Dynamical linkages between the Brent oil price and stock markets in BRICS using quantile connectedness approach

Chang, Hao-Wen; Chang, Tsangyao; Ling, Yuan Hung; Yang, Yung-Lieh

doi:10.1016/j.frl.2023.103748

Cited by 13 publications

(8 citation statements)

References 18 publications

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“…Currently, they play a key role in shaping various societal decisions, especially those related to the transition to (green) energy. The global oil market should serve as a platform for constant interaction between producers, regulators and users of different origins, despite occasional doubts about its global nature and integration (Dragomirescu- Gaina et al, 2023). The impact of a strong energy market extends beyond the economy as a whole, influencing key domestic markets such as foreign exchange and stock markets, as well as shaping trading methods (Tiwari et al, 2024).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In their research, the authors indicate that shocks in the oil market will spill over into other financial markets. The effects of these markets are increasingly pronounced (Cong et al, 2008). This is especially important because oil is an extremely important industrial raw material that has a direct impact on the work of industries that use the production process in their operations (Bampinas et al, 2023).…”

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confidence: 99%

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Assessing the spillover of shocks from the oil market to the stock market of different industry sectors in America: A quantile regression approach

Bakić

2024

Serb J Management

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The research problem of this paper examines the impact of Brent oil price shocks on stock returns of nine companies from the US market, operating in three different industrial sectors. The observation period covers 2015 to 2023. The research process involves determining the impact of shock transmission using a quantile regression approach. The results show that most of the evaluated quantile parameters are highly statistically significant, i.e. with more than 99% probability. The estimated quantile parameters have the property of being able to observe the spillover effects of shocks in different states of the economy, such as recession, normal state and expansion. The research results suggest that the spillover of shocks from the Brent oil market is most pronounced in the automotive industry sector, that is, in the companies that are most dependent on oil for energy. The significance of the research is reflected in the lack of existing research that deals with the impact of the most important commodity in the world on the prices of company shares with the application of this methodology, which is also a contribution to science. Finally, the results of this research are very relevant for making investment decisions for economic policy makers, investors and company management.

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Section: Literature Reviewmentioning

confidence: 99%

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confidence: 99%

Assessing the spillover of shocks from the oil market to the stock market of different industry sectors in America: A quantile regression approach

Bakić

2024

Serb J Management

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“…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). Other studies have examined the dynamic connectedness of South African stocks with other countries' stocks (Nyakurukwa & Seetharam, 2023;Khalfaoui et al, 2023;Chang et al, 2023). The papers have reported different results regarding the net receivers and transmitters of stock returns.…”

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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“…4. The SIC has been also used in the context of the QVAR analysis by Chatziantoniou et al (2021), Naeem et al (2021), Ando et al (2022), Jena et al (2022), Chen et al (2022b), Tiwari et al (2022) and Chang et al (2023) . The appropriate lag order turned out to be two.…”

Section: Notesmentioning

confidence: 99%

“…To address this limitation, Ando et al (2022) proposed the quantile vector autoregression (QVAR) model that allows the intensity and the pattern of spillovers to vary with small, large negative and large positive shocks. The quantile-based connectedness analysis has been recently used, among others, by Chatziantoniou et al (2021) on interest rate swaps; Naeem et al (2021) on Sukuk bonds; Liu et al ( 2021) on crude oil and stock markets, Ando et al (2022) on credit risk; Chen et al (2022a, 2022b) on energy, metals and carbon markets; Tiwari et al (2022) on energy and agricultural markets; Jena et al (2022) on world petroleum markets; Chang et al (2023) on crude oil and stock returns in BRICS countries; and Nyakurukwa and Seetharam (2023) on BRICS stock markets.…”

Section: Introductionmentioning

confidence: 99%

How does fear spread across asset classes? Evidence from quantile connectedness

Fousekis

2023

SEF

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Purpose This study aims to investigate the connectivity among four principal implied volatility (“fear”) markets in the USA. Design/methodology/approach The empirical analysis relies on daily data (“fear gauge indices”) for the period 2017–2023 and the quantile vector autoregressive (QVAR) approach that allows connectivity (that is, the network topology of interrelated markets) to be quantile-dependent and time-varying. Findings Extreme increases in fear are transmitted with higher intensity relative to extreme decreases in it. The implied volatility markets for gold and for stocks are the main risk connectors in the network and also net transmitters of shocks to the implied volatility markets for crude oil and for the euro-dollar exchange rate. Major events such as the COVID-19 pandemic and the war in Ukraine increase connectivity; this increase, however, is likely to be more pronounced at the median than the extremes of the joint distribution of the four fear indices. Originality/value This is the first work that uses the QVAR approach to implied volatility markets. The empirical results provide useful insights into how fear spreads across stock and commodities markets, something that is important for risk management, option pricing and forecasting.

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Dynamical linkages between the Brent oil price and stock markets in BRICS using quantile connectedness approach

Cited by 13 publications

References 18 publications

Assessing the spillover of shocks from the oil market to the stock market of different industry sectors in America: A quantile regression approach

Assessing the spillover of shocks from the oil market to the stock market of different industry sectors in America: A quantile regression approach

African stock markets’ connectedness: Quantile VAR approach

How does fear spread across asset classes? Evidence from quantile connectedness

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