Hedge and safe-haven attributes of faith-based stocks vis-à-vis cryptocurrency environmental attention: a multi-scale quantile regression analysis

ABSTRACT

The attractiveness of the equities of the Islamic faith-compliant companies as a hedge or possible diversifier has been underscored; however, there is a lack of empirical research on their safe-haven and hedge attributes against changes in the level of cryptocurrency environmental attention (ICEA). We examine whether various distributions of the ICEA possess a significant predictive power on various quantiles of Islamic sectoral stock returns by employing weekly data on the ICEA and Shariah-compliant stocks from 10 sectors of economic activity and base their multi-scale analysis on the complete ensemble empirical mode decomposition (CEEMDAN) approach. We present the asymmetric causality-in-means and quantile-on-quantile regression between the ICEA and Islamic stocks. The empirical results show a significant predictive power of the ICEA on various quantiles of Islamic sectoral stocks in the medium- and long term. We find that the safe-haven and hedging attributes of investments in Islamic stocks are sector-dependent across the medium- and long-term scales. Hence, our findings emphasize that based on market states, possible safe-haven attributes, diversification opportunities, and hedges for cross-sectoral investments with Islamic stocks are viable along various investment horizons for diverse levels of cryptocurrency environmental attention. These findings provide original valuable insights for portfolio management and improving financial stability.

KEYWORDS:

• Faith-based investments
• cryptocurrency environmental attention
• quantile-on-quantile regression
• Islamic stocks
• economic sectors

JEL CLASSIFICATION:

• C22
• C58
• G10
• G11
• G15
• G19
• G40
• G41

Acknowledgments

This work was supported by FCT, I.P., the Portuguese national funding agency for science, research and technology, under the Project UIDB/04521/2020. The article was prepared within the framework of the Basic Research Program at HSE University.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ We set the bandwidth as $\mathit{h}=0.05$. For $\left(\cdot \right)\mathit{K}$ and $\left(\cdot \right)\mathit{L}$, Gaussian as used the kernel type.

² Over-optimization with nonparametric techniques are mitigated by choosing the lag order based on the sparse Schwarz Information Criterion (SIC) under the VAR comprising of the ICEA and Islamic sectoral stock returns.

³ It is worth noting that the QQR estimations yield a 19 by 19 matrix for each analysed pair. To conserve space, we present them in 3D plots using default plotting tools in MATLAB. The numerical estimates of all QQR estimates are available upon request.

Additional information

Funding

The work was supported by the Fundação para a Ciência e a Tecnologia [UIDB/04521/2020].