NTNU Norwegian University of Science and Technology Faculty of Economics and Management Dept. of Industrial Economics and Technology Management
Jonas Sjong Forfot Henrik Giske Fosse
The green halo debt effect
Master’s thesis in Industrial Economics and Technology Management
Supervisor: Ana Jantarada, Maria Lavrutich, and Rita Pimentel June 2021
Master ’s thesis
Jonas Sjong Forfot Henrik Giske Fosse
The green halo debt effect
Master’s thesis in Industrial Economics and Technology Management Supervisor: Ana Jantarada, Maria Lavrutich, and Rita Pimentel
June 2021
Norwegian University of Science and Technology Faculty of Economics and Management
Dept. of Industrial Economics and Technology Management
The green halo debt effect
Jonas Sjong Forfot, Henrik Giske Fosse
Supervisors: Ana Jantarada, Maria Lavrutich, and Rita Pimentel
A B S T R A C T
In this paper, we investigate the hypothesis that green bond issuance is a risk-reducing signal of climate commitment. In particular, we test whether the outstanding debt of bond issuers experiences a positive price shift following the announcement of green bond issuance, agreen halo debt effect. We quantify the effect by conducting an event study where we compare green bond announcement events to vanilla ones. For each event, we calculate abnormal returns of the issuer’s outstanding bonds, before regressing the abnormal returns on appropriate control variables. Our results suggest that green bond labeling significantly increases the abnormal returns experienced by outstanding bonds on average. We further show that externally reviewed green bonds experience a stronger effect, and also document substantial regional and industrial heterogeneities. Conclusively, by documenting issuer-wide positive bond price shifts following green bond announcements, we provide evidence of the green halo debt effect.
1. Introduction
Climate change represents one of the greatest challenges facing humanity today. To combat this, green bonds were invented as a tool to attract capital towards green investments.
As the market has gained traction, academic attention towards the subject has increased. One of the most commonly researched questions within the subject of green bonds is whether there exists agreenium, a yield differential between green bonds and comparable vanilla bonds1(Harrison et al., 2020). To the extent that such a differential exists, the greenium provides issuers with a financial incentive to issue green bonds by making debt capital cheaper.
Greenium research tends to base itself on the notion that for green bonds to provide a financial advantage to the issuer, bond investors must be willing to accept a lower yield (Flammer,2021;Larcker and Watts,2020). In other words, the issuer’s bond pricing advantage is the bond investor’s pricing disadvantage. However, this notion ignores any positive, issuer-wide externalities associated with green bond issuance. In particular, it disregards the potential of green bond issuance to positively affect the issuer’s equity and debt, so-calledgreen halo effects2(Harrison et al.,2020). In this paper, we investigate the existence of the green halo effect on debt, thegreen halo debt effect. We thereby complement greenium research, expanding the discussion of financial incentives to issue green bonds.
Recent studies suggest that green bonds serve as signaling tools in addition to their purpose as financial instruments (Flammer,2021;Harrison et al.,2020;Maltais and Nykvist, 2020). Flammer (2021) develops a climate commitment signaling hypothesis for green bonds and corroborates it by ruling out alternative rationales for green bond issuance such
1In this paper,vanilla bondrefers to any bond that is not green.
2For more information on green halo effects, see Financial Times, "I can see your (green) halo," January 30, 2018, GreenBiz, "Green bonds may have a ’halo effect’ on financial markets," February 7, 2018, Markets Media,
"Green bonds may have halo effect," November 3, 2019, NatWest, "Green halo 2.0," June 24, 2019, and NatWest, "Greeniums and ’Halo’ effect – green bonds make financial sense," May 9, 2019.
as greenwashing.Maltais and Nykvist(2020) document that from the industry perspective, the main incentives to issue green bonds are not financial, but rather legitimacy seeking and reputational signaling.
Given the role of green bonds as signaling tools, it becomes clear why a green halo debt effect might materialize.
Improved environmental, social, and governance (ESG) or corporate social responsibility (CSR) is known to reduce issuers’ levels of risk (Godfrey et al.,2009;Hoepner et al., 2018) and bond yields (Bauer and Hann, 2010; Ge and Liu, 2015; Hasan et al., 2017; Oikonomou et al., 2014).
If the signal of climate commitment following green bond announcement extends to bondholders, outstanding bonds may therefore undergo a positive price shift.
The existence of the green halo debt effect would imply that existing bondholders derive value from green bond issuance. Additionally, if these secondary market effects carry over to the primary market, it would provide bond pricing advantages to the issuer as well. Contrary to the greenium, the green halo debt effect would provide an issuer- wide price shift of outstanding bonds. This means the effect would not be observable as a yield differential between green and vanilla bonds (greenium), but rather as a yield shift in time. Therefore, the pricing advantage of the green halo debt effect would come in addition to the greenium, affecting the entire portfolio of debt rather than the issued green bond in particular.
Despite the fact that several studies document a signifi- cantly positive equity market reaction following the announce- ment of green bonds (Baulkaran,2019;Flammer,2019;Tang and Zhang,2020), no prior research has considered market- wide effects for bonds, as emphasized by Harrison et al.
(2020) andSchiereck et al.(2019).
In this paper, we bridge this knowledge gap by empirically investigating whether the green halo debt effect exists. To do so, we construct an extensive data set spanning the bonds from all corporate green bond issuers and their peers that are available from Refinitiv Eikon. This data set covers the period from 2009 through 2020 and contains 26,111 bonds
from 1,432 issuers.
By employing a methodology based on best practices in bond market event studies (Bessembinder et al., 2009;
Ederington et al., 2015; Maul and Schiereck,2017), we test whether green bond announcements affect outstanding bond prices differently than vanilla bond announcements.
To ensure that this effect is not caused by other factors, we control for a large set of bond and issuer characteristics and macroeconomic effects.
Our main finding is that outstanding bonds on average experience a significant yield spread reduction following a green bond announcement compared to a vanilla one.
This confirms the existence of a green halo debt effect, complementing the studies that document similar green halo effects on stocks (Baulkaran,2019;Flammer,2021;Tang and Zhang,2020). The direct beneficiaries of the green halo debt effect are the existing bondholders, since they experience an asset appreciation. However, the issuer could also possibly benefit from this asset appreciation because the yield of outstanding bonds serves as an indicator of future bond issuance yields (Fridson and Gao,1996). If the effect persists and issuers can capture the entire yield spread reduction observed on outstanding bonds, investment grade and high-yield issuers could reduce new issuance yields on average by 2-3 and 8-11 basis points (bps), respectively.
We also find that issuers with externally reviewed green bond frameworks3experience a stronger green halo debt ef- fect. This corroborates the signaling hypothesis ofFlammer (2021), as the second party assures the issuer’s green commit- ment. This is in line withBaker et al.(2018) andHyun et al.
(2020), who find greater greeniums for externally reviewed green bonds.
Our results further suggest that the green halo debt effect exhibits some industrial heterogeneity. First, the green halo debt effect is particularly strong in consumer-oriented industries, which can be explained by consumers’ strong green preferences, as shown by several surveys (Barton et al., 2018;Haller et al.,2020;Nielsen,2014). Second, the effect is also significant in electric utilities. This industry is facing great transitional and physical climate risks while also being the key to reducing other industries’ carbon emissions (Fryer et al.,2017).
We also identify substantial regional heterogeneity in the green halo debt effect. The effect is only present for bonds from European and North American issuers. Possible expla- nations for this are that either the issuers send more credible green commitment signals or that investors have stronger envi- ronmental preferences in western markets than in developing ones4(Morningstar,2021). These conclusions are supported byZerbib(2019), who studies the greenium globally, but finds a significant effect only for bonds denominated in Euro and U.S. dollars.
3Issuers’ green bond frameworks are commonly reviewed by third parties to assure investors that the issuer follows the Green Bond Principles (ICMA,2018).
4For a geographic breakdown of responsible investment firms, see Principles for Responsible Investments, "Signatory directory."
Although the green halo debt effect serves as a financial incentive for green bond issuances, it is unlikely that this cheaper access to capital single-handedly will result in more green projects. Currently, projects determine financing and not vice versa (Maltais and Nykvist,2020). However, combined with other incentives, such as green halo effects on stocks (Flammer,2021;Harrison et al.,2020), broadened investor base5, increased bond liquidity (Febi et al.,2018), and the greenium (Zerbib,2019), it might explain why issuers prefer issuing green bonds to vanilla ones when financing green projects despite their constraining nature. Issuers will likely continue to issue green bonds to signal to stakeholders, including debt investors, that they will continue to combat climate change.
This study contributes to the literature on incentives to issue green bonds. The green halo debt effect broadens the incentives discussion to explore other potential financial benefits of green bond issuances than the greenium. As both issuers and bond investors can experience financial benefits from a green bond issuance over a vanilla one, we relieve the zero-sum notion of greenium research.
The remainder of this paper is organized as follows.
Section2discusses relevant literature. Section3describes the data. Section4describes the event study and regression methodology. Section5presents our main findings. Section 6concludes the paper.
2. Literature review
Since the inception of the green bond market, the question of whether green bonds lower the cost of debt capital has been extensively debated (Harrison et al.,2020). A large body of research has concentrated on measuring this through the greenium, and the literature typically distinguishes between greeniums in the primary and secondary markets.
In the secondary markets, some empirical evidence points towards the existence of a greenium (MacAskill et al.,2021).
However, there is no clear consensus regarding its size and sign. For instance,Hachenberg and Schiereck(2018),Karpf and Mandel(2018), andNanayakkara and Colombage(2019) find a1,−8, and63bps greenium on average in the secondary market, respectively. Also, it is not clear whether a secondary market greenium is transferable to the primary market.
A greenium in the primary markets, on the contrary, would provide direct issuer incentives.Bachelet et al.(2019), Ehlers and Packer(2020),Gianfrate and Peri(2019),Hyun et al.(2020),Li et al.(2020), andZerbib(2019) all match green bonds issuances to (synthetic) vanilla bonds, and find on average a 9 bps greenium in the primary market (ranging from 0 to 21 bps).Bachelet et al.(2019) andEhlers and Packer (2020) also find that the greenium is larger for corporate issuers than municipalities.
Baker et al. (2018), Hyun et al. (2020), and Li et al.
(2020) find that adding external reviews and Climate Bonds Initiative (CBI) certifications increase the greenium by 5
5See Bloomberg, "Muni-bond buyers want tax breaks. Saving the Earth? not much," April 11, 2019.
to 20 bps. They explain that external reviews increase the perceived greenness of the issuer and reduce information asymmetry. Extending this framework,Kapraun and Scheins (2020) connect the greenium to ESG rating, and find that greener issuers experience around 15 bps greater greeniums.
Conclusively, issuers’ green credibility seems to affect the greenium. Our findings suggest that the same holds for the green halo debt effect.
Some studies suggest that the greenium effect in earlier papers is due to a supply shortage which no longer exists (Febi et al.,2018;Larcker and Watts,2020;Zerbib,2019).
Febi et al.(2018) find that early green bond issuances had a liquidity premium.Zerbib(2019) adjusts for the liquidity effect, and finds a greenium of 2 bps which is smaller than found by previous studies.
More recent studies are unable to find a statistically significant greenium effect (Flammer,2021; Larcker and Watts,2020; Tang and Zhang,2020). Larcker and Watts (2020) employ exact matching on municipal bonds, and find no greenium when issuers simultaneously issue green and vanilla bonds with identical characteristics except for the green use of proceeds. They argue that the synthetic matching methods utilized by other greenium papers are superfluous when exact matching is possible, proving unable to capture all bond pricing factors.
Conclusively, no consensus has been reached regarding the greenium (MacAskill et al.,2021;Schiereck et al.,2019).
Earlier papers tend to find economically significant greeni- ums, but the more recent studies byFlammer(2021),Lar- cker and Watts(2020), andZerbib(2019) are less optimistic.
Schiereck et al.(2019), in their review of the greenium lit- erature, conclude that even the sign of the greenium has not been clarified. Ultimately, there is a need to broaden the discussion on financial incentives to issue green bonds, such as by investigating the green halo debt effect.
Academia’s attention towards a strict definition of the greenium seems to have restricted the search for alternative bond pricing advantages. Specifically, the greenium discus- sion bases itself on the premise that green bond issuance represents a financial trade-off between the bond issuers and investors (Flammer,2021; Larcker and Watts,2020).
Larcker and Watts(2020) set out to “examine whether in- vestors are willing to forgo pecuniary benefits to invest in environmentally friendly projects.” Since no greenium is found, the authors conclude that “the cost of capital benefits [of green bonds] appear largely hypothetical” (Larcker and Watts,2020). Furthermore, in the development of potential rationales for issuing corporate green bonds,Flammer(2021) distinguishes between the signaling argument and the cost of capital argument. The former rationale entails that green bonds represent a signal of the issuer’s environmental com- mitment, predicting a positive stock market reaction to green bond announcements. The latter entails that green bonds provide issuers with cheaper financing. When applying the methodology ofLarcker and Watts(2020),Flammer(2021) finds no corporate bond greenium. This result is then argued to be inconsistent with a cost of capital argument, corrobo-
rating the signaling hypothesis.
In this paper, we provide evidence that the conclusions drawn byLarcker and Watts(2020) andFlammer(2021) are not necessarily complete. If green bonds represent a signal to investors of climate commitment, resulting in a positive stock market reaction, one would expect a similar positive bond market reaction. This could indeed have consequences for issuers’ cost of (debt) capital. To the extent that the effect would carry over to primary markets, the issuer itself could experience a bond pricing advantage. In this setting, the signaling and the cost of capital hypotheses ofFlammer (2021) coincide. Green bond issuance is no longer necessarily a zero-sum game where issuers can only benefit from lower yields at the expense of bond investors.
The effect of green bond issuance on issuers’ overall cost of debt capital has been neglected by green bond researchers to date (Schiereck et al.,2019). Specifically,Schiereck et al.
(2019) suggest that the effect of green bond issues on the overall cost of debt should be investigated by differentiating between issuers’ cost of debt before and after their first green bond issuance. Drawing on research from stock markets (Oestreich and Tsiakas,2015),Schiereck et al.(2019) hypothesize green bond announcements to have a negative effect on later vanilla issuances. Since the proceeds of a green bond must be channeled towards green projects, a possible consequence of green bond issuance is that the number of green investment opportunities available to the issuer starts depleting. Later vanilla issuances, then, can only finance remaining investment projects, potentially subject to relatively high carbon risks. This suggests that later vanilla issuances should be subject to higher yields, meaning the green bond has contributed to an adverse effect on the issuer’s cost of debt capital.
This reasoning is in contrast to recent industry attention towards green halo effects2, that is, the tendency of green bond issuances to have a positive pricing effect on the issuer’s equity and debt (Harrison et al.,2020). Concentrating on the debt aspect of such effects, the green halo debt effect reflects the idea that by issuing a green bond, the issuer attracts a more sustainability-focused investor base, putting downward pressure on the issuer’s entire yield curve6. Our study is the first to explore this effect. According toHarrison et al.
(2020), the green halo debt effect complicates the search for a greenium since vanilla bonds might also experience a price shift. In our view, this is precisely what makes the green halo debt effect interesting.
Another stream of literature related to our study focuses on how equity markets respond to the announcement of green bond issuance (Baulkaran, 2019; Flammer, 2021;
Tang and Zhang, 2020). It is found that stock prices experience significantly positive abnormal returns around green bond announcement events, unlike following vanilla bond announcements (Flammer,2021). In the terminology ofHarrison et al.(2020), there is evidence of a green halo effect on equity. Additionally, green bond issuance increases the liquidity and institutional ownership of the issuer’s equity
6See Financial Times, "I can see your (green) halo," January 30, 2018.
(Tang and Zhang,2020), as does the ownership by long-term and green investors (Flammer,2021). Conclusively, green bond issuances are beneficial to stock owners.
An interesting question relevant to the discussion on green halo effects is whether financial incentives are even perceived as drivers of green bond issuance. It seems that this is not currently the case (Chiang,2017;Flammer, 2021;Maltais and Nykvist,2020). Through interviews with several categories of practitioners in the Swedish green bond market,Maltais and Nykvist(2020) find that business case incentives and legitimacy seeking dominate issuance. This makes green bonds more of a tool to improve stakeholder perception through reputational signaling than a means of obtaining cheaper financing. While this shows that the motives to issue green bonds are primarily non-pecuniary, it demonstrates the importance of green bonds as signaling tools. Furthermore, since stock owners and bondholders are also stakeholders, improving stakeholder perception might have implicit financial consequences for the issuer.
To form a hypothesis about the existence of the green halo debt effect, we look to studies of how increased attention towards CSR and ESG among corporations affects their debt. Several authors find evidence of a negative relationship between CSR and bond yields, suggesting that increased CSR corresponds to decreased financial risk and vice versa (Bauer and Hann,2010; Oikonomou et al., 2014). Considering the U.S. in particular,Ge and Liu(2015) andHasan et al.
(2017) find that these effects carry over to primary markets as well. Relatedly, Godfrey et al. (2009) and Hoepner et al.(2018) document a negative relation between ESG and risk, suggesting that the outstanding debt of bond issuers appreciates under ESG improvements. With this in mind, we hypothesize green bond issuances to yield a positive price shift on the outstanding bonds of the issuer, that is, a green halo debt effect. Given the role of green bonds as signaling tools of climate commitment (Flammer,2021;Harrison et al., 2020;Maltais and Nykvist,2020), the announcement of green bonds should have a positive impact on issuers’ outstanding bond prices since such commitments are improvements in issuers’ CSR and ESG.
3. Data
Our initial data set comprises all corporate non-financial bond issuers that have issued at least one green bond between January 2009 and December 2020 from Refinitiv Eikon.
We search for peer companies to the green bond issuers in such a way that we have at least ten issuers for every Refinitiv EikonTRBC Industry. We fetch all bonds, green and vanilla, issued by the selected corporates, that have been in the market during the considered time period. For each bond, we include daily clean closing mid price time series in addition to fundamental bond characteristics such as the maturity, coupon, credit rating, seniority, and issue size (in U.S. dollars). Credit ratings and seniorities are mapped to numeric scales as described in AppendixA. We further search through each green bond issuer’s online resources to determine which green bonds have external reviews.
We require bonds to have a maturity between 1 and 50 years, thereby excluding all bills. Further, all bonds are required to have anAsset Statusof either Issued,Called, Expired/Matured,Put, orRe-opening. Any bond which has missing data for any of the aforementioned fundamental characteristics are also removed. Moreover, any daily return beyond 20% in absolute value is regarded an outlier and removed. This leaves us with 26,111 bonds from 1,432 issuers.
To compute characteristics of bond announcement events, we combine bonds that share issuer and announcement dates into composite bonds, as recommended byEderington et al.
(2015). This procedure is only applied to the bonds that have announcement dates between January 2009 and December 2020. Maturities, seniorities, and coupons of bonds are combined as weighted averages, weighting by each bond’s issue size. A composite issue size is computed as the sum of underlying bonds’ issue sizes. Ratings for composite bonds are set to the highest rating among the underlying bonds. Maturity dates are calculated by adding the underlying bonds’ weighted average maturity to the earliest issue date among them. Next, we remove composite bonds for which the underlying bonds’ fundamental characteristics are very different. Specifically, we require a difference between the earliest and latest issue date of at most ten days, and a difference between the highest and lowest seniority of at most one unit. Also, we remove all composite bonds for which the underlying bonds are a mixture of green and vanilla bonds.
To avoid overlaps between bond announcement events, for any pair of composite bonds from the same issuer where the announcement dates differ by five days or less, both composite bonds are removed. This leaves us with 10,609 composite bonds.
We next map each composite bond to issuer-specific financial data corresponding to the latest fiscal year before each composite bond’s year of announcement. We include issuers’ total assets, total long-term debt, total debt, market capitalization, return on assets, and profit margin. When issuer data is unavailable, we instead map to corresponding parent company data if possible. In addition, we map each issuer’s home country and industry to appropriate categories, as described in AppendixA. For combinations of issuers and years where any of these fields are missing, all corresponding bonds are removed. Additionally, since very few composite bonds are issued in African countries, all African composite bonds are removed. This leaves us with 7,614 composite bonds.
Table1shows summary statistics for the final bond data set. As can be seen, the bond data set consists of far more vanilla than green bonds. In the data set, green bonds are typically smaller issuances, with shorter maturities and lower coupons than vanilla bonds. In terms of rating and seniority, the two groups of bonds exhibit only small differences.
Notably, however, the vanilla bonds are issued roughly uniformly across the last decade, whereas the majority of green bonds have been issued over the last couple of years.
Since larger issuers typically issue more bonds than smaller
ones, the bond data set exhibits event clustering. This is relevant for the interpretation of Table1, since it shows issuer- specific financial variables averaged over bonds rather than issuers.
4. Methodology
To test if the green halo debt effect exists, we utilize event study methodology. We first compute the abnormal returns of outstanding bonds in the event of announcing a new bond. We primarily measure abnormal returns as average abnormal standardized returns (AABSRs) (Ederington et al., 2015). Additionally, for easier interpretation of the abnormal returns, we also calculate a cumulative average abnormal return (CAAR) for each event (MacKinlay, 1997). The final step of our methodology involves applying regression analyses over the AABSRs and CAARs to control for bond and issuer characteristics and macroeconomic effects.
For detailed explanations of abnormal return calculations, see AppendixB7. For explanations of regression variables, see AppendixA. We also conduct a series of robustness tests in order to test the robustness of the regression results. Details are provided in AppendixC.
4.1. Abnormal returns
We compute abnormal bond returns by utilizing a match- ing market portfolio model, as recommended byMaul and Schiereck(2017) in their extensive bond event study method- ology analysis. FollowingBessembinder et al.(2009), we create benchmark portfolios based on bond rating and matu- rity categories.
To increase the testing power and handle the heteroskedas- ticity in abnormal returns, we use abnormal standardized returns (ABSRs), as proposed byEderington et al.(2015).
This involves standardizing the bond and benchmark returns in the event window before computing abnormal returns.
As recommended byEderington et al.(2015), we also utilize event window broadening with the ABSRs. For every outstanding bond, we compute multiple ABSRs with differing start and end days. The ABSRs for these windows are then averaged to create a single ABSR value. The bond volatility and benchmark calculations are also adapted accordingly.
Event window broadening is a compromise where precision in capturing abnormal returns is sacrificed to increase sample size. Event window broadening relieves problems with sparse trade data and noisy bond announcement date data. In our study, we calculate ABSRs over a nine-day event window centered at the announcement date. We refer to this as ABSR{-4,4}.
For a bond announcement event, one value of ABSR{-4,4}
is computed for every outstanding bond of the issuer and then averaged to create a single value for the announced bond, that is, the AABSR{-4,4}. For an event, if no ABSR{-4,4} val- ues are computable, the AABSR{-4,4} is undefined. This is known as the firm level approach, recommended byBessem- binder et al.(2009) andMaul and Schiereck(2017). In ad-
7Accompanying Python code is available upon request.
dition to the AABSR, we also aggregate outstanding bond characteristics on the event level. Specifically, for each event, we calculate a maturity, coupon, seniority, and rating of the outstanding bonds as a weighted average of the corresponding characteristics of all outstanding bonds for which the ABSR value is computable, weighted by each bond’s issue size.
As an alternative measure of abnormal returns, we also calculate CAARs for each bond in the bond data set. The primary difference between CAARs and AABSRs is that CAARs are unstandardized, yielding a more interpretable but less statistically powerful measure of abnormal returns (Ederington et al.,2015). In this study, CAARs are calculated using the same benchmark portfolios and nine-day event window as AABSRs, which we refer to as CAAR[-4,4].
4.2. Regression analysis
After computing abnormal returns, we apply regression analysis to explain how different properties of the announced bond, the outstanding bonds, the issuer, and macroeconomic effects affect the abnormal returns. The most relevant vari- able for this study is a dummy variable representing whether the announced bond is a green bond. This variable determines whether outstanding bonds experience a significantly differ- ent price shift on average from green bond announcements than from vanilla ones.
To control for other factors that can explain how bond prices react to bond announcement events, we include a comprehensive set of control variables. We select the variables based on other studies of bond market responses to the announcement of debt issuance (Akhigbe et al.,1997;
Chen and Stock,2018;Cook and Easterwood,1994;Kolodny and Suhler,1988;Maul and Schiereck,2018;Ongena et al., 2014), as well as a broader stream of bond market event studies (Campbell et al.,2016;Chen et al.,2014;DeFond et al.,2011;Eberhart et al.,2008;Ellul et al.,2011;Kedia and Zhou,2014;Mansi et al.,2004;Massa and Zhang,2021;
Nishikawa et al.,2011;Plunus et al.,2012;Shi and Zhang, 2008).
Table 1: Summary statistics for the data set of composite bonds. See AppendixAfor the definition of each variable. External review,Rating,Seniority,Region,Industry, andYear show how vanilla and green bonds are distributed. All other variables show average values taken over all vanilla and green bonds, respectively. Seniority values are rounded off to the closest integer. All issuer-specific financial variables correspond to the latest fiscal year before each bond announcement.
Green bonds Vanilla bonds
Number of bonds 322 7,292
Amount issued ($M) 351 719
Maturity (years) 7.2 8.9
Coupon (%) 3.1 3.3
Total assets ($M) 31,032 63,555
Total long-term debt ($M) 7,140 20,643
Total debt ($M) 8,788 27,028
Market capitalization ($M) 331,611 1,525,881
Return on assets (%) 3.7 4.1
Profit margin (%) 36.0 10.3
Long-term leverage ratio (%) 29.5 28.2
Leverage ratio (%) 38.3 37.2
External review (% of total)
With external review 67.7 -
Without external review 32.3 -
Rating (% of total)
6 16.1 10.3
5 14.6 15.6
4 20.5 32.1
3 33.9 28.8
2 9.9 8.3
1 5.0 4.9
Seniority (% of total)
7 13.0 7.4
6 75.8 68.2
5 0.0 0.1
4 0.0 0.2
3 0.0 0.0
2 0.0 0.3
1 11.2 23.9
Region (% of total)
Europe 39.4 22.7
North America 16.5 26.3
Asia-Pacific 35.4 44.2
Latin America 8.7 6.8
Industry (% of total)
Electric utilities 29.5 12.3
Other utilities 7.5 6.7
Industrials 7.5 8.7
Real estate 27.3 10.3
Transportation 5.3 6.7
Other industries 23.0 55.3
Year (% of total)
2020 31.4 10.6
2019 29.5 10.2
2018 16.5 9.2
2017 8.4 9.2
2016 5.0 8.1
2015 7.1 6.9
2014 2.2 8.2
2009-2013 0.0 37.5
Our main regression model is 𝐴𝐴𝐵𝑆𝑅=𝛽0
+𝛽1Green bond +𝛽2New issue size +𝛽3Maturity difference +𝛽4Coupon difference +𝛽5Seniority difference +𝛽6Existing maturity +𝛽7Existing bond rating +𝛽8Long-term leverage ratio +𝛽9Return on assets
+𝛽10Total assets
+𝛽11Market capitalization +
∑16
𝑖=12
𝛽𝑖Industry𝑖
+
∑26
𝑖=17
𝛽𝑖Year𝑖
+
∑29
𝑖=27
𝛽𝑖Region𝑖.
(1)
The first control variable, New issue sizeis included to control for the size of the announced bond. While the literature provides inconclusive evidence regarding the general effect of bond issuances on outstanding debt (Maul and Schiereck,2018;Renneboog and Szilagyi,2008), more recent studies document a significantly negative effect (Chen and Stock,2018;Maul and Schiereck,2018). Larger bond issuances should essentially be equivalent to multiple smaller ones, especially given that we add together issue sizes of underlying bonds in the construction of composite bonds.
Therefore, we expect an increase in the issue size of bond offerings to have a negative effect on abnormal returns.
The next three variables compare the characteristics of the announced bond to those of the outstanding bonds.Chen and Stock(2018) find that these have a significant impact on the abnormal returns of outstanding bonds. We expect Maturity differenceto have a positive effect since debt with shorter maturity is indirectly senior to longer debt. Repaying shorter debt would reduce the company’s ability to repay later debt. A positiveCoupon difference, on the contrary, would confirm to existing bondholders that their bonds’ coupon is below current market levels, sending a negative signal. We also expectSeniority differenceto have a negative effect on outstanding bonds as new bonds would be senior to existing bonds under a positive value of this variable.
The next two variables model characteristics of the is- suer’s outstanding bonds (seeChen and Stock,2018;Eber- hart et al., 2008; Ellul et al.,2011; Maul and Schiereck, 2018). When considering maturity,Li et al.(2013) argue that outstanding bond maturity exacerbates the impact of credit events on bond evaluations, both for positive and negative
events. Since recent studies document that new bond an- nouncements generally have a negative effect on outstanding bonds (Chen and Stock,2018;Maul and Schiereck,2018), we hypothesize a negative sign forExisting maturity. As for Existing bond rating, it is standard practice to control for bond default risk in empirical bond studies, and both ratings and leverage ratios can be used (Shi and Zhang,2008). Our prediction is that higher rated outstanding bonds experience less of a negative effect from new bond announcements since default is less likely.
The next four variables model characteristics of the is- suer itself. Together,Long-term leverage ratio,Return on assets,Total assets, andMarket capitalizationrepresent basic categories of firm-level attributes, specifically, leverage, prof- itability, firm size, and firm value (seeEberhart et al.,2008;
Massa and Zhang,2021;Maul and Schiereck,2018;Ongena et al.,2014). As another proxy for risk, we hypothesize that leverage ratios have the opposite effect on abnormal returns of bond ratings, that is, we predict a negative coefficient for Long-term leverage ratio. Since high levels ofReturn on assetsmight indicate more productive use of assets, issuing more debt to finance this productivity should be seen as a positive sign by existing bondholders. ForMarket capital- izationandTotal assets, an increase (decrease) in the former (latter) variable is indirectly an increase in a variant of the issuer’s market-to-book ratio. This can be interpreted both as an indicator of growth prospects and as a proxy for risk.
The impact of these variables is therefore ambiguous.
Finally, we include dummy variables to control for regional and industrial fixed effects in abnormal returns and event years to control for economic downturns or booms (see Plunus et al.,2012;Shi and Zhang,2008). The inclusion of industries and regions also serve a second purpose, since by interacting these variables with the green bond dummy variable, we can test for regional and industrial heterogeneity in the green halo debt effect. The greenium is sometimes only found for bonds denominated in Euro and U.S. dollars, while also exhibiting industrial heterogeneity (Zerbib,2019).
Therefore, we test whether similar heterogeneities are present in the green halo debt effect.
We run several regression analyses based on the basic model presented in Equation (1). These are further explained in Section5. To alleviate a potential problem of clustering in our bond data set, we use ordinary least squares (OLS) with clustered standard errors, clustering observations based on the issuer. For some of our analyses, we use a stricter definition of green bonds by requiring them to have externally reviewed green bond frameworks. As a potentially stronger signal of climate commitment, we expect this to exacerbate any result found with the green bond dummy variable, corresponding to what is typically found in studies of the greenium (Baker et al.,2018;Flammer,2021;Hyun et al., 2020;Li et al.,2020).
5. Results and discussion
5.1. Abnormal returnsFigure1shows the distributions of abnormal returns for the bond data set. All bonds for which abnormal returns are not computable are removed. Since OLS regressions are sensitive to outliers, we also remove the bonds with the 0.1% most extreme abnormal return values. This reduces the size of the bond data set from 7,614 to 5,901 bonds.
All distributions appear unimodal, centered around a mean close to zero. The plots also suggest that the distribution of externally reviewed bonds is shifted slightly to the right compared to non-reviewed bonds.
Table2shows measures of centrality for abnormal return distributions for several event window sizes. We also perform two-tailed tests of the null hypothesis that means and medians are zero. For vanilla bonds, most mean and median AABSR values are positive and significantly different from zero. Conclusively, the general effect of bond issuance on outstanding bonds seems to be slightly positive, in line with Akhigbe et al.(1997) andKolodny and Suhler(1988), but contrary to more recent studies (Chen and Stock,2018;Maul and Schiereck,2018). For CAAR values, we cannot reject the null hypothesis that the mean is equal to zero. Median values, however, are significantly negative, although very small in absolute value.
For green bonds, and in particular, externally reviewed bonds, abnormal return values are typically larger than for their counterparts. In particular, AABSR means and median values are significantly positive at the 1% level for all windows considered. This is in line with the hypothesis of an existent green halo debt effect.
Figure2illustrates CAAR values for all bonds, plotted along the time dimension. For each day ranging from ten days before to ten days after each bond’s announcement date, we compute average CAAR values over all bonds. Over the [-10,10]-window, green bonds, in particular, externally reviewed bonds, exhibit a substantially higher CAAR on average than their counterparts. The largest change in CAAR values is from day -1 to day 0, suggesting the presence of a green halo debt effect.
5.2. Regression analyses
Table3shows the results from five regression models which we run to investigate the green halo debt effect. All models are based Equation (1). Details about robustness tests are found in AppendixC. Across all regression models, the maximum correlation coefficient between any pair of variables is 0.55, suggesting that multicollinearity is not present in our data set.
Regression A is run to detect an overall green halo debt effect. The model shows that all else equal, green labeling increases the AABSR values of about 0.19 on average, significant at the 1% level. This result is confirmed by robustness tests, with coefficients ranging between 0.16 and 0.25 for different model specifications. Conclusively, there is evidence that the green halo debt effect exists in general.
In a robustness test, we run a regression analysis where separate dummy variables are included to distinguish between externally reviewed and non-reviewed green bonds. This allows us to investigate any green halo debt effect exhibited by non-reviewed green bonds separately from externally reviewed ones. The results do not support a green halo debt effect for non-reviewed green bonds, providing no evidence that non-reviewed green bonds have any different effect on outstanding bonds than vanilla ones. Therefore, in the rest of the regression models,External reviewis always used in place ofGreen bond. This essentially constrains the definition of green bonds to include only those green bonds that have received external reviews from third parties.
In Regression B, we substitute the green bond dummy variable with the external review dummy variable. As can be seen, this strengthens the results compared to Regression A, quantifying the green halo debt effect at a 0.30 increase in AABSR values on average, significant at the 1% level. This result is confirmed by robustness tests, with coefficients rang- ing between 0.25 and 0.35 for different model specifications.
Conclusively, we find that the green halo debt effect is more pronounced for externally reviewed green bonds. This is consistent with a signaling argument of green bonds. Since externally reviewed green bonds are more costly to issue, they represent a more credible signal of climate commitment (Flammer,2021). The result is also in line with the findings of some greenium studies, where greeniums are more pro- nounced for externally reviewed green bonds (Baker et al., 2018;Flammer,2021;Hyun et al.,2020;Li et al.,2020).
In Regression C, we investigate the industrial heterogene- ity of the green halo debt effect by interacting the external review dummy variable with the six industries included in our study. Despite the fact that dividing externally reviewed green bonds into industries reduces testing power, all indus- tries apart fromTransportationexhibit a significantly positive effect. The coefficients vary, indicating that the green halo debt effect exhibits industrial heterogeneity. Taking robust- ness tests into account, it seems that a green halo debt effect is more pronounced withinElectric utilitiesandOther indus- tries. Further, we comment on these industries in particular.
WithinElectric utilities, we find an increase in AABSR values due to externally reviewed green labeling of 0.39 on average, significant at the 10% level. This result is confirmed by robustness tests, with coefficients ranging between 0.25 and 0.46 for different model specifications. Electric utilities represent an industry facing substantial transitional and physical climate risks. At the same time, the industry plays an important role in reducing carbon emissions of other industries (Fryer et al.,2017). This might explain why a green halo debt effect is more pronounced in this industry.
Additionally, most of the externally reviewed green bonds within electric utilities in our data set are issued in Europe and North America. These are regions in which we find a stronger green halo debt effect in general, as demonstrated by Regression D.
Other industries represents a diverse group of firms, consisting of issuers within Basic materials, Consumer
(a) (b)
(c) (d)
Figure 1: Distributional plots showing abnormal returns for the green and vanilla bonds, and externally reviewed and non-reviewed bonds in the bond data set. All bonds for which abnormal returns are not computable are excluded from the data set. The 0.1%
most extreme abnormal returns are also removed. The abnormal return distributions extend beyond the values depicted by the distributional plots, but the tails are not plotted. All distributions are scaled to have an area of one. Panelashows the distribution of AABSR{-4,4} values for green and vanilla bonds. Panelbshows the distribution of CAAR[-4,4] values for green and vanilla bonds. Panelcshows the distribution of AABSR{-4,4} values for externally reviewed and non-reviewed bonds. Paneldshows the distribution of CAAR[-4,4] values for externally reviewed and non-reviewed bonds.
Table 2: Abnormal return centrality measures for the green and vanilla bonds, and externally reviewed and non-reviewed bonds in the bond data set. We consider AABSR and CAAR distributions for several event window sizes. N represents the number of bonds for each combination of distribution and event window size. For all abnormal return distributions, we report the mean and median values. Mean values are tested with a null hypothesis of being zero using a two-tailed t-test. Median values are tested with a null hypothesis of being zero using a two-tailed Wilcoxon signed-rank test. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.
Green bonds Vanilla bonds With external review Without external review
Measure Mean Median N Mean Median N Mean Median N Mean Median N
AABSR
{-5,5} 0.27*** 0.16*** 216 0.05*** 0.02*** 5,685 0.39*** 0.21*** 160 0.05*** 0.02*** 5,741 {-4,4} 0.25*** 0.15*** 216 0.05*** 0.02*** 5,685 0.35*** 0.18*** 160 0.05*** 0.02*** 5,741 {-3,3} 0.23*** 0.13*** 216 0.04*** 0.01** 5,681 0.33*** 0.16*** 160 0.04*** 0.01** 5,737
{-2,2} 0.23*** 0.08*** 216 0.04*** 0.00 5,671 0.33*** 0,12*** 160 0,04*** 0.00 5,727
{-1,1} 0.26*** 0.15*** 213 0.03** -0.01 5,613 0.36*** 0.19*** 158 0.03** 0.00 5,668
CAAR
[-5,5] 0.07% 0.03% 216 -0.02% -0,01%** 5,685 0.12%** 0.07%*** 160 -0.02% -0.01%** 5,741 [-4,4] 0.07% 0.04%* 216 -0.01% -0.01%* 5,685 0.12%** 0.06%** 160 -0.01% -0.01%* 5,741
[-3,3] 0.00% 0.01% 216 -0.01% -0.01%** 5,685 0.05% 0.05% 160 -0.01% -0.01%** 5,741
[-2,2] 0.01% 0.03% 216 -0.01% -0.01%*** 5,685 0.04% 0.04%* 160 -0.01% -0.01%*** 5,741 [-1,1] 0.05%** 0.02% 216 -0.01% -0.01%*** 5,683 0.07%** 0.04%* 160 -0.01% -0.01%*** 5,739 [0,0] 0.05%** 0.01%* 215 -0.01%*** -0.01%*** 5,677 0.06%** 0.02%* 160 -0.01%*** -0.01%*** 5,732
(a)
(b)
Figure 2: Line plots of average CAAR values for all green and vanilla bonds, and externally reviewed and non-reviewed bonds in the bond data set. The horizontal axes represent days around the announcement date (day 0), spanning from ten days before to ten days after the announcement date. The vertical axes represent CAAR values. Panelashows CAAR values for green and vanilla bonds. Panelbshows CAAR values for externally reviewed and non-reviewed bonds.
cyclicals,Consumer non-cyclicals,Energy,Healthcare, and Technology. Here, we find increases in AABSR values due to externally reviewed green labeling of 0.32 on average, significant at the 10% level. This result is confirmed by robustness tests, with coefficients ranging between 0.21 and 0.37 for different model specifications. The majority of bonds inOther industriesin our sample are issued by firms providing consumer products and services. One explanation for a green halo debt effect within this industry is that issuers might be facing customers with strong green preferences (Haller et al.,2020;Nielsen,2014). Relatedly, the issuers might also promote their green ambitions to a greater extent than what business-to-business companies do. The reason why this affects debt investor preferences could be expectations of greener companies performing better, asBarton et al.(2018) report that 62% of consumers choose greener brands over their competitors. The result is consistent with the findings ofKapraun and Scheins(2020),
who document a positive relation between perceived issuer greenness and greeniums.
In Regression D, we investigate the regional heterogeneity of the green halo debt effect by interacting the external review dummy variable with the four regions included in our study. Of these, only European and North American green bond announcements have significantly positive effects on issuers’ outstanding bonds. This indicates that the green halo debt effect exhibits substantial regional heterogeneity, and is only present in western countries. In Europe, externally reviewed green labeling increases AABSR values by 0.35 on average, significant at the 1% level. This result is confirmed by robustness tests, with coefficients ranging between 0.30 and 0.44 for different model specifications. In North America, externally reviewed green labeling increases AABSR values by 0.46 on average, significant at the 1% level. This result is confirmed by robustness tests, with coefficients ranging between 0.43 and 0.52 for different model specifications. The regional heterogeneity observed might be explained by the fact that green bond markets in western countries are more mature than in the rest of the world (CBI,2020). Furthermore, bond investors might have stronger green preferences in the western world than elsewhere4 (Morningstar, 2021).
Our results are related to the finding ofZerbib(2019) that greeniums are only significant for green bonds denominated in Euros or U.S. dollars.
In Regression E, we run the same model as in Regression B, but instead use CAAR values as the dependent variable.
The model shows that all else equal, externally reviewed green labeling increases the CAAR values of about 0.0014 on average, significant at the 5% level. Since bond market event studies based on CAAR rather than AABSR exhibit less statistical power (Ederington et al.,2015), it is not unexpected that the p-values increase with Regression E compared to Regression B. Still, most robustness tests confirm a significant effect, with coefficients ranging between 0.0007 and 0.0015.
Since CAAR values are unstandardized, the coefficient can be interpreted directly as a positive price shift of outstanding debt of between 7 and 15 bps on average. Following the procedure explained in AppendixD, this translates into an average yield spread reduction of between 2 and 3 bps for the bonds in the bond data set. Distinguishing between investment grade and high-yield bonds gives an average yield spread reduction of 2-3 and 8-11 bps, respectively.
In terms of control variables, Regression A-E exhibit very similar results regarding the significance and sign of control variable coefficients, thereby demonstrating robustness. The same is true in terms of coefficient values, keeping in mind that Regression A-D cannot be directly compared to Regression E in this regard since the dependent variable is different. Conforming to our earlier predictions, larger issue sizes and higher existing maturities of outstanding bonds exacerbate a generally negative effect on abnormal returns. Furthermore, increases (decreases) in total assets (market capitalization) have a negative effect on abnormal returns. WhereasMarket capitalizationis significant only for Regression E,Total assetsis significant in all models.
Table 3: Results from the five regression models used to investigate the existence of the green halo debt effect. We run four regression models on AABSR{-4,4} as the dependent variable, and one model on CAAR[-4,4] as the dependent variable. Abnormal returns are explained by green bond labels and external reviews in addition to bond-related, issuer-specific, and macroeconomic control variables. See AppendixA for the definition of each variable. Uninteracted control variables representing fixed year, regional, and industrial effects are included in all regressions, but the results are not displayed. One dummy variable is included for every year from 2014 through 2020, while the years 2009 through 2013 act as the reference value. One dummy variable is included for each region in the data set, apart fromAsia-Pacific, which acts as the reference value. One dummy variable is included for each industry in the data set, apart fromTransportation, which acts as the reference value. All regression models are run on a restricted data set of bonds where we remove the bonds for which abnormal returns are not computable, and the most extreme abnormal return values are removed. p-values are presented in parentheses. *, ** and *** denote significance at the 10%, 5% and 1% level, respectively.
AABSR{-4,4} CAAR[-4,4]
Variable A B C D E
Intercept 1.6130*** 1.6079*** 1.6045*** 1.6251*** 0.0119***
(0.000) (0.000) (0.000) (0.000) (0.000)
Green bond 0.1895***
(0.008)
External review 0.3004*** 0.0014**
(0.001) (0.037)
Electric utilities x External review 0.3936*
(0.074)
Other utilities x External review 0.2450*
(0.056)
Industrials x External review 0.2403*
(0.076)
Real estate x External review 0.2675*
(0.098)
Transportation x External review 0.3197
(0.222)
Other industries x External review 0.3192*
(0.082)
Europe x External review 0.3495***
(0.006)
North America x External review 0.4559***
(0.000)
Asia-Pacific x External review 0.2050
(0.261)
Latin America x External review 0.1800
(0.257)
New issue size -0.0402*** -0.0400*** -0.0401*** -0.0402*** -0.0003***
(0.000) (0.000) (0.000) (0.000) (0.001)
Maturity difference -0.0293 -0.0287 -0.0292 -0.0286 -0.0003
(0.134) (0.140) (0.134) (0.142) (0.139)
Coupon difference -0.0041 -0.0040 -0.0038 -0.0040 0.0000
(0.594) (0.607) (0.622) (0.605) (0.909)
Seniority difference -0.0001 0.0001 -0.0001 0.0005 0.0001
(0.995) (0.995) (0.988) (0.956) (0.377)
Existing maturity -0.0600** -0.0589** -0.0594** -0.0575* -0.0004
(0.040) (0.044) (0.043) (0.050) (0.107)
Existing bond rating -0.0048 -0.0020 -0.0019 -0.0024 0.0001
(0.729) (0.883) (0.892) (0.864) (0.608)
Total assets -0.0426*** -0.0419*** -0.0419*** -0.0413*** -0.0005***
(0.004) (0.004) (0.004) (0.005) (0.002)
Market capitalization 0.0108 0.0096 0.0098 0.0087 0.0002***
(0.221) (0.273) (0.265) (0.316) (0.006)
Return on assets 0.0012 0.0009 0.0009 0.0010 -0.0001
(0.753) (0.800) (0.806) (0.793) (0.373)
Long-term leverage ratio 0.0841 0.0863 0.0861 0.0842 0.0004
(0.391) (0.375) (0.379) (0.389) (0.710)
Fixed year effects Yes Yes Yes Yes Yes
Fixed industry effects Yes Yes Yes Yes Yes
Fixed region effects Yes Yes Yes Yes Yes
Observations 5,901 5,901 5,901 5,901 5,901
R2 0.026 0.027 0.027 0.028 0.018
Regardless, an interpretation is that a corresponding decrease in market-to-book ratios is seen as a decrease in future growth prospects, leading to a negative effect on abnormal returns of outstanding bonds. Other than this, no other control variable seems to exhibit significant effect on abnormal returns of outstanding bonds. All findings related to control variables exhibit robustness.
5.3. Event window considerations
In event studies, a common issue is the potential inclusion of confounding events in the event windows. In our study, this is especially relevant during issuers’ first green bond issuance.
For instance, issuers could partake in roadshows, create a green bond framework, get this externally reviewed, or get an ESG rating if missing one8. Although such events are more likely to occur before the bond issuance process begins8, outside our chosen nine-day event window, we investigate this issue further. Specifically, we perform a robustness test to investigate whether the green halo debt effect persists for seasoned green bond issuances. Our results confirm that this is indeed the case, suggesting that confounding events are unlikely to be an issue in our study.
A related question is whether the chosen event windows are wide enough to fully capture the impact of the events in question. In our study, we concentrate on the short-term price shifts in outstanding bonds. According to the efficient market hypothesis, the green halo debt effect should be a quick price shift, since investors respond quickly to new information (Fama,1970;Fama et al.,1969). However, as seen in Figure2b, the CAAR continues to increase after the announcement date, reaching its highest value on day ten. It therefore appears that the investor reaction to the green bond announcement is not instantaneous, suggesting low market liquidity, slow spread of information, or investors needing time to re-evaluate the value of the issuer’s outstanding bonds. Furthermore, it appears that there are positive average abnormal returns before the announcement date, suggesting information leakage. These features of the observed price shifts might be due to bond markets exhibiting less informational efficiency than equity markets (Downing et al.,2009;Kwan,1996).
The AABSR methodology used in this study is designed to capture short price shifts. This is useful for statistical power, but less so for measuring the effect’s size. The potential consequence is that we are only able to partially capture the green halo debt effect. As there might be information leakage and slow bond market reactions, future research should consider the green halo debt effect over wider event windows to better determine its size.
5.4. Economic impact
Having shown the existence of the green halo debt effect on outstanding debt, it is yet to be determined whether this effect could be economically beneficial for the issuer. The
8For more about the green bond issuance process, see Deloitte, "Green Bonds Issuance and Support Offering," 2019 and Sustainalytics, "Steps to issue a Green Bond," February 6, 2019.
benefit to the existing bondholders is trivial since their assets appreciate. For issuers, it is more complicated since they would need to exploit the price shift caused by the effect in future bond issuances. Doing so is contingent on two factors.
The first factor is whether the effect translates to the primary market, and the second is whether the effect persists over time.
This paper has concentrated on the green halo debt effect in the secondary market. However, the effect could transfer to primary markets. This is intuitive, as outstanding bonds serve as a yield rate floor for new bond issuances. Otherwise, investors would prefer to invest in secondary markets (Frid- son and Gao,1996). In practice, new bonds are usually issued at a slight discount to the secondary market prices, so-called underpricing (Cai et al.,2007;Ederington,1974;Lindvall, 1977;Sorensen,1982).Cai et al.(2007) study the determi- nants of underpricing and find that asymmetric information is the main determinant. Other determinants, such as whether issuers are investment grade or exchange-listed, although sig- nificant, are essentially fixed in the short run. Following the signaling hypothesis ofFlammer(2021), the green halo debt effect occurs because the issuer signals climate commitment, reducing asymmetric information. Therefore, underpricing could decrease following green bond announcement, poten- tially reducing yields for future bond issuances.
This study has investigated the abnormal returns of out- standing bonds over a short period around bond announce- ment events. Therefore, we cannot establish whether the detected green halo debt effect is a persistent shift in pricing or a temporary increase from the gained attention towards the issuer by the green bond announcement. However, based on the efficient market hypothesis, the price increase is due to new information available to investors, which they rationally respond to (Fama,1970;Fama et al.,1969). For the effect to be only temporary, a reversal of the green halo debt effect due to new information would have to occur.
Combining the existence of the green halo debt effect on outstanding bonds with the ideas of decreased underpricing of new issuances and persistence of the effect, we hypothesize that the green halo debt effect applies to future bond issuances.
However, Schiereck et al. (2019) hypothesize a potential problem with the effect’s transferability to future bond issuances. Their idea is that if green labeling becomes the norm of an issuer, future vanilla bonds may be perceived as brown bondswith a correspondingbrown halo debt effect.
Conclusively, this makes for an interesting future research opportunity.
Assuming that the green halo debt effect fully transfers to the primary market, we convert the coefficient ofExternal reviewof Regression E in Table3to a yield spread reduction (see AppendixD) to measure the potential economic impact of our results. The approximate conversion gives a yield spread reduction of 2-3 bps. The measured abnormal return is a relative change in price. Therefore, the yield spread reduction is greater for issuers with higher yield rates.
Differing between investment grade and high-yield bonds, we get 2-3 and 8-11 bps, respectively. Although the effect is
lower than what many greenium studies report (Baker et al., 2018; Ehlers and Packer,2020;Gianfrate and Peri,2019;
Hyun et al.,2020), the effect is issuer-wide. Therefore, each basis point would have a greater economic impact for the issuer than those of a greenium. However, although being issuer-wide, the effect only applies to future bond issuances.
Determining whether this yield spread reduction is financially significant for the issuer would require further study as this depends on issuer attributes such as profitability, cost of debt, and leverage ratio.
Even if the green halo debt effect is financially significant, it is unlikely that it would provide additionality, that is, resulting in more green projects than what otherwise would have been undertaken. It is projects that determine financing and not the opposite (Maltais and Nykvist,2020). Still, the green halo debt effect along with the green halo effect on equity (Flammer,2021), broadened investor base5, increased bond liquidity (Febi et al.,2018), and the greenium (Zerbib, 2019) could explain why issuers prefer issuing green bonds over vanilla ones to fund green projects.
6. Conclusion
The green halo debt effect is an effect where the outstand- ing bonds of a bond issuer experience a significantly more positive price shift on average following the announcement of a green bond than a vanilla bond. In this paper, we provide evidence of the green halo debt effect, controlling for bond and issuer characteristics and macroeconomic effects.
Throughout this paper, we have expanded the discussion on financial incentives to issue green bonds beyond the greenium by introducing the green halo debt effect. Doing so, we have shown that issuers’ pricing advantages need not come at the expense of investors. The green halo debt effect suggests a broader impact on the issuer’s entire debt portfolio compared to the greenium, which only affects the green bond issued.
This paper is also the first to study the green halo effect on debt. The results compliment the research on green halo effects on equity (Baulkaran,2019;Flammer,2021;Tang and Zhang,2020) by confirming a similar effect on debt. This way, we both confirm and expand the signaling hypothesis of green bonds (Flammer,2021). WhereFlammer(2021) concludes that green bonds are issued to signal climate commitment to investors and not for cost of capital benefits, we show that the issuance also sends a signal to debt investors, possibly reducing the yield of future bond issuances.
In what follows, we discuss suggestions for further work.
With the rapid growth of the green bond market, future studies will have more data available to confidently detect the existence and nuances of the green halo debt effect. This being said, as the green bond market matures and green bonds’ market share increases, the effect may disappear as the climate commitment signal could become less salient.
This could lead to vanilla bonds being perceived asbrown bonds(Schiereck et al.,2019), calling for a study on potential negative brown externalitiesof vanilla bond issuances.
This paper also calls for studying whether the green halo debt effect translates to price advantages at future issuances.
As we discussed in the previous section, answering this ques- tion is important to determine whether the green halo debt effect is financially beneficial for the issuer. This paper ad- dresses the effect on outstanding bonds, since this enables the use of established event study methodology. However, to study the effect in the primary market, new methodol- ogy would likely need to be developed. An intuitive ap- proach would be to investigate yield spreads of the first (green and vanilla) bond issuance following green bond issuance.
Schiereck et al.(2019) also acknowledge this gap in the litera- ture, and provide a hypothesis opposite to ours. Investigating these opposing hypotheses provides an interesting research opportunity and an important contribution to the green bond issuance incentives discussion.
Appendices
A. Description of variables
Table4explains all the regression variables and how they are computed.
B. Computation of abnormal returns
This appendix explains how to compute abnormal re- turns. Methodological choices can be found in Section4. We use two different methods of computing abnormal returns, average abnormal standardized returns (𝐴𝐴𝐵𝑆𝑅) and cu- mulative average abnormal returns (𝐶𝐴𝐴𝑅). We will first explain how to compute the𝐴𝐴𝐵𝑆𝑅for a single event, and then explain the𝐶𝐴𝐴𝑅. The𝐴𝐴𝐵𝑆𝑅computations are first made without event window broadening to simplify notation.
For each bond announcement event, we compute an (𝐴𝐴𝐵𝑆𝑅) value based on outstanding bonds. To compute the𝐴𝐴𝐵𝑆𝑅, we average the abnormal standardized returns (𝐴𝐵𝑆𝑅) for each of the issuer’s outstanding bonds as
𝐴𝐴𝐵𝑆𝑅= 1
|𝑂𝐵|
∑
𝑜∈𝑂𝐵
𝐴𝐵𝑆𝑅𝑜, (2)
where 𝑂𝐵 is the set of outstanding bonds at the time of announcement and𝐴𝐵𝑆𝑅𝑜is the𝐴𝐵𝑆𝑅of bond𝑜∈𝑂𝐵.
If𝐴𝐵𝑆𝑅𝑜is undefined, bond𝑜is excluded from𝑂𝐵. If𝑂𝐵 is empty, the𝐴𝐴𝐵𝑆𝑅is undefined.
The𝐴𝐵𝑆𝑅of an outstanding bond is computed by sub- tracting a matching standardized benchmark return (𝑆𝐵𝑀) from the standardized return of the outstanding bond (𝑆𝑅𝑅) as
𝐴𝐵𝑆𝑅=𝑆𝑅𝑅−𝑆𝐵𝑀 . (3)
If either𝑆𝑅𝑅or𝑆𝐵𝑀is undefined,𝐴𝐵𝑆𝑅is undefined.
To compute the𝑆𝑅𝑅value for an outstanding bond, we divide its raw return (𝑅) by its estimated standard deviation (𝜎) at bond announcement as
𝑆𝑅𝑅= 𝑅
𝜎. (4)
