Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/304194 
Year of Publication: 
2023
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 11 [Issue:] 2 [Article No.:] 2251300 [Year:] 2023 [Pages:] 1-19
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
Our study uses the quantile vector autoregressive (QVAR) network approach to compare the median-based and tail connectedness in BRICS equity markets using daily time series spanning from 3rd March 2020 to 9th September 2022. The study is conducted on both returns and volatility series, and the findings from our static and dynamic analysis can be summarized as follows. From the static perspective, we observe stronger connectedness and spillover effects on the left and right (right only) tails for returns (volatility) series. For the returns series, China and South Africa (Brazil, Russia and India) are net receivers (transmitters) of shocks at the left tail and median quantiles whilst China and Russia (Brazil, India and South Africa) are net receivers (transmitters) at the right-tail, whereas for the volatility series China and India (Brazil, Russia and South Africa) are the net receivers (transmitters) at both quantile tails, whilst Brazil (Russia, India, China and South Africa) is (are) the net receiver(s) (transmitters) at the median. From a dynamic perspective, time-varying total connectedness is higher at the median (tail-end) quantile(s) during the COVID-19 pandemic (Russia-Ukraine war). Moreover, the time-varying market-specific analysis distinguishes which individual equities are most or least vulnerable to systemic tail-risk transmission effects during the COVID-19 pandemic and more recent Russia-Ukraine. Ultimately, these findings are relevant for investors in their search for better hedging opportunities in equity markets as well as for market regulators who can use systematic risk as an early warning signal for contagion and market crash.
Subjects: 
COVID-19
Russia-Ukraine war
stock market
BRICS
QVAR model
tail-risk connectedness
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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