Quantile dependencies and connectedness between stock and precious metals markets

Prachi, Jain; Maitra, Debasish; McIver, Ron; Kang, Sang Hoon

doi:10.1016/j.jcomm.2022.100284

Cited by 14 publications

(3 citation statements)

References 39 publications

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“…It follows that from a portfolio diversification point of view, when the variables within the network are tradable assets then, including both net transmitters and net receivers within a portfolio of investments will subsequently improve portfolio diversification. In point of fact, the added value from considering connectedness for improving portfolio diversification has been reported by many authors (see, among many others, Antonakakis et al, 2019;Maitra et al, 2021;Mensi et al, 2021a;Jain et al, 2022). Bear in mind though that, periods characterized by very strong connectedness among the variables in the network are rather indicative of fewer diversification opportunities.…”

Section: Implications and Extensionsmentioning

confidence: 92%

Dynamic spillovers across precious metals and oil realized volatilities: Evidence from quantile extended joint connectedness measures

et al. 2022

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Section: Implications and Extensionsmentioning

confidence: 92%

Dynamic spillovers across precious metals and oil realized volatilities: Evidence from quantile extended joint connectedness measures

et al. 2022

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“…The first category of studies considered within-group connectivity of stocks (Pan, et al, 2023;Bouras et al, 2019;Yang et al, 2021;Salisu et al, 2022). Another group of studies analyzed the connectedness of equities with other assets (Jain et al 2023;Mensi et al 2023;Dakhlaoui & Aloui, 2016;Wang et al, 2023). Yet another group of empirical papers on stock market connectedness considered the religious dimension, that is, Islamic stocks with conventional (Bossman, Junior, & Tiwari, 2022;Hassan, et al 2022;Haddad et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

African stock markets’ connectedness: Quantile VAR approach

Yaya,

Adenikinju,

Olayinka

2024

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The present paper investigates African stock markets’ linkages by considering stocks in the continent’s largest economies, specifically Egypt, Kenya, Morocco, Nigeria, South Africa, and Tunisia. Using a dataset that spanned November 25, 2008, to September 18, 2023, the quantile connectedness approach of Chatziantoniou et al. (2021) is employed, and the results unfold these interesting dynamics of African market connectivity: (i) In the bearish market phase, South African stock dominated the entire network, transmitting shocks to the remaining stocks, while Moroccan and Kenyan stocks played similar role mildly. (ii) In the bullish market phase, Nigerian stock dominated the market as a major net transmitter of shock supported by South African and Kenyan stock markets. (iii), The Egyptian and Tunis stock markets are net shock receivers in both the bear and bull market phases. (iv), At the median quantile value, stocks become less riskier and the Kenyan stock market becomes the most vulnerable while Nigerian, Egyptian, and South African stock markets are influenced by other stock markets when markets are calm. (v), Though, African stocks are underperforming, interested portfolio managers will learn from the trading strategies to be adopted to maximize their returns. These findings will benefit portfolio managers, international stakeholders, and regulators.

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“…As a result, the results may be distorted by incomplete commodity markets, as shocks in one market could be mistakenly attributed to another if a market is not included in the volatility spillover network (Huang & Liu, 2023). For instance, Guo et al (2023) considered only agricultural markets; Jain et al (2023) considered solely the precious metal markets. Chen and Mo (2023) considered only the gold markets, but they neglect other commodity markets.…”

mentioning

confidence: 99%

High–low volatility spillover network between economic policy uncertainty and commodity futures markets

Xiang,

Borjigin

2024

Journal of Futures Markets

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Based on the formation and evolution of systemic risk, we study the high‐low volatility spillovers between economic policy uncertainty (EPU) and commodity futures and identify the source of risk accumulation and risk outbreak, as well as the corresponding contagion mechanisms. Upon comparing topological characteristics on each volatility layer, our results demonstrate that high and low volatility spillover networks have different network characteristics and evolution behaviors. At the system level, high volatility spillovers are relatively stronger than spillovers in in low volatility network, while the risk propagation efficiency in the low volatility network is higher. At the market level, EPU is not only an important risk‐emitter but also a risk‐recipient most of the time. Additionally, compared with high volatility network, low volatility network characteristics have greater predictive ability for risk spillover among commodity futures, which means that it contains additional information and provides early warning signals for financial stress.

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Quantile dependencies and connectedness between stock and precious metals markets

Cited by 14 publications

References 39 publications

Dynamic spillovers across precious metals and oil realized volatilities: Evidence from quantile extended joint connectedness measures

Dynamic spillovers across precious metals and oil realized volatilities: Evidence from quantile extended joint connectedness measures

African stock markets’ connectedness: Quantile VAR approach

High–low volatility spillover network between economic policy uncertainty and commodity futures markets

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