Quantile on quantile connectedness between safe-haven assets and stock markets: a portfolio risk perspective
Date
2025
Authors
Mensi, W.
Nabli, M.A.
Guesmi, M.
Belghouthi, H.E.
Kang, S.H.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
North American Journal of Economics and Finance, 2025; 80(102496):1-20
Statement of Responsibility
Conference Name
Abstract
This study investigates quantile-on-quantile connectedness between the stock markets of China, Europe, Japan, the UK, and the US, and safe-haven assets including gold, Bitcoin, and green bonds, employing the methodology proposed in Gabauer and Stenfors (2024). Furthermore, we examine the optimal design of investment portfolios built with these assets using Minimum Variance Portfolio, Minimum Correlation Portfolio, and Minimum Connectedness Portfolio measures. Our key findings show that reversely related quantiles show significantly stronger total connectedness than directly related ones, highlighting the significance of tail risk in portfolio management. The connectedness among these stock markets and safe haven assets is asymmetric and fluctuates over time, especially during major economic events such as the oil surplus of 2014, the Chinese economic deceleration in 2015, the COVID-19 pandemic in 2020, the Russia-Ukraine war in 2022, and the war between Israel and Hamas that began in 2023. We find that gold, Bitcoin and green bonds can act as safe havens for international equities, especially in periods of market stress, but their status depends on market conditions. A portfolio analysis indicates that Bitcoin and the Nikkei 225 index serve as effective hedges against stock market volatility, and that Bitcoin is an important portfolio component with the highest optimal weight.
School/Discipline
Dissertation Note
Provenance
Description
Data source: supplementary data, https://doi.org/10.1016/j.najef.2025.102496
Access Status
Rights
Copyright 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.