Climate Change and Environmental Risks
Hedging Climate Change News
NYU Stern and the National Bureau of Economic Research
Yale University, the National Bureau of Economic Research and the Center for Economic and Policy Research
Yale University and the National Bureau of Economic Research
NYU Stern, the National Bureau of Economic Research and the Center for Economic and Policy Research
We propose and implement a procedure to dynamically hedge climate change risk. To create our hedge target, we extract innovations in climate news series that we construct through textual analysis of high-dimensional data on newspaper coverage of climate change. We then use a mimicking-portfolio approach to build climate change hedge portfolios using a large panel of equity returns. We discipline the exercise by using third-party ESG scores of firms to model their climate risk exposures. We show that this approach yields parsimonious and industry-balanced portfolios that perform well in hedging innovations in climate news both in sample and out of sample. The resulting hedge portfolios outperform alternative hedging strategies based primarily on industry tilts. We discuss multiple directions for future research on financial approaches to managing climate risk.
The climate is changing, but there is substantial uncertainty around the exact climate trajectory and as well as the economic consequences of climate change. As a result, investors around the world have a huge demand for hedging themselves against the realizations of climate risk. Due to the long-run and non-diversifiable nature of climate risk, standard futures or insurance contracts in which one party promises to pay the other in the event of a climate disaster are hard to implement. Indeed, no counterparty could credibly guarantee to pay claims during a climate disaster event that might materialize in many decades, partly because a bad outcome would mandate all contracts to be paid at the same time. Individual investors are therefore largely constrained to self-insure against climate risk.
In this paper, we propose an approach for constructing climate risk hedge portfolios using publicly traded assets. We follow a dynamic hedge approach similar to Black and
Scholes (1973) and Merton (1973). In this approach, rather than buying a security that directly pays off in the event of a climate change disaster in the distant future, we construct portfolios whose short-term returns hedge news about future climate change over the holding period. By hedging, period by period, the innovations in expected long-run climate change, an investor can ultimately hedge her long-run exposure to climate risk. In the short run, such a portfolio differs from the Markowitz mean-variance efficient portfolio and will thus exhibit a lower Sharpe ratio; but in the long run, the dynamic hedging approach will compensate investors for losses that arise from the realization of climate risk. We show that our approach, which uses tools from standard asset pricing theory, allows us to construct portfolios that can successfully hedge climate news out of sample.
The first challenge to implementing such a dynamic hedging strategy is to construct a time-series that captures news about long-run climate risk, and which can therefore help us to construct an appropriate hedge target. We start from the observation that when there are events that plausibly contain such information about changes in climate risk, this will likely lead to newspaper coverage of these events; indeed, newspapers may even be the direct source that investors use to update their subjective probabilities of the risk of climate change. Our approach in this paper therefore is to extract a climate news series from textual analysis of news sources. A wide range of events covered in newspapers can potentially carry relevant information. The list of topics that are often covered by newspapers in relation to discussions about climate change includes extreme weather events (e.g., floods, hurricanes, droughts, wild fires, extreme temperatures), physical changes to the planet (e.g., sea level changes, glacial melting, ocean temperatures), regulatory discussions, technical progresses in alternative fuel delivery, and the price of fossil fuels.
We construct two complementary indices that measure the extent to which climate change is discussed in the news media. The first is calculated as the correlation between the text content of The Wall Street Journal (WSJ) each month and a fixed climate change vocabulary, which we construct from a list of authoritative texts published by various governmental and research organizations. The WSJ is among the most salient media outlets for market participants, and thus our index captures the intensity of climate change discourse that is accessible to the investment community at very low cost.
Our WSJ Climate Change News Index associates increased climate change reporting with news about elevated climate risk, based on the idea that climate change only rises to media attention when there is cause for concern. An alternative approach is to directly differentiate between positive and negative news in our index construction. To this end, we study a second news-based climate index that is designed to focus specifically on bad news about climate change. This index applies sentiment analysis to climate-related
articles to measure the intensity of negative climate news in a given month.
The second step in implementing our dynamic hedging strategy is to construct portfolios that allow us to hedge innovations in these two news series. In particular, we seek to systematically explore which stocks rise in value and which stocks fall in value when (negative) news about climate change materializes. Then, by constructing portfolios that overweight stocks that perform well on the arrival of such negative news, an investor will have a portfolio that is well positioned to profit the next time when such news about climate change materializes. Continued updating of this portfolio based on new information about the relationship between climate news and stock returns will ultimately lead to a portfolio which is long the winners from climate change and short the losers.
Our econometric approach to forming such hedge portfolios follows standard methods in the asset pricing literature. If climate risk represents a risk factor for asset markets
(i.e, if it is a factor that drives the comovement of different assets), it is possible to construct a well-diversified portfolio whose return isolates the exposure to that risk factor. Investors can then hedge their climate risk exposure by trading this hedge portfolio, without changing their exposures to the other risk factors in their portfolios. Various approaches to construct such hedge portfolios have been proposed in the literature. The two main ones are cross-sectional regressions like Fama-MacBeth (in which the hedging portfolio is obtained through period-by-period cross-sectional regressions of asset returns onto exposures to the risk factors), and direct projections of the risk factor onto a set of asset returns (the so-called mimicking-portfolio approach).1 Among the many prominent papers in this literature are Fama and MacBeth (1973), Chen et al. (1986), Huberman et al. (1987), Breeden et al. (1989), Lamont (2001), Balduzzi and Robotti (2008), Lönn and Schotman (2017), and Roll and Srivastava (2018). Giglio and Xiu (2018) study the asymptotic properties of the different estimators in large cross-sections, and investigate their robustness
to model specification errors. In this paper, we will apply the mimicking-portfolio approach, as advocated by Lamont (2001).
The challenge with implementing these approaches is that we only observe a limited number of months of climate news realizations, but have a large set of assets that we could use to form hedge portfolios. This leads to concerns about data mining, where we construct hedge portfolios that perform very well in-sample but that are not stable going forward. To address this concern, we use characteristics that proxy for a firm’s exposure to climate risk to parsimoniously parameterize the weights of the hedge portfolios. For example, one such characteristic might be the carbon footprint of each firm. In particular, it might be that when there is news about increasing climate risk, individuals will buy low carbon footprint stocks and sell high carbon footprint stocks. If this were the case, one could construct a portfolio that increases in value when there is (negative) news about climate risk using thousands of long and short positions based on just one parameter, the firms’ carbon footprints.
We implement this characteristics-based approach by using firm-level environmental performance scores constructed by the ESG (“Environmental, Social, and Governance") data providers MSCI and Sustainalytics to proxy for firms’ climate risk exposure. In particular, we use these scores as characteristics on which to sort individual stocks to
form portfolios. We then construct the final hedge portfolios by projecting our climate change indices onto these ESG-characteristic-sorted portfolios, together with standard Fama-French factor-sorted portfolios (market, size, and value). When we compare our hedge portfolios to alternative hedge portfolios that add simple industry bets (such as positions in the energy ETF XLE) to the standard Fama-French factors, we find that our ESG-characteristic-based mimicking portfolios procedure produces hedge portfolios that perform better than the alternatives in hedging innovations in climate risk. In particular, our portfolios deliver higher in-sample and out-of-sample correlation with those innovations. Our hedge portfolios also do not resemble industry bets; rather, they identify, both within and across industries, those firms with largest exposures to climate change risk, yielding a climate hedge portfolio that is relatively industry-balanced.
Our work contributes to a burgeoning literature that studies how climate change affects asset markets, and how asset markets in turn may affect the dynamics of climate change. Andersson et al. (2016) propose a passive investment strategy tilted to low-carbon stock as a climate risk hedging portfolio, while Choi et al. (2018) explore how
investors update their information about climate risk. Hong and Li (2018) investigate whether international stock markets efficiently price drought risk, and Kumar et al. (2018) explore whether fund managers misestimate the risk of climate disasters. Giglio et al. (2018), Baldauf et al. (2018), Bernstein et al. (2018), and Murfin and Spiegel (2018) explore the pricing of climate risk in real estate markets, while Giglio et al. (2015, 2018) use real estate pricing data to back out very long-run discount rates that are appropriate for valuing projects aimed at mitigating climate change. In related work, Daniel et al. (2015) apply standard asset pricing theory to calibrate the social cost of carbon.