This study expands on the mean-based vector auto-regressive framework by incorporating a quantile vector auto-regressive approach, revealing the behaviour of connectedness in traditional and thematic exchange-traded fund (ETF) markets across different quantiles of the conditional distribution. The variables included exchange-traded funds focused on technology, energy, social media and healthcare. Traditional broad equities ETFs, which are renowned for their diversification benefits and consistent performance, are additionally included in our research. Utilizing daily return data from 2020 to 2023, the study reveals pronounced shock propagation at the tails of the distribution, in contrast to the mean or median. It also evaluates the role of each ETF as either a predominant receiver or transmitter of shocks, influenced by significant market shifts. The findings highlight that SPY, a traditional ETF, is the most resilient fund across various market conditions. Theme-based ETFs such as clean energy, Social Media ETFs and traditional ETFs such as SPY demonstrate diversification potential, particularly in extreme quantiles. The study also observes a significant impact of market crises on the overall connectedness of the funds, underscoring the importance of considering market extremes in portfolio management strategies.
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