Hedging Climate News

Robert F. Engle is Michael Armellino Professor of Management and Financial Services at NYU Stern School of Business; Stefano Giglio is Professor of Finance at Yale School of Management; Bryan T. Kelly is Professor of Finance & Associate Director of the International Center for Finance at Yale School of Management; Johannes Stroebel is Professor of Finance at NYU Stern School of Business; and Heebum Lee is a PhD candidate in finance at NYU Stern School of Business. This post is based on their recent paper.

Despite the widespread recognition that the climate is changing, there is substantial uncertainty around the exact climate trajectory and as well as the economic consequences of climate change. As a result, financial market participants have an increasing demand for hedging themselves against future realizations of climate risk. However, hedging these risks through traditional insurance or futures contracts is difficult, both because climate risk is non-diversifiable and because it will materialize over long horizons. As a result, it would be hard for any counterparty to credibly guarantee to pay claims during a climate disaster event that might materialize in many decades. Financial market participants are therefore largely constrained to self-insure against climate risk.

In Hedging Climate Change News, we propose an easily implementable approach for constructing climate risk hedge portfolios using publicly traded assets. Our proposed methodology follows a dynamic hedging strategy using insights from asset pricing theory. In our proposed approach, rather than buying a security that directly pays off in the event of a future climate disaster, we construct portfolios that have short-term returns that hedge news about climate change over the holding period. By hedging, period by period, the innovations in news about 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.

The first step in implementing a dynamic hedging strategy for climate risk is to construct a time series that captures news about long-run climate risk. Innovations in this news series will become the ultimate hedge target. To construct this news series, we start from the observation that events that contain information about changes in climate risk are likely to lead to newspaper coverage. Our approach is therefore to extract a climate news series from textual analysis of news sources. 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 each month and a fixed climate change vocabulary, which we construct from a list of authoritative texts published by various governmental and research organizations. This first index thus associates increased climate change reporting with news about elevated climate risk, based on the idea that climate change primarily rises to the media’s attention when there is an increasing cause for concern. An alternative approach is to directly differentiate between positive and negative news about climate risk in our index construction. To this end, we construct a second climate news 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 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 increase in value whenever negative news about climate change materializes. Continued updating of this portfolio based on new information about the relationship between climate news and stock returns will lead to a portfolio which will pay off over time as climate change materializes.

We form our hedge portfolio for climate change news using standard methods in the asset pricing literature: in particular, we compute the portfolio of all equities that best approximates the movement over time of the climate news hedge target. The resulting mimicking portfolio will be a well-diversified portfolio the return of which isolates the exposure to that target. Investors can then hedge their climate risk exposure by trading this portfolio, that is, by going long and short its underlying components with appropriate weights. To implement this approach, we face an additional challenge in 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 might end up constructing 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. Specifically, 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.

When we compare the hedge portfolios constructed using our approach 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. That is, our portfolios deliver higher in-sample and out-of-sample correlation with those innovations.

We view the primary contribution of this paper as providing a rigorous methodology for constructing portfolios that use relatively easy-to-trade assets (equities) to hedge against climate risks that are otherwise difficult to insure. We do not view our resulting hedge portfolios as the definitive best hedges against climate risk, but instead as a starting point for further exploration. Along these lines, there are a number of valuable directions for future work on using financial markets to hedge climate risk. For example, future research might aim to distinguish between different types of climate-risk news, such as news about risks of physical damages from climate change and news about regulatory risks that are related to governments’ responses to climate change. These two risk measures are clearly related but distinct. Relatedly, the focus in this paper is on global climate change news—our indices do not capture news about local climate events that would not be covered in the WSJ or in a large cross-section of newspapers; certain investors may also choose to hedge against more local climate risks. Further promising directions for research are to explore other measures of firms’ climate risk exposures, or to expand the pool of potential hedge assets beyond U.S. equities.

The complete paper is available for download here.

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