Quantile Connectedness and Downside Risk in Portfolio Construction

This paper examines whether quantile-based connectedness measures contain useful information for portfolio risk management. Using U.S. sector equity data, we estimate connectedness measures within a quantile vector autoregression framework and construct portfolios based on the cross-sectional distribution of net connectedness. In particular, sectors identified as extreme shock transmitters receive lower portfolio weights. Our results reveal substantial asymmetries across quantiles. Portfolios constructed using lower-tail connectedness measures exhibit smaller maximum drawdowns and lower expected shortfall relative to both equal-weight benchmarks and portfolios based on upper-tail connectedness. By contrast, median connectedness measures tend to provide more stable overall portfolio performance and lower turnover. The findings also suggest that the informational content of connectedness depends critically on the quantile considered. Lower-tail connectedness becomes particularly informative during crisis periods, suggesting that downside spillovers play an important role in portfolio resilience and systemic risk transmission. Overall, the results demonstrate that quantile connectedness measures provide economically meaningful information for downside risk management and offer a simple and transparent framework for incorporating systemic risk into portfolio construction.