Uncovering risk transmission between socially responsible investments, alternative energy investments and the implied volatility of major commodities
Introduction
Socially responsible investments (SRIs) and the renewable energy industry have grown in popularity over the past decade, especially after the global meltdown of 2008/09 and the European debt crisis (Kazemilari et al., 2017; Cupriak et al., 2020). For instance, money flowing into Socially Responsible Investments (SRIs) has increased 18 times from USD 639 billion in 1995 with an annual compound growth rate of 13.6%.1 While SRIs have grown noticeably, the related investments in the renewable energy sector have also witnessed a substantial uptrend over the last few years, growing from $47 billion in 2004 to $279.8 in 2017.2 This significant expansion is mainly driven by the adverse impact of conventional energy sources on global climate change and thus the willingness of governments to cope with deteriorating environmental conditions.3
Any investment which fulfills the criteria of environmental, governance, and social factors (ESG) is considered a form of SRI, thereby excluding investments in firms that sell weapons, alcohol, and tobacco as well as firms that possess poor governance system hence, different from conventional investments (Kempf and Osthoff, 2008). On the other hand, clean & renewable energy and clean technology energy (henceforth C&RTE) investments include investments in firms that are principally developers, manufacturers, installers, and distributors of clean energy technologies i.e., firms that provide C&RTE like hydroelectric, biomass, wind, solar and geothermal.
Given the increasing significance of the SRI and the C&RTE asset classes, the current study investigates how the risk of these assets can be reduced using implied volatility indices of commodities. The contribution of this paper is manyfold. First, good estimates of volatility and correlations are necessary for hedging, risk management, portfolio optimization, and pricing derivatives therefore, modeling and forecasting volatility lies at the heart of modern finance. However, the volatility dynamics of SRIs and C&RTE investments and evidence of possible correlations of both sectors with other available assets like commodities (grain, oil, industrial and precious metals) are largely unchartered territory. The literature on portfolio diversification opportunities between SRIs and C&RTE and other key commodities is not sufficiently developed (Dutta et al., 2018; Dutta et al., 2020; Cupriak et al., 2020). As investing in SRIs and C&RTE bring positive environmental impacts and enhance sustainable socio-economic growth (Ahmad et al., 2018), therefore, it is necessary to uncover the hedging effectiveness of SRIs and C&RTE investments with commodities because historically commodities (especially gold, silver & oil) display weak or negative correlation with equities hence recommended as a useful strategy for hedging, risk management, portfolio optimization, and diversification. Second, there is no literature that has investigated the risk transmission or interrelations between the SRIs, C&RTE, and the market volatility indices like Gold Implied Volatility Index (GVZ), Oil Implied Volatility Index (OVX), and Silver Implied Volatility Index (VXSLV). This is an important contribution to the literature as the investigation of risk transmission and the spillover would reveal whether the investors can diversify the investment risk associated with SRIs and C&RTE indices. Last but not the least, the study is motivated by the changing behavior of SRIs and C&RTE returns over time which casts serious doubt on the Efficient Market Hypothesis (EMH). As the trends, cycles, bubbles, manias, crashes, and other phenomena in the financial market have consequences on market efficiency, therefore it can be argued that the evolving behavior of both SRIs and C&RTE is closer to the concept of Adaptive Market Hypothesis (AMH) of Lo (2004) as opposed to EMH. This implies that the efficiencies and the inefficiencies in the markets co-exist in a logically consistent manner and hence providing the prospects of portfolio diversification. In essence, this study contributes to the existing knowledge in several ways. First, it offers an extension to using SRI and C&RTE indices to gauge the risk associated with implied volatility indices through dynamic conditional correlation. Second, it fills the existing gap by combining the SRI and C&RTE with oil, silver and gold implied volatility indices. Third, this is the first study that relates the evolving behavior of SRIs, C&RTE, and VIX with AMH through the GS test and rolling-sample window analysis. Finally, this is the first study to investigate the hedging effectiveness of commodities in connection with clean technology energy indices.
To achieve the objectives, dynamic correlation analysis is applied based on the bivariate DCC-GARCH model of Engle (2002) that uses standardized residuals to directly estimate the correlation matrix. Simultaneously it minimizes the parameters to be predicted. Sadorsky (2012b) demonstrates the empirical superiority of the DCC-GARCH model by comparing it with various other multivariate GARCH models. We assessed the hedging effectiveness of indices based on the study of (Basher and Sadorsky, 2016). Furthermore, we relate the time-varying behavior of all the series under study with hedging effectiveness through the non-parametric Generalized Spectral (GS) test of (Escanciano and Velasco, 2006).
The findings indicate a negative association between SRIs, C&RTE, and the implied volatility indices of commodities. Precisely, the findings suggest that the implied volatility indices of oil, gold, and silver are better in terms of providing diversification opportunities as compared to the inclusion of oil, gold, and silver as commodities in the portfolio. This is an important finding of this paper that volatility indices of commodities are a better hedge as compared to the commodities themselves. Moreover, we also report the time-varying behavior of SRIs, C&RTE, and volatility indices of commodities through the GS test confirming the presence of AMH. These findings are robust across different employed methodologies namely, DCC-GARCH, ADCC-GARCH, and VAR-GARCH.
The findings of the study could be helpful for portfolio managers, brokers, and market participants to draw the role of implied volatilities of commodities in hedging the risk associated with SRIs and C&RTE investments. Investors use the trends of commodity implied volatility indices to predict the returns from SRIs and C&RTE investments while policymakers can avoid the contagion risk stemming from commodity returns and for better decision-making. The results would help academic researchers to improve the existing asset pricing models by considering dynamic correlations and implied volatilities.
In the following section, a brief literature review along with the hypothesis development is presented followed by the Methodology in Section 3. Data and Results & Discussion are presented in 4 Data, 5 Results. Finally, we conclude in Section 6.
Section snippets
Literature review
The meteoric rise of Sustainable investments in the last two decades has created a massive industry comprising alternative asset classes. The rise of industry has given rise to firms specializing in renewable energy and clean technology. These firms are important for the economies to meet the national agenda and aspirations such as Sustainable Development Goals (SDGs) and the Paris agreement. In other words, these firms are key for the countries to transition to a low-carbon state.
The growth of
Methodology
The extant literature has utilized energy investments as part of portfolio construction (Fritz and von Schnurbein, 2019; Biasin et al., 2019) however only a few studies have considered the role of socially responsible investments in portfolio construction (see for instance, Cupriak et al., 2020). On the other hand, very limited studies consider renewable energy investments (Dutta et al., 2020), and none of the studies have investigated the role of clean technology energies to construct
Data
Our dataset consists of a total of 2100 observations and covers the period from 15 October 2013 to February 14, 2022. The start date and the end date of the sample period are based on the availability of data for the implied volatility index of silver. The index series selected in the study are four socially responsible stock indices, four clean energy stock indices, and three implied volatility indices of strategic commodities. From socially-responsible indices, we employ FTSE4Good data which
Results
Summary statistics are presented in Table 1. As per the results, GVZ and OVX display positive mean returns whereas VXSLV's mean return is negative. All other indices exhibit positive mean returns. OVX has the highest standard deviation from a panel of implied commodity indices followed by GVZ and VXSLV. Among the indices, clean & renewable and clean technology energy are more volatile as compared to their socially responsible counterparts.
All socially responsible and clean energy indices are
Conclusion
Even though SRIs and the C&RTE have emerged as a set of new asset classes, yet it is surprising to find scarce literature on the diversification prospects available to the investors of these asset classes. In this study, we depart from the nascent extant literature on the subject and investigate the hedging effectiveness of implied volatility indices of commodities to diversify the investment risk associated with the SRIs and C&RTE investments.
In other words, we extend the extant literature by
CRediT authorship contribution statement
Muhammad Naeem Shahid: Conceptualization, Methodology, Software. Wajahat Azmi: Data curation, Methodology, Writing – original draft. Mohsin Ali: Visualization, Investigation. Muhammad Umar Islam: Writing – original draft. Syed Aun R. Rizvi: Validation, Writing – review & editing.
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