Abstract
It is well documented that the biopharmaceutical sector has exhibited weak financial returns, contributing to underinvestment. Innovations in the industry carry high risks; however, an analysis of systematic risk and return compared to other asset classes is missing. This paper investigates the time–frequency interconnectedness between stocks in the biotech sector and ten asset classes using daily cross-country data from 1995 to 2019. We capture investors' heterogeneous investment horizons by decomposing time series according to frequencies. Using a maximal overlap discrete wavelet transform (MODWT) and a dynamic conditional correlation (DCC)-Student-t copula, diversification potentials are revealed, helping investors to reap the benefits of investing in biotech. Our findings indicate that the underlying assets exhibit nonlinear asymmetric behavior that strengthens during periods of turmoil.
Highlights
The literature has documented mediocre financial returns in the biopharmaceutical sector accompanied by high risks due to drug pipelines and economic conditions (Fagnan et al, 2013; Fernandez et al, 2012; Gopalakrishnan et al, 2008)
We find that over the pre-2000 period, the VaRs are higher for biotech assets, whereas, over the post-2000 period, the VaRs have significantly declined with few periods of abrupt changes, especially during the Iraq war and the Global Financial Crisis (GFC), in line with the findings of Thakor et al (2017)
We utilize a maximal overlap discrete wavelet transform (MODWT), which refers to a modified version of a discrete wavelet transform (DWT)
Summary
The literature has documented mediocre financial returns in the biopharmaceutical sector accompanied by high risks due to drug pipelines and economic conditions (Fagnan et al, 2013; Fernandez et al, 2012; Gopalakrishnan et al, 2008). Investment horizons differ; we apply a maximal overlap discrete wavelet transform (MODWT) to decompose short and long-term price movements This approach is in line with recent studies by Aguiar-Conraria and Soares (2014), Kahraman and Unal (2016), and Mestre (2021). The combination of wavelet decomposition and time-varying Student-t copula provides information that enhances our understanding of dependence among asset classes in periods of turmoil or stability. These findings are essential for risk assessment and portfolio management decisions over different investment horizons.
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