Systematic risk in the biopharmaceutical sector: a multiscale approach

Annals of Operations Research https://doi.org/10.1007/s10479-021-04402-8 ORIGINAL RESEARCH Systematic risk in the biopharmaceutical sector: a multiscale approach Gazi Salah Uddin1 · Muhammad Yahya2 Raanadeva Jayasekera5 · Gerhard Kling6 · Stelios Bekiros3,4 · Accepted: 29 October 2021 © The Author(s) 2021 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. Keywords OR in medicine · Biotech · Time-varying copulas · Wavelets · Risk management B Muhammad Yahya Gazi Salah Uddin Stelios Bekiros ; Raanadeva Jayasekera Gerhard Kling 1 Department of Management and Engineering, Linköping University, 581 83 Linköping, Sweden 2 Department of Business Administration, Inland Norway University of Applied Sciences, 2418 Elverum, Lillehammer, Norway 3 Department of Banking and Finance, FEMA and DLT Centre, University of Malta, Msida, Malta 4 Department of Economics, European University Institute, Via delle Fontanelle, 18, I-50014 Florence, Italy 5 Trinity Business School, Dublin 2, Dublin, Ireland 6 Business School, University of Aberdeen, Edward Wright Building S29, Aberdeen, UK 123 Annals of Operations Research JEL Classification C32 · C44 · G1 · G10 · G15 · L65 1 Introduction 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). However, systematic risk and return compared to other asset classes have not been analyzed, which this paper addresses. Since Markowitz (1952) laid the foundations for the capital asset pricing model (CAPM) (Fabozzi & Francis, 1978; Lintner, 1965; Mossin, 1966; Sharpe, 1964), systematic risk, measured by beta coefficients, has been estimated from the relationship between stock and market returns. A literature has emerged to derive time-varying betas using bivariate t-GARCH, Markov switching model, and Kalman filters (for an overview, see Mergner & Bulla, 2008). Limitations such as focusing on a single factor driving risk have been addressed by multifactor models (Fama & French, 1993). Other sources of risk stem from shocks in commodity prices, e.g., oil prices (Boyer & Filion, 2007). In addition, gold serves as an anti-cyclical asset (Baur & Lucey, 2010). This study includes oil and gold prices, focusing on their interconnectedness with the biopharmaceutical sector and other asset classes. Investment horizons differ; hence, 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). Mestre (2021) estimates a time–frequency multi-betas model using an AR-EGARCH with and without wavelets. Aguiar-Conraria and Soares (2014) illustrate wavelet coherency and phase differences between stock market returns and oil prices. In contrast, Kahraman and Unal (2016) apply Vector Autoregressive Moving Average models to predict metal prices. Our approach differs in terms of methodology as we apply dynamic conditional correlation (DCC)-Student-t copulas. In Operations Research, a large body of literature has applied MODWT to decompose time series. These studies conduct forecasting of financial time series (Jana et al., 2021), multiresolution analysis (Kilic and Ugur, 2018), risk assessments for different trading horizons (Tzagkarakis & Maurer, 2020), and multifractal theory (Zhao et al., 2015) among many other applications. In addition, copulas have been used to study time-varying asymmetric tail dependence in portfolio selection (Yan et al., 2020) and the risk exposure to oil price shocks (Shahzad et al., 2021). Our contribution is twofold. To the best of our knowledge, this is the first study to evaluate the connectedness of biotech assets with other asset classes. Specifically, we contribute by examining the potential of various biotech assets (Nasdaq biotech, DJGL US biotech, SP500 pharmaceuticals, and NYSE ARCA Tech) as safe havens in periods of turmoil of major stock market indices (S&P500 composite, FTSE100, and STOXX Europe), commodities (crude oil and gold), and the forex market (Euro to USD exchange rate). Our methodological contribution refers to evaluating the temporal and spectral interdependence among asset classes employing a wavelet-based DCC-Student-t copula. We capture heterogeneous investment preferences of market participants by decomposing time series into frequency horizons. Applying a wavelet decomposition via an entropy analysis captures heterogeneous investment preferences, which are not apparent in the scale-dependent information set. In the context of wavelet analysis, the Shannon entropy might understate the randomness of the data. Consequently, the Wavelet Entropy is commonly used based on the energy distribution of wavelet coefficients. We refer to Zou et al. (2015) for further details. 123 Annals of Operations Research Market participants have heterogeneous investment and risk preferences, resulting in specific term objectives and investment horizons. The wavelet transforms analysis decomposes the time series into signals providing information embedded in the frequency domain. These signals are attributed to short-, medium-, and long-run components based on their frequencies. We utilize a time-varying Student-t copula framework to examine the temporal connectedness among asset classes. Copulas are flexible and efficient in modeling both average and extreme joint movements (tail dependence). Hence, 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. We use daily data sourced from DataStream from 1995 to 2019. This period includes several shocks, including the Asian Financial Crisis (1997–1998), the Iraq war (2003), the Global Financial Crisis (GFC) (20 (...truncated)