategy and time-varying copula-based GARCH models in terms of volatility reduction and Value at Risk reduction. We show that the constant OLS hedge ratio is not inherently inferior to the time-varying alternatives. Nonetheless, GAS models tend to exhibit better hedging effectiveness than other strategies, particularly for natural gas.

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Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach

Zhang

Yang

Zhang

et al. 2023

Journal of Futures Markets

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This paper proposes a new network topology approach to identify the contemporaneous and noncontemporaneous idiosyncratic spillovers of lowermoment and higher-moment risks in commodity futures markets using highfrequency data. Our results show that contemporaneous information has more explanatory power in constructing a network than noncontemporaneous information, especially for higher-moment risk spillover networks. In contemporaneous spillover networks, the role of one commodity future and the structure of the networks vary across different realized estimators. Specifically, gold, silver, and wheat are the main volatility and kurtosis risk transmitters, while corn and silver are the main skewness spillover transmitters. Agricultural futures markets are relatively closed in the volatility and kurtosis risk spillover networks, while in the skewness network, they become closer to precious metal futures. Furthermore, crisis events can enlarge the idiosyncratic volatility spillovers in commodity markets. The total spillover effects of higher-moment risk are stronger than those of lower-moment risk.

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Volatility spillovers and cross-hedging between gold, oil and equities: Evidence from the Gulf Cooperation Council countries

Cited by 126 publications

References 66 publications

Dynamic Spillovers between Gulf Cooperation Council’s Stocks, VIX, Oil and Gold Volatility Indices

Dynamic Spillovers between Gulf Cooperation Council’s Stocks, VIX, Oil and Gold Volatility Indices

Optimal futures hedging for energy commodities: An application of the GAS model

Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach

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