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Looking for stability in a volatile stock market? Try green energy

A novel statistical technique used to track global financial markets reveals the potential of clean energy investments to buffer against losses
By Sarah DeWeerdt
August 27, 2024 Anthropocene

Investments in green energy can help optimize investment portfolios and ‘hedge’ against volatile markets, according to a new study.

Investors try to avoid financial losses by diversifying their portfolios with investments that follow different trends. Clean energy investments tend to be more influenced by government policies and technological advances than factors like overall economic conditions and market vibes that affect traditional stock market investments.

The green energy field has also expanded greatly in recent years and looks set to experience continued growth in the future due to the Paris Agreement and other climate policies. As a result, many people have suggested clean energy investmentsas a hedging strategy.

But until now, little has been known about the interactions between clean energy investments and traditional stock markets, and past studies have used varying methods and yielded inconsistent results in terms of whether and exactly when clean energy investments represent a safe harbor during market downturns.

In the new study, researchers a statistical method called tail quintile connectedness regression to evaluate 10 years’ worth of daily price data of various stock market indices, including SP500 (US), FTSE100 (UK), Nekkei225 (Japan), CSI300 (China), and STOXX50 (Europe), and the green investment indices Global Clean Energy Index (GCEI), Green Bond Index (GB), and Renewable Energy and Clean Technology Index (RECTI).

The statistical technique enabled the researchers to trace how market shocks propagate from one index to another, showing the connectedness between different markets and the timing, magnitude, and direction of ‘spillovers’ between them.

Clean energy investments can act as buffers providing stability when markets are fluctuating, the researchers report in the journal Energy Economics.

They found that financial shocks often begin in the US, EU, UK stock markets and the RECTI index, then travel to Japanese markets and the GCEI. Most interactions between markets are short-term, but can be longer lasting during market downturns.

Different clean energy indices play different roles in the global financial system: RECTI is active, GCEI receives information passively, and the Green Bond Index is relatively isolated from the rest of the global financial system.

These three indices also reflect different aspects of clean energy development. The GCEI focuses on companies that are specifically involved in the clean energy sector, while RECTI also incorporates a broader suite of clean technology companies. The Green Bond Index spreads risk even more widely across different bond issuers, market sectors, and geographic regions.

Using a so-called multivariant portfolio design approach, clean energy assets emerge as a key strategy for ensuring stable returns, along with the CSI300 index and a type of petroleum known as West Texas Intermediate, or WTI. Looking at pairs of assets, a bivariant analysis suggests the Green Bond Index as an important player. The upshot, say the researchers, is that clean energy assets should be a prominent part of a diversified portfolio to mitigate risk.

Source: Ziadat S.A. et al. “Are clean energy markets hedges for stock markets? A tail quantile connectedness regression.” Energy Economics 2024

Are clean energy markets hedges for stock markets? A tail quantile connectedness regression

https://doi.org/10.1016/j.eneco.2024.107757Get rights and content

Highlights

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    Examines the spillover dynamics between clean energy markets and international stock markets.

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    Highlights how these spillovers vary across different market conditions and time horizons.

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    Spillovers are concentrated in the short term during normal and bull market conditions.

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    Renewable energy and green bond act as a net receiver of spillover.

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    Suggests significant portfolio weight allocations to clean energy assets, WTI, and CSI300.

Abstract

Acknowledging the long-term potential of alternative energy sources, this paper examines the quantile frequency connectedness between clean energy markets and international stock markets, with implications related to hedging effectiveness. The main results point out that spillovers run from the US, the EU, the UK, and the Renewable Energy and Clean Technology Index to Japan and the Global Clean Energy Index. Furthermore, while the transmissions are concentrated in the short run during normal (0.5) and bull market (0.95) conditions, they extend to intermediate and long-term amid busting market (0,05 quantile) states, signifying a long-lasting impact that cannot be absorbed in the short run. Notably, clean energy index roles in information transmissions range from a net sender (Renewable Energy and Clean Technology Index), isolated (Green Bond Index), and a net receiver (Global Clean Energy Index). From a multivariant portfolio design perspective, we notice that a substantial weight should be allocated to clean energy assets, WTI, and CSI300 when compared with the rest of the financial markets. Moreover, the low (high) volatility regime yields lower (higher) weights than the ones reported in the mean state, but the results remain largely similar. Bivariant portfolio weights show that GB should have substantial weight when paired with all assets.

Section snippets

Gel classification

G14

F36

C40

Literature review

The literature has so far presented different robust models to explain the relationship between the clean energy market and the stock market, applying various empirical methods such as GARCH models (Ahmad et al., 2018; Kocaarslan and Soytas, 2019), vector autoregression (VAR) models (Kyritsis and Serletis, 2019; Rahman et al., 2021), wavelets (Ferrer et al., 2018; Maghyereh et al., 2019), copulas (Bouri, 2017), and cointegration (Bondia et al., 2016). Several other research papers indicate a

Quantile frequency connectedness approach

We follow the Ando et al. (2022) quantile connectedness approach to detect connectedness in various quantiles . Using the Wold representation theorem, the infinite moving average (MA) representation of the QVAR method is defined as:

In line with Koop et al. (1996) and Pesaran and Shin (1998), we compute the generalized forecast error variance decomposition (GFEVD) for forecast horizon as follows:

Data and preliminary analysis

This study comprises daily prices for stock market indices, namely, SP500 (US), CSI300 (China), Nekkei225 (Japan), STOXX50 (Europe), and FTSE100 (UK). To construct diversified portfolios, we also take into account the global Green Bond Index (GB), Global Clean Energy Index (GCEI), and Renewable Energy and Clean Technology Index (RECTI). Our sample data is obtained from the database of DataStream and covers the period from September 30, 2013, to October 11, 2023. The sample period is

Quantile total connectedness analysis

Using the Ando et al. (2022) methodology, Table 3 and Fig. 2 exbibit the static connectedness index and dynamic connectedness index at the quantiles, respectively. Looking at Table 3 and Fig. 2, we can clearly see a large difference in the TCI across quantiles, as the values range from 43% at the median quantile to rise to 84% and 83% at the lower and higher quantiles, respectively. This indicates that level spillovers are contingent on the market regime. In other words, transmissions between

Conclusion

This paper sought to examine the quantile frequency connectedness between clean energy and conventional indices, with implications related to hedging effectiveness. The main results point out that spillovers run from the SP500, STOXX50, RECTI, and FTISE to Nikkei225 and GCEI. Furthermore, while the transmissions are concentrated in the short run during normal (0.5) and bull market (0.95) conditions, they extend to intermediate and long-term amid busting market (0,05 quantile) states, signifying

CRediT authorship contribution statement

Salem Adel Ziadat: Writing – original draft, Conceptualization. Walid Mensi: Writing – review & editing, Writing – original draft, Supervision. Sami Al-Kharusi: Formal analysis. Xuan Vinh Vo: Methodology, Data curation. Sang Hoon Kang: Writing – review & editing, Investigation, Funding acquisition.

Acknowledgements

This research is partly funded by the University of Economics Ho Chi Minh City, Vietnam. he last author acknowledges the financial support by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5A2A01038422).

References (65)

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