Abstract:
We review the literature studying interactions between climate change and financial markets. We first discuss various approaches to incorporating climate risk in macro-finance models. We then review the empirical literature that explores the pricing of climate risks across a large number of asset classes including real estate, equities, and fixed income securities. In this context, we also discuss how investors can use these assets to construct portfolios that hedge against climate risk. We conclude by proposing several promising directions for future research in climate finance.
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Our review of the current literature is organized into two parts. In the first section, we discuss efforts to incorporate climate risk into macro-finance models. The pioneering work of Nordhaus (1977) paved the way for thinking about the interaction of the physical process of climate change with the real economy. Early papers in this literature — such as Nordhaus (1977, 1991, 1992) — focused on optimal climate change mitigation, and worked in deterministic settings. As such, these papers did not directly speak to the ways in which climate change affects asset prices and risk premia. Subsequent work extends these models to incorporate different aspects of risk and uncertainty about climate change and its link to the economy. These attributes include the stochastic nature of physical and economic processes as well as uncertainty about models of these processes (see, for example, the work by Kolstad, 1992, Manne et al., 1992, Nordhaus, 1994, Kelly & Kolstad, 1999, Nordhaus & Popp, 1997, Weitzman, 2001, 2009, Lemoine & Traeger, 2012, Golosov et al., 2014). Much of this literature has focused on the way risks and uncertainties affect optimal mitigation policies and the “social cost of carbon.” More recently, the financial economics literature has explored the implications of these models for the prices and returns of financial assets.
In the second part of this review article, we discuss the empirical literature that explores the pricing of climate risk across a large number of asset classes. This literature considers the price effects of at least two broad categories of climate related risk factors: physical climate risk and transition risk. Physical climate risk includes risks of the direct impairment of productive assets resulting from climate change; transition risk includes risks to cash flows arising from a possible transition to a lowcarbon economy. A central element of the research designs in these papers is that assets are differentially exposed to these climate risk factors: for example, houses located near the sea are more exposed to physical climate risks, while coal companies are more exposed to transition risks. Many papers then combine the differential exposure of assets within an asset class with time-varying attention paid to climate risk in order to understand how this type of risk is priced in asset markets. We review research that documents climate-related asset price effects in equity markets, bond markets, housing markets, and mortgage markets. We also discuss recent work that shows how one can use financial assets to construct portfolios that hedge climate change risks.
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To sum up, the debate around the term structure of discount rates for valuing investments to mitigate climate change (and its effects on the social cost of carbon) can in large part be traced to different assumptions about the nature of the shocks that mitigation investments are hedging, and about the dynamics of the economy and the climate in response to those shocks. While this two-dimensional distinction does not fully span the variety of models that have been written in the literature, it helps to understand what has lead the literature to reach different (sometimes opposite) conclusions.
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| https://www.climatepolicyinitiative.org/publication/global-landscape-of-climate-finance-2019/ |
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Lemoine (2020) argues that accounting for model uncertainty leads to higher estimates of the social cost of carbon than would otherwise prevail. ... Uncertainty thus introduces a new channel that impacts asset prices in the form of covariance between model parameters and agents’ consumption. This induces precautionary savings and risk premia effects in addition to those resulting from stochastic shocks in standard unambiguous models. Viewing damage uncertainty as a compound lottery, when the
agent “draws” an especially adverse damage parameter, carbon mitigation becomes especially valuable and raises the social cost of carbon (as long as relative risk aversion is greater than one, as commonly assumed in calibrations of macro and finance models).
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Barnett et al. (2020) analyze the additional incremental effects of ambiguity aversion on the social cost of carbon. Holding fixed the extent of model uncertainty, they compare model calibrations with ambiguity averse investors versus a model with ambiguity neutrality. Ambiguity aversion magnifies the cost of carbon by roughly 60% to 70% in current value terms relative to the baseline scenario with model uncertainty but ambiguity neutrality.
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Krueger et al. (2020) conduct a survey of active investment managers to explore their approaches to managing climate risk. They find that investors believe that climate change has significant financial implications for portfolio firms, and that considerations of climate risk are important in the investment process. For example, 39% of investors in the survey reported to be working to reduce the carbon footprints in their portfolios. These survey responses are also consistent with findings from Alok et al. (2020), who show that fund managers adjust their portfolios in response to climatic disasters. Pedersen et al. (forthcoming) provide an ESG CAPM framework and outline how investor beliefs and preferences regarding climate change risks (and ESG considerations more broadly) fit in with the factor model paradigm that dominates empirical asset pricing research.
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Given the attention that investors dedicate to climate change, a growing literature explores the pricing of various dimensions of climate risk in equity markets (e.g., Hong et al., 2019). Much of this literature has focused on the effects of regulatory climate risk, where different measures of carbon intensity or environmental friendliness are often used as proxies for regulatory climate risk. For example, Bolton & Kacperczyk (2020) analyze U.S. equity markets, and demonstrate that firms with higher carbon emissions are valued at a discount. Quantitatively, the authors estimate that a one standard deviation increase in emissions across firms is associated with a rise in expected returns of roughly 2% per annum. The authors trace this effect at least in part to exclusionary screening performed by institutional investors to limit the carbon risk in their portfolios. In related work, Hsu et al. (2020) show a similar spread in average returns between high- and low-pollution firms, and link it to uncertainty about environmental policy. Engle et al. (2020) document that stocks of firms with high E-Scores — which the authors argue capture lower exposure to regulatory climate risk — have higher returns during periods with negative news about the future path of climate change. Similarly, Choi et al. (2020) explore global stock market data and find that stocks of carbon-intensive firms underperform during times with abnormally warm weather, a period when investors’ attention to climate risks are likely to be particularly high. Barnett (2020) uses an event study analysis to explore financial market impacts of regulatory risk. He finds that increases in the likelihood of future climate policy action lead to decreased equity prices for firms with high exposure to climate policy risk. Similar evidence of the pricing of climate risk can be found in equity options markets. Ilhan et al. (2019) show that the cost of option protection against extreme downside risks is larger for firms with more carbon-intense business models, and particularly so at times when there is an increased public attention to climate risk.
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Climate risks may also affect financial assets beyond equities. Municipal bond markets are a particularly interesting setting for analyzing the financial market implications of climate risk. In particular, when considering the physical risks of climate change, firms may be at risk depending on the location of their production facilities. However, even the most exposed firms usually have the option of relocating their modes of production to other geographies. Municipalities have no such luxury. As a result, one would expect that municipal debt backed by tax revenues from localities more exposed to physical climate risks such as rising sea levels or wildfires would trade at a substantial discount. In evidence along these lines, Painter (2020) shows that at-issuance municipal bond yields are higher for counties with large expected losses due to sea level rise (SLR). Consistent with the hypothesis that such price differences reflect the pricing of climate risk, he finds that this effect is concentrated in long-dated bonds and essentially absent at short maturities over which the likelihood of SLR remains low. In related work, Goldsmith-Pinkham et al. (2019) show via a structural model that this effect of SLR on municipal bond yields is tantamount to a 3–8% reduction in the present value of local government long-run cash flows.
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To implement this dynamic hedging strategy, it is necessary to determine which firms increase or decrease in value when there is news around climate change. Engle et al. (2020) solve this problem by proxying for firms’ climate risk exposures using “E-Scores” that capture various aspects of how environmentally friendly a firm is. The hedge portfolio would then overweight high-E-Score firms, and underweight lowE-Score firms, with the relative weights updated dynamically as more data on the relationship between E-Scores, climate news, and asset prices is obtained. While it is straightforward to construct such a hedge with the benefit of hindsight, the true test of a hedge portfolio is its ability to profit in adverse conditions on an out-of-sample basis. Indeed, Engle et al. (2020) find an out-of-sample correlation of 20% to 30% between the return of the hedge portfolio and innovations in the WSJ climate change news index. In summary, the paper provides a rigorous methodology for constructing portfolios to hedge against climate risks that are otherwise difficult to insure.
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Zillow economist Krishna Rao (2017) calculates that a six feet sea level rise would put 1.9 million homes worth about $882 billion at risk of flooding, with about half the losses coming from Florida alone.
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Using these data, Giglio et al. (2020) show that while properties in a flood zone generally trade at a premium compared to otherwise similar properties (likely because of positive amenities such as beach access), this premium compresses in periods with elevated attention paid to climate risk. Quantitatively, a doubling in the Climate Attention Index (i.e., a doubling in the share of listings that mention climate risk-related words) is associated with a relative 2.4% decline in the transaction prices of properties in the flood zone.
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A number of other papers exploit related research designs to explore the pricing of climate risk in real estate markets. Bernstein et al. (2019) also explore the relationship between house prices and sea level rise (SLR). They find that houses that are exposed to sea level rise sell for a discount compared with observably equivalent unexposed properties. The authors are able to control for the distance from the beach, which allows them to alleviate some concerns around differential amenity values of these properties. Quantitatively, properties that will be inundated after one foot of global average SLR sell at a 14.7% discount, properties inundated with two to three feet of SLR sell at a 13.8% discount, and properties inundated with six feet of SLR sell at a discount of 4.4%. Baldauf et al. (2020) present related evidence suggesting that the extent to which physical climate risk is priced in housing markets depends on whether the local population believes in climate change. Bakkensen & Barrage (2017) explore a similar point, highlighting that when individuals who do not believe in climate change disproportionately sort to purchase more exposed properties, this will reduce the extent to which climate change risk is priced in housing markets.
A related set of papers has explored the effect of hurricanes on house prices, arguing that a recent hurricane makes future costs of climate change more salient to individuals, even in cases where the Hurricane did not affect a particular property. Ortega & Taspinar (2018) show that following Hurricane Sandy there was a significant and permanent relative price decline of New York City properties in flood
zones, even if they were not damaged by Sandy. Gibson et al. (2017) also find that increasing salience of climate risk following hurricanes reduces the relative valuation of more exposed properties in the New York housing market. Eichholtz et al. (2019) find similar results in the commercial real estate market. The authors study transactions in three cities, New York, Boston, and Chicago, before and after the shift in the salience of flood risk caused by Hurricane Sandy. They find that properties exposed to flood risk experience slower price appreciation after the storm than equivalent unexposed properties. As the previous papers, Eichholtz et al. (2019) conclude that the price effect is persistent and not driven by physical damage incurred from Hurricane Sandy
While rising sea levels are a first-order concern for coastal home owners, they are not the only channel through which climate change poses a physical risk for real estate values. Another salient risk of climate change is the increasing danger of wildfires in many states. For example, Corelogic (2019) found that nearly 776,000 homes with an associated reconstruction cost value of more than $221 billion were at extreme risk of wildfire damage. Many of the most at-risk properties are in California MSAs, but properties in Texas and Colorado are also potentially at risk. Garnache & Guilfoos (2019) explore whether such wildfire risk is priced, and find that the price of homes drops when these home are designated to be in a wildfire risk zone (see also McCoy & Walsh, 2018).
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by Stefano Giglio, Bryan T. Kelly & Johannes Stroebel
National Bureau of Economic Research www.NBER.org
Working Paper 28226; Issue Date December 2020
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