**1. Introduction**

The transition to a low-carbon economy entails a vast amount of financial resources, which, in turn, raises awareness among investors about opportunities and risks linked to that transition. Renewable-energy and low-carbon assets are arguably the most suitable investment vehicles to ensure private capital reallocation that meets the challenges posed by decarbonization. Therefore, understanding interdependence between the prices of renewable-energy and low-carbon assets is essential information for environmentally-friendly investors, as it determines the diversification benefits of allocating private capital to climate-resilient portfolios and shapes private incentives to deploy financial resources to clean energies and low-carbon industries. Moreover, interdependence between renewable-energy and low-carbon assets is also of interest for policymakers, as low-carbon investments could provide adequate incentives to invest in renewable energies and vice versa, thereby determining public funds to be allocated to support the transition to a climate-resilient economy.

We examine dependence between renewable-energy and low-carbon stock prices using a conditional quantile price dependence approach that allows price spillovers between those markets to be explored under different market circumstances, including extreme upward and downward movements in asset values ([1]). Specifically, to assess the impact of price movements of a particular size in one market on stock prices in the other market, we characterize the bivariate dependence structure between renewable-energy and low-carbon stock price returns through copulas, then we compute conditional stock return quantiles and evaluate whether these differ from unconditional quantiles.

The extant literature on renewable-energy and low-carbon stock prices has developed along two separate strands.

One strand has examined the relationship between clean-energy and oil prices. Some studies have explored causality between oil prices and renewables, finding evidence of Granger causality that differs across sample periods and time horizons ([2–6]). Other studies have examined oil price spillovers to renewable stocks, documenting significant impacts from oil price oscillations to renewable stock prices ([7–9]), volatility spillovers between oil and clean-energy stocks ([10–12]) and connectedness between clean energy stocks, oil prices and financial variables ([13]). Likewise, a different set of articles have explored dynamic correlations between renewable energy and stock prices ([14]) and the contribution of energy prices to renewable asset prices and volatility ([15–17]).

The other strand has investigated the effects of carbon emissions on firm performance and on investor portfolios. The authors of [18] find that firm value is negatively impacted by carbon emissions, whereas [19] shows that the cost of capital increases with carbon emissions. The authors of [20,21] find that firms with higher carbon emissions earn higher returns, whereas [22] show that higher emissions are related with higher levels of downside risk. From an investor's perspective, the authors of [23] explores a dynamic investment strategy for passive investors to hedge climate risk without sacrificing financial returns, finding that, even for low-carbon indexes with carbon footprints of 50% less than the benchmark, the tracking error can be virtually eliminated; they also indicate that those results could improve with the pricing of carbon dioxide emissions. Similarly, the authors of [24] shows how bond investor portfolios can be hedged against climate risk with no introduction of unintended exposure that could sacrifice a portfolio's benchmark-tracking properties. More recently, in their investigation of investor portfolio divestment from fossil fuels, the authors of [2] find that clean-energy investments offer better returns, whereas [25], in comparing the financial performance of investment portfolios with and without fossil fuel stocks, report that fossil fuel divestment does not seem to impair portfolio performance, given that fossil fuel stocks do not outperform other stocks on a risk-adjusted basis and that fossil fuel stocks provide relatively limited diversification benefits. Likewise, the authors of [26] contend that socially responsible investing has not been costly in terms of forgone market returns, as the return performance of a fossil-fuel-free portfolio surpasses the S&P 500 returns index due to poor fossil fuel sector performance.

From the investors' perspective, the above-mentioned strands in the literature provide useful information on the impact of energy prices or carbon emissions on the value of low-carbon portfolios composed of either renewable energy or low-carbon assets. However, this literature is silent about the impact of changes in low-carbon asset values on renewable energy asset values and vice versa; such information is crucial for climate-friendly investors as both renewable-energy and low-carbon assets are alternative or complementary assets in terms of the design and risk diversification aims of low-carbon portfolios. This paper fills this gap by analysing interdependence between renewable-energy and low-carbon stock prices in a bivariate copula framework and computing how differently sized stock price movements in one market impact on stock prices in the other market. We model price changes in renewable-energy and low-carbon assets using a multifactor pricing model that includes autoregressive components, with co-movement under different market circumstances modelled through copulas taking into account the effect of common pricing factors in that co-movement. Our empirical study covers the period January 2010 to July 2019 and the European and the USA markets, with renewable-energy stocks represented by the European Renewable Energy and the Wilder Hill

Clean Energy indexes, respectively, and low-carbon assets represented by the Euro STOXX Low Carbon Select 50 and the USA STOXX Low Carbon Select 50 indexes, respectively. Our empirical results point to dissimilarities in both stock markets. Specifically, while we observe interdependence between renewable-energy and low-carbon markets in Europe, those markets do not co-move in the USA. Furthermore, we find evidence of symmetric tail dependence in Europe but independence in the USA. We consistently find evidence of symmetric downside and upside price spillover effects between the European renewable-energy and low-carbon stock markets, differing, however, in that price spillovers from low-carbon to renewable-energy stocks are greater than vice versa. Contrarily, for the USA, we find no evidence of price spillovers.

These findings have implications for both investors and policymakers. Investors holding positions in renewables can hedge such positions using low-carbon assets when they invest in the USA market but should seek alternative hedging devices for the European market. As low-carbon and renewable-energy stocks in the European markets behave as a similar asset class, raising funds for renewables from environmentally-aware investors is more difficult as there are opportunities to invest in other low-carbon assets. Finally, our evidence is informative for the design and funding of renewable energy policies: boosting funding to renewables may have a detrimental effect on low-carbon industries in Europe but only a minor effect in the USA.

The remainder of the paper is laid out as follows. In Section 2 we outline our methodology to assess conditional quantile dependence using copula functions. In Section 3 we describe the main features of our data for renewable-energy and low-carbon stock markets in Europe and the USA. In Section 4 we discuss our results on dependence, the impact of price oscillations from/to low-carbon assets and to/from renewable-energy stocks, and the main implications of those results. Finally, Section 5 summarizes our results and concludes this study.
