The pricing of green bonds: external reviews and the shades of green

Dorfleitner, Gregor; Utz, Sebastian; Zhang, Rongxin Article — Published Version The pricing of green bonds: external reviews and the shades of green Review of Managerial Science Provided in Cooperation with: Springer Nature Suggested Citation: Dorfleitner, Gregor; Utz, Sebastian; Zhang, Rongxin (2021) : The pricing of green bonds: external reviews and the shades of green, Review of Managerial Science, ISSN 1863-6691, Springer, Berlin, Heidelberg, Vol. 16, Iss. 3, pp. 797-834, https://doi.org/10.1007/s11846-021-00458-9 This Version is available at: https://hdl.handle.net/10419/286892 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Review of Managerial Science (2022) 16:797–834 https://doi.org/10.1007/s11846-021-00458-9 1 3 ORIGINAL PAPER The pricing ofgreen bonds: external reviews andtheshades ofgreen GregorDorfleitner1,2 · SebastianUtz3· RongxinZhang1 Received: 19 October 2020 / Accepted: 26 February 2021 / Published online: 1 April 2021 © The Author(s) 2021 Abstract We investigate the asset pricing implications of the greenness of bonds. To estimate a green-pricing effect, we determine the ‘green bond premium’ as the difference between the yields of matched conventional and green-labeled bonds. On a crosssectional average, green bonds experience a statistically significant positive premium. This premium increases with external greenness evaluations, i.e., investors accept premiums of up to 5 basis points for bonds with a substantial environmental agenda. This external validation effect, which is strongest for bonds that are rated dark-green, may offset not incurring information costs, as this effect decreases with increasing age of bonds. Keywords Green bond premium· External review· Second-party opinion· Shade of green· Climate finance· Impact investing Jel Classification 91B76· 91B16· 62P20 1 Introduction In recent finance literature, there has been a lively debate on the asset pricing implications of sustainable and particularly green investment opportunities (Bolton and Kacperczyk 2021; Cheema-Fox etal. 2019). While existing studies focus mainly on equity, green bonds are also an important innovative financing tool for addressing environmental and climate challenges (Ehlers and Packer 2017). In the last decade, green bonds have become increasingly appealing to investors (Krueger etal. 2020). Moreover, since the European Investment Bank issued the first green bond in 2007, the * Gregor Dorfleitner [email protected] 1 Department ofFinance, University ofRegensburg, 93040Regensburg, Germany 2 CERMi (Centre forEuropean Research inMicrofinance), Brussels,Mons, Belgium 3 School ofFinance, University ofSt. Gallen, 9000St.Gallen, Switzerland 798 G.Dorfleitner et al. 1 3 green bond market has experienced exponential development. According to the Climate Bond Initiative (2020), the worldwide annual issue volume has grown from less than 40 billion USD in 2014 to over 160 billion USD in 2018 and 257.7 billion USD in 2019 worldwide. This rapid growth which indicates an increasing amount of funds to finance climate change adaptation and mitigation, has also attracted the attention of academics. Existing studies explore whether issuers of such green securities enjoy lower costs of financing, and at the same time, whether investors request lower returns. In this study, we exploit the green bond market as a laboratory for testing the asset pricing implications of investment vehicles dedicated to actions related to climate change. We measure the asset pricing implications of bond greenness in terms of the so-called ‘green bond premium’, i.e., the difference between the yields of matched green and conventional bonds. In particular, we systematically examine the existence of the green bond premium and analyze how it is influenced by external evaluation for a bonds greenness. We extend the methodological frameworks of earlier related studies (Hachenberg and Schiereck 2018; Nanayakkara and Colombage 2019; Zerbib 2019) by a stricter matching approach, a more precise measurement of the green bond premium, and a larger sample to analyze the green bond premium as the yield difference between green bonds and synthetic conventional bonds. The latter bonds are created by matching a pair of conventional bonds to each green bond and adjusting the maturity by interpolation. Our main finding is that investors reward green bonds that are approved by external reviews, documenting the bond’s serious and genuine green purposes, with a premium in the sense of lower yields and higher bond prices. The existence of such a green bond premium is in contrast to the modern portfolio theory, which makes the assumptions of rational investors, efficient markets, and expected returns as a function of risk. Nevertheless, asset pricing literature has shown that, additionally to these assumptions, several anomalies predict asset prices (Harvey etal. 2016). More specifically, behavioral finance literature generally assumes that investors are imperfect and subject to many emotional biases, i.e., behavioral finance differs from traditional finance in that it focuses on how investors actually behave, rather than theorizing how they should behave. In our particular case, green bonds cater for both the traditional financial and green objectives of bond investors. Therefore, green impact investors may achieve utility from the green investment outcome besides the utility gained from financial performance. Thus, following this utility paradigm, which goes one step beyond the irrationality-based behavioral finance perspective, green bonds could be priced higher than comparable conventional bonds, as the non-financial utility component may compensate for a lower financial return for green impact investors. To identify whether this pattern applies on financial markets and how the level of greenness impacts the green bond premium, is the research gap that needs to be filled. Some evidence on whether such a premium really prevails amongst investors exists (e.g., Baker etal. 2019; Bachelet etal. 2019; Zerbib 2019), but no consensus has been reached, and the findings so far paint an unclear picture. While there is some evidence supporting a positive green bond premium and the appreciation of the greenness of these instruments (Baker et al. 2019), other studies elicit no green bond premium or even a negative one (Bachelet etal. 2019; Climate Bond Initiative 2019b). 799 1 3 The pricing ofgreen bonds: external reviews andtheshades… Our paper develops a theoretical framework for a ‘greenness bias’ in expectations of green bond investors and conducts empirical tests based on a sample of 250 matched bond triplets in the period from 2011 to 2020 containing more than 90,000 daily observations. To determine the matched bond triplets we applied a rigorous matching process. Moreover, we build a comprehensive dataset by consolidating various sources of information on green bonds and their comparable counterparts. To investigate the pricing mechanism of green bonds, we run hybrid regressions and focus on different types of external review reports and their evaluation results, to explain the variations in the distribution of green bond premiums. Furthermore, we control liquidity difference via a hybrid model, in order to extract the real green bond premium. Our results show that, on average, green bonds enjoy an expected positive premium (approximately 1 BP) over comparable conventional bonds. Indeed, some green bonds do have an evidently higher premium than others. Reports from independent external reviewers are a main driver for investors to pay a significant green bond premium. However, the type of external review is crucial. While there is no evidence that external reviews such as a certification assigned by the Climate Bond Initiative (CBI) and a green rating from traditional credit rating agencies have a positive influence on the green bond premium, green bonds with a second-party opinion and a verification enjoy significantly lower yields, i.e., are traded at a positive premium (3–4 BP). Particularly second-party opinions asserting a ‘dark green’ or ‘medium green’ shade tend to be associated with a positive premium (5 or 4 BP, respectively). This finding adds to the discussion of whether investors are willing to pay more for certified green and sustainable investments (see e.g., Gutsche and Ziegler 2019). This finding also adds to the debate on whether green bonds could be used for financing regular non-green projects (Flammer 2020) and as a tool for green-wash- ing (Walker and Wan 2012; Nyilasy etal. 2014). If this were the case, the green bond market would lose much of its credibility, and investors might start to ignore the green label. Therefore, preserving the integrity and credibility of green bonds is at the core of building a healthy green bond market. To mitigate the risk of greenwashing, the International Capital Market Association (ICMA) recommends issuers to appoint an independent external reviewer to confirm the alignment of their green bonds with the ‘Green Bond Principles’ (GBPs). Consequently, external reviews are the main approach to enhancing the integrity and credibility of the green bond market (Shishlov etal. 2016). With a growing market, the role of independent external reviewers is becoming even more prominent. However, to the best of our knowledge, very few studies present empirical indications that external reviews impact investor decisions and thereby the pricing of green bonds (Baker etal. 2019; Bachelet etal. 2019; Larcker and Watts 2020). The question of whether different types of external reviews create value for investors has not yet been answered. Moreover, even though an increasing number of external reviewers explicitly evaluate detailed greenness issues instead of a general assessment, it is unanswered how investors react to the external greenness assessments. Our empirical results provide evidence that investors rely on external reviews, especially second-party opinions and verifications, as a source of proven information 800 G.Dorfleitner et al. 1 3 on the greenness of green bonds. In particular, investors reward the integrity (expressed by second-party opinions) of green bond issuers with lower expected returns. We document that the effect of external validation on the green bond premium is strongest for bonds that are rated dark-green in second-party opinions, which affirms investors’ positive perception of the shade of greenness of the project. This pattern may offset not incurring information costs, as the external validation effect decreases with increasing age of bonds. Thus, a second-party opinion, especially one with a clear evaluation conclusion in terms of a shade of green, can be one channel for investors to reduce information search costs aimed at confirming the greenness of a bond, reduces the uncertainty that the respective bond is reliably and consistently green, and thus motivates investors to buy the bond at higher prices, i.e., lower expected returns. This major finding is in accordance with the recent finding that retail investors, especially socially responsible investors, have significant preferences for socially responsible equity funds with certification and transparency logos (Gutsche and Zwergel 2020). With these findings, our study makes the following two contributions. First, our study considers almost all green bonds which provides adequate information for our analysis and covers most of their yield development between 2011 and 2020. In contrast to earlier studies that focus on a specific time frame or a relatively small sample (e.g., Ehlers and Packer 2017; Nanayakkara and Colombage 2019), our setting for analyzing the green bond premium is comprehensive and minimizes potential bias that could influence the statistical estimations. Furthermore, our stringent matching process ensures that the observed yield premium between green and corresponding conventional bonds can be regarded as the ‘real’ green bond premium. Second, this is the first study to systematically examine the impact of all four different kinds of external review reports on the pricing of green bonds.1 To this end, we collect all available external review reports from major green bond databases or official issuer websites and classify them into different categories based on their formats and evaluation results. This dataset enables us to determine that serious climate action confirmed by ‘dark green’ and ‘medium green’ second-party opinions have a significant impact on the green bond premium. Nevertheless, the value of confirming climateprotection-related issues for investors declines with the age of the green bond. In terms of practical and policy implications, the reliability of external reviews in the green bond market is important to investors, and thus has implications for the cost of capital for financing climate-change adaption and mitigation. The remainder of the paper is organized as follows. In Sect.2, we discuss the importance of green credentials and the role of external review reports in the green bond market. We review the literature on the green bond premium and develop several hypotheses in Sect. 3. Section4 presents our sample and Sect. 5 1 Several studies also touch upon this question (Baker etal. 2019; Bachelet etal. 2019; Larcker and Watts 2020), but only to a minimal extent. For instance, Bachelet etal. (2019) adopt subsample analysis to examine the role of ‘third-party verification’, while Baker etal. (2019) include only a dummy variable to investigate the general influence of the CBI certification. None of these studies treats different types of external reviews separately, or examine the evaluated greenness (shade of green). 801 1 3 The pricing ofgreen bonds: external reviews andtheshades… the methodological approach. Section6 contains the empirical results and Sect.7 concludes. 2 The green bond market: institutional details 2.1 Green bond labels The development of the green bond market in the past decade demonstrates the huge demand for climate adaptation and mitigation investments. Indeed, studies show that both institutional and retail investors with a focus on sustainable investment have a strong interest in investing in green bonds (Climate Bond Initiative 2019a, b). Also, from an issuer perspective, green bonds can provide an ideal financing source for green projects. Besides fulfilling their commitment to the environment, green bond issuers may enjoy lower costs of capital in the primary market (Ehlers and Packer 2017). Originally, the proceeds from green bonds were intended to be used for green projects such as renewable energy or energy efficiency projects. As more and more issuers from various sectors entered the market, the concern arose that green bonds could be misused to finance greenwashing projects (Flammer 2020). Shishlov etal. (2016) point out that one of the two major challenges for the green bond market is to ensure its environmental integrity so as to mitigate the green-washing criticism that could threaten its survival. Investors are also aware of the greenwashing risk. According to an investor survey conducted by CBI (Climate Bond Initiative 2019a), green credentials and issuer transparency are the most important factors for green bond investors making investment decisions. However, the green bond market is generally not subject to government regulation and there are only a few voluntary rules to prevent the possibility of greenwashing. Currently, the voluntary process guidelines proposed by ICMA, called ‘Green Bond Principles’ (GBPs), are regarded as the most widely accepted standards to promote the integrity of the green bond market. To ensure that green bonds make the expected contribution to the environment, issuers can disclose an overall green bond framework which has four core components, comprising (1)the use of proceeds, (2)process for project evaluation and selection, (3)managing of the proceeds, and (4)reporting, as defined by the GBPs. Yet, the fact that issuers can label their bonds as green and draft a green bond framework on their own, results in a need to seek independent and professional external reviewers to examine the alignment with the GBPs and the greenness of the bonds. Therefore, the GBPs encourage green bond issuers to seek external reviews, besides releasing statements on the four core components. 2.2 Different external green bond reviewers According to the GBPs, there are generally four types of external review report, namely second-party opinion, verification, certification, and green rating. Each 802 G.Dorfleitner et al. 1 3 green bond can have just one or several types of external review. External reviewers are usually independent research institutions dedicated to environmental research such as the Center for International Climate Research (CICERO) and ISS-Oekom. They examine the alignment of green bonds with the GBPs, or evaluate greenness based on their specific criteria and methodologies. These external reviewers are intended to facilitate communication between investors and issuers, and thus contribute to a healthy and prosperous green bond market. Second-party opinions (SPOs) are the most popular external reviews for green bonds. Each green bond can have an SPO issued by an independent research institution such as CICERO, ISS-Oekom, and Sustainalytics. SPOs are usually detailed and comprehensive, providing a thorough analysis of the four core components of the GBPs and other related issues. An SPO released by CICERO mainly contains a description and an assessment of the issuer’s green bond framework, rules, and procedures for climate-related activities. The assessment part of the report comprises strengths, weaknesses, and pitfalls of the green bond framework. Moreover, some SPOs even provide a broad qualitative indication of the true greenness of green bonds. For instance, CICERO’s SPOs are graded into several shades, namely ‘dark green’, ‘medium green’, ‘light green’, and ‘brown’, indicating the possible environmental impact of the green bond and the robustness of the issuer’s governance structure that supports the framework. According to CICERO’s criteria,2 ‘dark green’ is only awarded to green projects and solutions that represent the best way to realize the long-term vision of a climate-resilient future. For instance, the 2015 green bond framework of the German state-owned development bank KfW obtained such a ‘dark green’ shade from CICERO, because of its clear and exclusive focus on renewable energy and robust procedures for project screening. However, in 2019 the KfW green bond framework received the ‘medium green’ shade from CICERO. Even though the proceeds are allocated to provide favorable loans for renewable projects and the construction of energy efficiency buildings to push forward the usage of fossil-free sources, the 2019 green bond framework cannot fully guarantee the exclusion of fossil fuels (see CICERO 2019) and thus regarded as somewhat less green as the 2015 one.3 Moreover, ‘light green’-shaded bonds finance mere quickfix solutions that help initiate the transition towards the long-term vision, such as improvement of energy efficiency in fossil-based activities. The so-called ‘brown’ shade (which does not occur for any bond in our sample) indicates a bond’s negative ecological impact. Besides CICERO, other SPO providers have a similar evaluation methodology in their SPO reports (see Table 1). For comparison purposes among different greenness evaluations, we convert the different schemes of greenness evaluation into one scale represented by the shades of ‘dark green’, ‘medium green’, ‘brown’, and ‘no shade’. A green bond is awarded with the shade of dark green if it exhibits an above-average positive evaluation, while the shade of medium green indicates a level of greenness that SPO provider considers to be standard in the green bond market. Bonds classified as brown shade show below-average or 2 The CICERO’s shade of methodology: https:// www. cicero. green. 3 See also CICERO (2015). We compare those two SPOs and come to this conclusion. 803 1 3 The pricing ofgreen bonds: external reviews andtheshades… negative evaluation results. When no specific shade of green is explicitly expressed in an SPO, it is classified as having no shade in this study. In that case, green bond investors must be able to draw on their own overall judgment, based on positive and negative signals implicitly delivered by SPO providers. Verification reports are, compared with SPOs, generally less lengthy and detailed,4 and issued by auditing companies such as KPMG and PwC. In verification reports, reviewers accomplish predefined tasks such as examining whether the use of proceeds is aligned with the GBPs or other related national regulatory rules. Finally, they provide a statement on the question of whether the issuer has violated any requirements defined by the GBPs or by the issuer (on voluntary basis). Therefore, it can be stated that verification reviewers evaluate green bonds more objectively, while SPO reviewers deliver subjective and comprehensive opinions on green bonds, according to their own standards. CBI certification is another type of external reviews. CBI as a well-known international organization dedicated to the development of the green bond market, offers a certification scheme which is based on scientific criteria ensuring consistency with the 2 degree Celsius warming of the Paris Agreement. CBI can award green bonds a certification through the approved verifiers.5 When assigned a CBI certification, a green bond obtains the recognition of CBI regarding its greenness. Green rating reports are issued by traditional credit rating agencies such as Moody’s and S&P. For instance, Moody’s assigns five grades of green ratings to green bonds, ranging from ‘excellent’ to ‘poor’. At first glance, green ratings are similar to SPOs with a shade of green, since they both provide a greenness assessment. However, green rating reports from credit rating agencies are regarded as a different type of external review, as they are more quantitative and focus on issuers’ environmental performance data. Moreover, they are far less frequent than SPO reports in the green bond market. Table 1 Different shade of green schemes Shade CICERO Vigeo ISS-Oekom Sustainalytics Dark green Dark green Reasonable Excellent Leader Good Outperformer Medium green Medium green Moderate Medium Average performer Light green Brown Brown Weak Poor Underperformer Laggard No shade No clear shade No clear shade No clear shade No clear shade 4 A verification report normally has only 2–3 pages. 5 A complete list of approved verifiers can be seen on the official website of CBI. 804 G.Dorfleitner et al. 1 3 3 Literature review andhypotheses development 3.1 The green bond premium andits determinants Several studies analyze the green bond premium and its determinants. Regarding the question of whether green bonds enjoy a significant premium, earlier studies show mixed empirical evidence. While some studies find evidence that green bonds enjoy a positive premium (e.g., Baker etal. 2019: 6 BP; Nanayakkara and Colombage 2019: 63 BP; Zerbib 2019: 2 BP), other studies cannot confirm its existence (Climate Bond Initiative 2019b; Larcker and Watts 2020; Flammer 2020). Bachelet etal. (2019) even find that green bonds are slightly underpriced and thus have a negative premium ( −2 BP). Differences in the identification strategy, sample selection, and observation period potentially cause diversity in the results (see the overview on the methodological spectrum of studies in Table1 of Zerbib 2019). Concerning the identification strategy, a comparison of the yield from green and conventional bonds could be conducted in the primary market (Ehlers and Packer 2017; Climate Bond Initiative 2019b) or on the secondary market by indirectly examining the impact of the green label by regressing the bond yield on a green label indicator (e.g., Baker etal. 2019; Nanayakkara and Colombage 2019). Some recent studies extract the green bond premium by adopting a matching approach (Hachenberg and Schiereck 2018; Bachelet etal. 2019; Zerbib 2019), which enables researchers more precisely to estimate the premium. Besides the inconsistent findings on the existence of a green bond premium, a few approaches analyze possible green bond determinants. Hachenberg and Schiereck (2018) and Zerbib (2019) show that basic bond features such as the credit rating and issuer type influence the green bond premium. Also, liquidity is confirmed as a major determinant of yield spreads of green bonds (Wulandari etal. 2018; Zerbib 2019). Moreover, some preliminary findings show that green credentials are important for the cost of green bonds (Baker etal. 2019; Bachelet etal. 2019; Li etal. 2019). In particular, Baker etal. (2019) investigate the pricing of 2083 U.S. municipal and 19 corporate green bonds and find that green bonds with a CBI certification have yields 26 BP lower than ordinary bonds with similar characteristic. Bachelet etal. (2019) focus on 89 matched green bonds and find that those green bonds issued by private firms with external reviews show a small premium (1 BP). Kapraun and Scheins (2019) analyze 641 green bonds and observe that certified green bonds have yields 2 BP lower than green bonds without a certification and green bonds traded on green exchanges show lower yields (7 BP) because they are required to meet some standards set by green exchanges. In contrast, Larcker and Watts (2020) examine a matched sample of 640 municipal green bonds and find that the CBI certification make no significant difference in the pricing of municipal green bonds. Nevertheless, these earlier studies have several drawbacks. For instance, some of them do not apply a strict matching process (see e.g., Baker etal. 2019; Kapraun and Scheins 2019) to gain more observations and thus may be subjected to estimation biases. Some of them focus only on a sub-sector of the green bond 811 1 3 The pricing ofgreen bonds: external reviews andtheshades… We remove all observations with an absolute yield difference |Δr| larger than 100 BP as a signal for data irregularities.15 To minimize the error resulting from linear interpolation, we solve the problem to determine the triplet with the smallest sum of absolute maturity differences as the final matched triplet (gb, cbi∗ , cbj∗ ). 4.3 Liquidity adjustment One important determinant of the bond pricing is liquidity (Amihud and Mendelson 1986; Chen etal. 2007). Therefore, we apply the following approach to capture a possible liquidity difference in the yield difference Δr for each potential bond triplet and to provide an accurate estimation of the green bond premium. We choose the daily bid-ask spread as the measure of liquidity in bond markets (see e.g., Schestag etal. 2016). For a single bond, we calculate the bid-ask spread L as the difference between the bid and the ask yield: For the synthetic bonds, we interpolate the liquidity measure based on the liquidity of the two comparable conventional bonds: Thereafter, the corresponding liquidity difference ΔL between green bonds and synthetic conventional bonds is We use the liquidity difference ΔL to capture the influence of distinct liquidity on the yield difference between green and conventional bonds in the following. 4.4 Sample anddescriptive statistics After the matching process, we identify 250 best matched bond triplets (250 green bonds matched with 500 conventional bonds).16 We document the reduction in (7) min i,j | | |Dcbi −Dgb | | |+ | | |Dcbj −Dgb | | | s.t. i∈I and j∈J (8) L=rbid −rask. (9) L cb =Lcbi∗+ L cbj∗ −L cbi∗ Dcb j ∗−Dcb i ∗ ⋅(Dgb −Dcbi∗) . (10) ΔL=Lcb −Lgb. 15 This data cleaning procedure leads to a reduction of only 112 daily observations. We also remove this procedure or change the 100 BP yield difference requirement to 150 BP to see whether it may lead to biases. These additional checks show similar empirical results as the main results reported in this paper. 16 Our green bonds are at least representative for plain vanilla green bonds for which a GBP can be identified. Tables13 and14 contain the respective summary statistics on the sample of all 1248 plain vanilla green bonds. 812 G.Dorfleitner et al. 1 3 sample size from 1248 to 250 during the whole matching process when adding matching criteria step by step in Table11. In total, our sample comprises 92,774 daily observations for the period from 2011 to 2020 and for various variables defined in Table 12. On average, the yield and liquidity difference between green bonds and comparable conventional bonds, are both close to zero (see Table2). The maturity of green bonds has an average value of 4.20years and ranges from less than one month to more than 28years. Green bonds have a maximum yield of 23% and a minimum of −0.97% , with a mean of 1.62%. The average issue volume of green bonds is 0.43 billion USD, which is lower than that of comparable conventional bonds (0.67 billion USD). Around half of the green bonds in the final sample are denominated in USD or EUR, while those denominated in currencies such as HKD, MXN, and SGD have a share of less than 1% (see Table3). Regarding issuer type, the largest share (26.80%) of green bonds are from supranational institutions such as the World Bank and the International Finance Corporation, and financial institutions such as banks. Furthermore, green bonds with an AAA credit rating comprise almost a third of the sample while those with a credit rating lower than A+ have a share of 10%. Besides basic bond features, we observe information related to external review reports. SPOs are the most popular type of external reviews. 196 out of 250 green bonds are assigned to an SPO. Among the green bonds with an SPO, 49 are categorized as dark green, and 52 as medium green. However, the other 95 have no specific shade of green, despite the existence of an SPO. Moreover, no green bond is classified as brown by SPO providers in our sample.17 Verification reports and Table 2 Descriptive statistics for metric variables This table reports summary statistics on time-variant and time-invariant green bond characteristics. The entire data sample contains 92,774 daily observations from 250 bond triplets (250 green bonds matched with 500 conventional bonds). The variables are defined in Table12 a Maturity of the green bond at issuance Variable Obs. Mean Std. Min Median Max Panel: time-variant Δr (%) 92,774 −0.0012 0.1208 −0.9969 0.0012 0.9834 ΔL (%) 92,774 −0.0019 0.0460 −0.4477 0.0001 0.3898 gb_yield (%) 92,774 1.6214 2.1388 −0.9720 0.9430 23.0020 maturity (in years) 92,774 4.2459 3.0428 0.0548 3.5863 28.8110 Panel: time-invariant Maturity a (in years) 250 6.2407 3.2921 1.9973 5.0055 30.0192 gb_volume (bn USD) 250 0.4267 0.4243 0.0018 0.3727 3.3456 cb_volume (bn USD) 250 0.6695 0.8690 0.0015 0.3235 5.5760 17 This does not mean that our sample is not representative. SPO providers seldom release a negative shade. For instance, CICERO’s SPOs are graded as dark green or medium green in most instances, if there is a clear evaluation result. 813 1 3 The pricing ofgreen bonds: external reviews andtheshades… Table 3 Descriptive statistics for categorical variables This table contains summary statistics on the green bond sample of this study. The entire data sample contains 92,774 daily observations from 250 bond triplets (250 green bonds matched with 500 conventional bonds). The variables are defined in Table12 MTG senior secured and mortgage backed, SEC secured, SR senior unsecured, SRBN senior non-preferred, SRP senior preferred, SRSEC senior secured, UN unsecured a Seniority indicates the combined information on bond seniority and collateral status on Eikon b NR means that the green bond does not have a S&P equivalent crediting rating on Eikon Variable Obs. Relative Variable Obs. Relative SPO CNY 10 4.00 Yes 196 78.40 EUR 72 28.80 No 54 21.60 GBP 3 1.20 HKD 1 0.40 Shade INR 4 1.60 Dark green 49 19.60 JPY 5 2.00 Medium green 52 20.80 MXN 1 0.40 No shade 95 38.00 NOK 6 2.40 No SPO 54 21.60 SEK 48 19.20 SGD 1 0.40 Verification TRY 3 1.20 Yes 51 20.40 USD 52 20.80 No 199 79.60 ZAR 6 2.40 CBI_certification issuer_type Yes 17 6.80 Agency 46 18.40 No 233 93.20 Corporate 47 18.80 Financial 67 26.80 green_rating Municipal 21 8.40 Yes 10 4.00 Sovereign 2 0.80 No 240 96.00 Supranational 67 26.80 Seniority a credit_rating MTG 8 3.20 AAA 79 31.60 SEC 2 0.80 AA + 11 4.40 SR 203 81.20 AA 13 5.20 SRBN 4 1.60 AA− 12 4.80 SRP 15 6.00 A + 15 6.00 SRSEC 4 1.60 A 5 2.00 UN 14 5.60 A− 4 1.60 BBB + 8 3.20 Currency BBB 4 1.60 AUD 23 9.20 BBB− 4 1.60 CAD 7 2.80 NR b 95 38.00 CHF 8 3.20 814 G.Dorfleitner et al. 1 3 the CBI certification appear to be less popular than SPOs in the green bond market. In our final sample, 20.40% green bonds have a verification report and only 6.80% have a certification from CBI. Only ten green bonds have a green rating from traditional credit rating agencies. Eight green bonds reveal an ‘excellent’ green rating from Moody and two green bonds a ‘Green 1’ green rating from Japan Credit Rating Agency (JCR). 5 Empirical methodology 5.1 Estimating thegreen bond premium To eliminate the impact of the liquidity difference on the green bond premium, we regress the yield difference on the liquidity difference in a hybrid model (see e.g., Mundlak 1978; Bell and Jones 2015): where ΔLi is the mean of the liquidity difference within a specific bond i, ui represents the individual error term, and eit is the overall error term. In the hybrid model, the variable ΔLit is decomposed into a within-effects component ΔLit − ΔLi and a between-effects component ΔLi . The estimate of the within-effects 𝛽1 is unbiased, regardless whether ui is correlated with ΔLit (Schunck 2013; Bell and Jones 2015). Moreover, it is also possible to estimate the between-effects 𝛽2 in the hybrid model. Given that the bonds in this study are collected from various countries and traded on various platforms, there could be between-effects in the bond pricing dynamics. We further subtract the influence of the liquidity difference from the yield difference and estimate the green bond premium as follows: where  𝛽1 and  𝛽2 are estimated coefficients from the hybrid model in Eq.(11). In this way, the estimated green bond premium pit varies across different bonds and over time. 5.2 Determinants ofthegreen bond premium We investigate the determinants of the green bond premium in another hybrid regression model. Besides its advantages mentioned in the previous subsection, the hybrid model enables incorporating time-invariant variables (Bell and Jones 2015). Since some time-invariant variables related to external reviews such as SPO and shade are of particular interest and important for testing our hypotheses, we adopt the hybrid model to investigate the determinants. We run the model with the time-variant green bond premium pit extracted from the initial hybrid regression in Eq.(11) as the dependent variable: (11) Δ r it =𝛽 0 +𝛽 1 (ΔL it −ΔL i )+𝛽 2 ΔL i +(u i +e it) (12) p it =Δr it −  𝛽 1 (ΔL it −ΔL i )−  𝛽 2 ΔL i 815 1 3 The pricing ofgreen bonds: external reviews andtheshades… TVit represents time-variant control variables, i.e., maturity and gb_yield. Accordingly, each time-variant variable is transformed into two variables (one in the within-effects vector TVit −TVi and the other in the between-effects vector TVi ) in the hybrid regression. TIi comprises time-invariant variables of interest, namely dummy or categorical variables regarding the existence of a specific type of external review or related greenness evaluation results. Moreover, TIi includes other timeinvariant control variables related to basic bond features such as currency, issuer_ type, and credit_rating that have been extensively investigated in earlier studies (see e.g., Zerbib 2019). 6 Results 6.1 The green bond premium This section tests our first hypothesis of whether investors trade green bonds at a premium in the secondary market in general. Accordingly, we apply the hybrid model in Eq.(11). Thereby, we estimate the green bond premium for each green bond on each trading day following Eq.(12). The variation of liquidity difference at the bond (13) p it =𝛾 0 +𝛾 1 (TV it −TV i )+𝛾 2 TV i +𝛾 3 TI i +(u i +e it ) . Table 4 Hybrid model to extract the green bond premium This table contains the results of the hybrid model explaining the difference in the yields of green and matched conventional bonds by the variation of liquidity. Δ L it − ΔL i measures the within-variability in liquidity, i.e., at the bond level. ΔLi represents the between-var- iability to capture cross-sectional effects. _cons represents the estimate for the average overall green bond premium in our sample. The full sample includes 92,774 daily observations for 250 bond triplets. Standard errors are cluster-robust at the issuer level ∗p<.1 , ∗∗ p<.05 , ∗∗∗ p<.01 *indicates the significance level of the coefficients: *p<.1, **p<0.05,*** p<0.01 Coef. Robust SE Δ L it − ΔL i 0.2882 ∗∗∗ 0.0953 Δ L i 0.9210 ∗∗ 0.3754 _cons 0.0094 ∗∗ 0.0039 N 92,774 Wald chi2 43.7700 Prob > chi2 0.0000 Rho 0.4968 816 G.Dorfleitner et al. 1 3 level explains part of the variation of yield difference as the coefficient of Δ L it − ΔL i which is significant at the 1% level (see Table4). Therefore, it is important to control for the liquidity difference when estimating the green bond premium. The significance of the coefficient of the second term Δ L i at the 5% level shows the existence of between-effects among different bonds. Moreover, the constant term (0.94 BP) in Table4 is significant at the 5% level. This constant term is the estimate for the expected value of the overall green bond premium. Considering Eqs.(11)and(12), this constant term is the estimate for the expected value of 𝛽0 of Eq.11. Thus, the expected overall green bond premium in our model is the average over the premiums of each green bond. Based on the significances presented in Table4, we find statistical evidence that supports H1 stating that investors trade green bonds, on average, at a premium over comparable conventional bonds. To illustrate the time-variant green bond premium, we calculate the cross-sec- tional average of pit on a daily basis to show the general development of the estimated green bond premium over time (see Fig.1).18 The green bond premium was rather volatile in earlier years and became stable in recent years.19 It appears that overall the green bond premium was more likely to be negative before 2015, and increased in the following years. 6.2 Shades ofgreen andtime‑variant green bond premium We continue with the test of hypotheses H2 to H4 regarding whether an external review and the greenness of green bonds impact on the premium in the secondary Fig. 1 Premium development over time. This figure shows the daily, cross-sectional average green bond premium over time 18 The spikes and dips are reasonable, as for some trading days, there are fewer daily observations. 19 It should be noted that the panel dataset is unbalanced and there are fewer green bonds in the first few years. 817 1 3 The pricing ofgreen bonds: external reviews andtheshades… market. Therefore, we run hybrid model regressions defined in Eq.(13) with robust standard errors clustered at the issuer level. To test Hypothesis2, we include four dummy variables (SPO, green_rating, verification, and CBI_certification) indicating whether a specific type of external review is available, besides control variables in the hybrid model (Model Hybrid1). The coefficients of SPO and verification are both significantly positive, at the 1 and 5% level, respectively. Thus, green bonds with an SPO and a verification face higher premiums than green bonds without such external reviews. In our theoretical framework, this finding supports the hypothesis that non-financial disclosure from external reviewers increases transparency substantially and there is a sufficiently large group of investors with an 𝛼>0 that influence equilibrium prices of green bond investments. However, we cannot find evidence that a CBI certification or a green rating makes an additional marginal contribution to a higher green bond premium. Therefore, even though there are four types of external reviews available in the green bond market, we find that the more popular type of external reviews, i.e., SPOs and verifications, are really valued by green bond investors. Furthermore, we include an interaction term ( maturity ∗SPO ) of maturity and SPO (= 1−SPO) in ModelHybrid2 to test whether the influence of an SPO is timedependent (H3). In a hybrid model, the time-variant interaction term is transformed into two terms, namely the within-effects term ( d _ maturity ∗SPO ), denoted by the prefix ‘d’, and the between-effects term ( m _ maturity ∗SPO ), denoted by the prefix ‘m’. Nevertheless, the coefficient of the within-effects term is not significant in the entire sample and thus does not support H3 stating that the premium of green bonds without an SPO will increase as investors become more familiar with these bonds. ModelHybrid3 takes advantages of the classification of SPOs into different categories, namely dark_green, medium_green, no_shade, and no_SPO, according to external reviewers’ evaluation results. The coefficients of the different shades of green in ModelHybrid3 are all significantly positive at the 1% level compared to the no_SPO category (reference category), with that of dark_green being the highest (5.36 BP) and no_shade the lowest (3.30 BP). In line with H2, green bonds reviewed by an SPO provider show significantly higher premiums compared to green bonds without an SPO for all shades of green. More specifically, green bonds with a “better” shade of green tend to have a higher green bond premium, which is in accordance with H4. Thus, investors also integrate the greenness of green bonds, as suggested by an SPO provider, into the pricing. Investors are willing to pay a higher premium if the green bond has proved to contribute seriously to climate adaptation and mitigation. To investigate the significance of the differences in the impact on the premium among different shades of green, we analyze the impact of the level of greenness on the green bond premium in the subsample of green bonds with an SPO. Accordingly, we run the estimation of ModelHybrid4, Table5, on the subsample of green bonds with an SPO. When no_shade is taken as the reference category, the coefficient of dark_green is significantly positive at the 5% level, while that of medium_green is not significant. Thus, investors trade a ‘dark green’-shaded green bond at a significantly higher premium than ones with no shade. This finding confirms our theoretical expectation that investors appreciate a higher level of greenness, and supports 818 G.Dorfleitner et al. 1 3 Table 5 Determinants of the green bond premium: main hybrid models Hybrid1 Hybrid2 Hybrid3 Hybrid4 H2 SPO 0.0355 ∗∗∗ (0.0126) 0.0425 ∗ (0.0231) Verification 0.0246 ∗∗ (0.0099) 0.0248 ∗∗ (0.0100) 0.0255 ∗∗∗ (0.0095) CBI_certifica- tion −0.0257 (0.0261) −0.0260 (0.0258) −0.0244 (0.0264) green_rating 0.0179 (0.0120) 0.0187 (0.0116) 0.0210 ∗ (0.0112) H3 d _ maturity ∗ SPO −0.0070 (0.0062) m_maturity ∗ SPO 0.0018 (0.0037) H4 dark_green 0.0536 ∗∗∗ (0.0157) 0.0227 ∗∗ (0.0094) medium_green 0.0376 ∗∗∗ (0.0145) 0.0076 (0.0093) no_shade 0.0330 ∗∗∗ (0.0126) Controls d_maturity 0.0076 (0.0052) 0.0103 ∗ (0.0055) 0.0076 (0.0052) 0.0131 ∗∗ (0.0061) d_gb_yield −0.0257 ∗∗ (0.0131) −0.0262 ∗∗ (0.0131) −0.0257 ∗∗ (0.0131) −0.0366 ∗∗ (0.0156) m_maturity −0.0026 ∗∗ (0.0012) −0.0029 ∗∗ (0.0014) −0.0026 ∗∗ (0.0012) −0.0024 ∗ (0.0013) m_gb_yield 0.0112 (0.0071) 0.0113 (0.0072) 0.0100 (0.0076) 0.0151 (0.0096) gb_volume −0.0173 (0.0139) −0.0168 (0.0142) −0.0152 (0.0131) −0.0245 ∗ (0.0142) issuer_type agency −0.0175 ∗ (0.0098) −0.0186 ∗ (0.0100) −0.0161 (0.0106) 0.0013 (0.0122) Financial 0.0327 ∗∗ (0.0163) 0.0326 ∗∗ (0.0163) 0.0343 ∗∗ (0.0161) 0.0340 ∗∗ (0.0171) Municipal −0.0067 (0.0109) −0.0075 (0.0112) −0.0067 (0.0109) 0.0020 (0.0104) Sovereign 0.0185 (0.0376) 0.0175 (0.0373) 0.0297 (0.0372) 0.0527 (0.0381) Supranational −0.0041 (0.0100) −0.0048 (0.0103) 0.0032 (0.0110) 0.0004 (0.0127) credit_rating AAA 0.0745 ∗∗∗ (0.0269) 0.0748 ∗∗∗ (0.0270) 0.0676 ∗∗ (0.0270) 0.0376 (0.0284) AA + 0.0573 ∗ (0.0295) 0.0574 ∗ (0.0296) 0.0551 ∗ (0.0299) 0.0262 (0.0278) AA 0.0023 (0.0221) 0.0027 (0.0223) −0.0029 (0.0238) −0.0001 (0.0240) AA− 0.0484 (0.0296) 0.0482 (0.0297) 0.0486 (0.0302) 0.0417 (0.0294) 819 1 3 The pricing ofgreen bonds: external reviews andtheshades… H4 to some extent. The pricing effect of the greenness level on the green bond premium prevails only for the dark_green vs.no_shade comparison, but is insignificant for the dark_green vs.medium_green comparison.20 Besides the above findings from variables that are of special interest, it is noteworthy that the coefficient of d_maturity is significantly positive at the 5% level in ModelHybrid4 (the subsample analysis). This pattern indicates that the premium of green bonds with an SPO is positively related to their maturity. In other words, as green bonds with an SPO have been traded on the market for a longer time (the maturity decreases), the green bond premium decreases. This fact provides some weak supporting evidence in the context of H3 to the extent that the premiums of green bonds with an SPO diminish, when the green bonds approach maturity. In Table 5 (continued) Hybrid1 Hybrid2 Hybrid3 Hybrid4 A + 0.0106 (0.0357) 0.0114 (0.0358) 0.0091 (0.0360) 0.0248 (0.0398) A −0.0055 (0.0380) −0.0054 (0.0379) −0.0141 (0.0381) −0.0104 (0.0414) A− 0.0976 ∗∗∗ (0.0303) 0.0988 ∗∗∗ (0.0313) 0.0955 ∗∗∗ (0.0286) 0.0786 ∗∗∗ (0.0294) BBB + 0.0328 (0.0215) 0.0334 (0.0214) 0.0321 (0.0218) 0.0546 ∗∗ (0.0228) BBB 0.0137 (0.0209) 0.0140 (0.0208) 0.0070 (0.0204) −0.0055 (0.0244) NR 0.0381 ∗ (0.0225) 0.0384 ∗ (0.0225) 0.0338 (0.0229) 0.0184 (0.0246) Seniority Yes Yes Yes Yes Currency Yes Yes Yes Yes _cons −0.0442 (0.0404) −0.0498 (0.0440) −0.0412 (0.0429) 0.0206 (0.0443) N 92,774 92,774 92,774 68,215 Rho 0.4971 0.4993 0.4971 0.4871 This table reports the results of the hybrid model regressions with the green bond premium pit as the dependent variable. Standard errors are cluster-robust at the issuer level and provided in parentheses. The full sample includes 92,774 daily observations for 250 matched bond triplets. The subsample in ModelHybrid4 only includes 68,215 daily observations for 196 green bonds with an SPO ∗p<.1 , ∗∗ p<.05 , ∗∗∗ p<.01 20 This is analyzed by making medium green the reference category and redoing the regression. Given the absence of a significant result, the corresponding table is omitted. 820 G.Dorfleitner et al. 1 3 terms of informational transaction cost theory, the documented premium difference pattern can be explained by searching costs for information, which is already provided by SPOs. The more mature a green bond becomes, the more information on the respective greenness is available. This reduces information costs and therefore, the requirement of higher yields to compensate for idiosyncratic greenness risk and to cover search costs. Regarding the other control variables, we observe the following. The coefficient of d_gb_yield indicates that the green bond yield is negatively related to the green bond premium. As regards issuer type, green bonds issued by agencies have a lower premium (in ModelHybrid1 - Hybrid2), while those issued by financial institutions enjoy a significantly higher premium compared to those issued by corporates (in all model specifications). Moreover, green bonds with a credit rating of AAA or AA+, which constitute a considerable percentage of the sample, evidently enjoy a higher Table 6 Hybrid model to extract the green bond premium— robustness check This table contains the results of the hybrid model explaining the difference in the yields of green and matched conventional bonds by the variation of liquidity for the restricted sample. Δ L it − ΔL i measures the within-variability in the liquidity, i.e., at the bond level. ΔLi represents the between-variability to capture cross-sectional effects. The restricted sample includes only 89,285 daily observations for 216 bond triplets, due to stricter data filters. Standard errors are cluster-robust at the issuer level ∗p<.1 , ∗∗ p<.05 , ∗∗∗ p<.01 *indicates the significance level of the coefficients: *p<.1, **p<0.05, ***p<0.01 Coef. Robust SE Δ L it − ΔL i 0.4603 ∗∗∗ 0.1309 ΔLi 0.8898 ∗∗∗ 0.3364 _cons 0.0065 ∗ 0.0037 N 89,285 Wald chi2 25.9900 Prob > chi2 0.0000 Rho 0.4917 Table 7 Descriptive statistics for the green bond premium—robustness check The restricted sample includes only 89,285 daily observations for 216 bond triplets, due to stricter data filters. The green bond premium pit is estimated by Eq.12. The green bond premium pi is extracted from the fixed-effects model in Eq.14. p-value is from a t-test identifying whether pit or pi is significantly different from zero Obs. Mean Std. p-value Min Median Max Panel: smaller sample pit 89,285 −0.0002 0.1058 0.4881 −0.9698 0.0024 0.9509 Panel: Zerbib’s approach pi 250 0.0095 0.0820 0.0678 −0.4799 0.0031 0.6327 827 1 3 The pricing ofgreen bonds: external reviews andtheshades… OLS1 OLS2 OLS3 Currency Yes Yes Yes _cons 0.0182 (0.0399) 0.0180 (0.0368) 0.0741 ∗ (0.0442) N 250 250 196 R2 0.23 0.24 0.34 Adjusted R2 0.08 0.08 0.19 Table 10 (continued) This table reports the results of the OLS regressions with the green bond premium pit as the dependent variable. The dependent variable is the estimated individual effects pi derived from the fixed-effects regression. Standard errors are cluster-robust at the issuer leveland provided in parentheses. The full sample includes 250 matched bond triplets. For ModelOLS3, the subsample only includes 196 green bonds with an SPO ∗p<.1 , ∗∗ p<.05 , ∗∗∗ p<.01 ModelOLS_1 of Table10 shows that the coefficient of SPO is significantly positive at the 1% level. This provides evidence supporting H2 stating that external review reports have a positive influence on the premium. Regarding CBI_certifica- tion, green_rating, and verification, we do not find strong evidence for H2 except that green_rating is significant at the 10% level in Model OLS_2. When SPOs with different shades of green are treated separately in ModelOLS_2, the coefficient of dark_green, medium_green, and no_shade yield a similar pattern as in the main models and thus support H4. Lastly, the coefficient of dark_green shows significantly higher premium in ModelOLS_3. In summary, the OLS regression results support most of our main findings from the hybrid models regarding H2 and H4. 7 Conclusion In this paper, we revisit the existence of the green bond premium in a comprehensive dataset and examine systematically the impact of all four different types of external reviews and their greenness evaluation on the bond yields. To estimate the green bond premium, we adopt a strict matching between green and conventional bonds. After the matching process, the final sample contains 250 green bonds matched with 500 conventional ones, and more than 92,774 daily observations from 2011 to 2020. On this sample, we perform a two-step regression procedure based on a hybrid model to elicit the green bond premium and its determinants. The first main finding is that, on average, the expected green bond premium is positive and statistically significant. However, some green bonds are priced evidently higher than their counterparts. In particular, green bonds with an SPO or a verification c.p.enjoy a higher green bond premium. This relationship indicates that credible and assured non-financial disclosure seems valuable for investors. In particular, investors trade green bonds 828 G.Dorfleitner et al. 1 3 with SPOs at prices that increase with the level of greenness evaluation of the green bond, i.e., a darker shade of green is more likely to have a higher premium. This pattern implies that the shade-of-green methodology adopted by external reviewers has the potential to function as a tool for assessing the greenness of green bonds in a pricing-relevant manner, analogous to credit ratings. Issuers of green bonds can thereby lower the financing costs, at least for such green bonds that finance deeply green projects related to mitigating climate change. Our results also have significant policy and research implications. Independent external reviews appear to be one of the most important pillars of a healthy green bond market, through reducing information asymmetry between issuers and investors. The importance of external reviews and shade of green methodology in green bond pricing reveals that investors are sensitive to information asymmetry on the green asset market. If more public information regarding the greenness of green bonds is available, the investor base of green assets may be extended as investors have more confidence in green assets and are subject to a lower risk of greenwashing. Thus, a reduction of information asymmetry is indeed crucial to the development of climate finance. For instance, there could be more deliberately designed mandatory rules that foster transparency in the industry besides current voluntary-based industry guidelines such as the GBPs. Easier access to third-party reports and evaluations should be promoted to facilitate communication among market participants. Governmental policies supporting issuers of green bonds to achieve standardized, affordable, and independent greenness assessments may contribute to a prosperous climate finance market. This observations on financial markets also highlight the need for more theoretical and empirical research on green finance aspects. Clearly, our findings are not in line with traditional finance theory. Thus, from a behavioral finance perspective they provide some evidence for a greenness bias in the prices of green bonds. However, a contemporary view on such phenomena is rather to rationalize them, i.e.,to view them as rational and not as irrational effects. The theoretical reasoning pursued in this study adheres to such an approach. However, more future research on the rationale – and even the calculus – of impact investors appears to be in urgent need. Moreover, future research may analyze further green pricing anomalies and, if applicable, develop a new asset pricing model for bonds. Furthermore, these results are not limited to the bond market but may be applied to other asset classes. Appendix See Tables11, 12, 13 and 14 and Fig.2. 829 1 3 The pricing ofgreen bonds: external reviews andtheshades… Table 11 How the sample size is reduced during the matching process This table shows how the sample size is reduced during the matching process step by step. The initial sample size of green bonds is 1248. We extract a complete list of conventional bonds for each green bond issuer and start the matching process from step 1 to step 9 a This requirement means that for 292 green bonds we do not find straight conventional bonds which can be matched with green bonds Criterium description Sample size Initial sample 1248 1 Same bond structure (i.e. straight conventional bonds) a −292 2 Same currency type −74 3 Same coupon type −1 4 Same seniority and collateral status −67 5 Same credit rating −25 6 Issue amount: 0.25 to 4 times −66 7 Issue date: −6 to 6 years −38 8 Duration difference: −2 to 2 years −241 9 50 joint daily yield observations −194 Final sample 250 830 G.Dorfleitner et al. 1 3 Table 12 Definition of variables Variable Description H1 Δr Yield difference between green bonds and comparable synthetic conventional bonds pit Green bond premium extracted from the hybrid model in Eq.11 pi Individual effects extracted from the fixed-effects model in Eq.14 H2 SPO Binary variable with a value of one if a second-party opinion is assigned to the green bond, zero otherwise Verification Binary variable with a value of one if a verification is assigned to the green bond, zero otherwise CBI_certification Binary variable with a value of one if a CBI certification is assigned to the green bond, zero otherwise green_rating Binary variable with a value of one if the green bond has a green rating from a traditional credit rating agency, zero otherwise H3 SPO Binary variable with a value of one if a second-party opinion is not available, zero otherwise. SPO =1−SPO H4 shade Categorical variable indicating the shade of green. Green bonds are classified into four categories, namely dark green, medium green, no shade and no SPO. The default reference category is no SPO Controls ΔL Liquidity difference between a green bond and its comparable synthetic conventional bond Maturity Maturity of the green bond gb_yield Daily bid yield of the green bond gb_volume Issue volume of the green bond cb_volume Issue volume of the synthetic bond. The issue volume of the synthetic bond is calculated as the mean of the issue volumes of the two conventional bonds (cb1 and cb2) Seniority Categorical variable indicating the seniority and the collateral status of the green bond on Eikon. The reference category is ‘unsecured’ Currency Categorical variable indicating which currency the green bond is denominated in. The reference category is USD issuer_type Green bond issuers are classified into six categories, such as agency, corporate and financial institution. The reference category is corporate credit_rating Credit rating of the green bond. Credit ratings from different rating agencies have been transformed into the same scale. The reference category is BBB− Table 13 Descriptive statistics for the green bond sample before matching—metric variables This table reports summary statistics on characteristics of the green bond sample before the matching process. The sample includes 1248 green bonds. The variables are defined in Table12 a Maturity of the green bond at issuance Variable Obs. Mean Std. Min Median Max Maturity a (in years) 1248 7.4109 6.2719 0.9945 5.0055 100.0658 gb_volume (bn USD) 1248 0.3000 0.5010 0.0000 0.1029 6.6912 831 1 3 The pricing ofgreen bonds: external reviews andtheshades… Table 14 Descriptive statistics for the green bond sample before matching—categorical variables This table reports summary statistics on characteristics of the green bond sample before the matching process. The sample includes 1248 green bonds. The variables are defined in Table12 MTG senior secured and mortgage backed, SEC secured, SR senior unsecured, SRBN senior non-preferred, SRP senior preferred, SRSEC senior secured, UN unsecured a Seniority indicates the combined information on bond seniority and collateral status on Eikon b NR means that the green bond does not have a S&P equivalent crediting rating on Eikon Variable Obs. Relative Variable Obs. Relative Seniority a SEK 148 11.86 MTG 12 0.96 SGD 4 0.32 SEC 6 0.48 THB 5 0.40 SR 904 72.44 TRY 12 0.96 SRBN 11 0.88 TWD 23 1.84 SRP 20 1.60 USD 264 21.15 SRSEC 38 3.04 VND 2 0.16 UN 257 20.59 ZAR 17 1.36 Currency issuer_type AUD 58 4.65 Agency 183 14.66 BRL 15 1.20 Corporate 345 27.64 CAD 27 2.16 Financial 389 31.17 CHF 18 1.44 Municipal 67 5.37 CNY 119 9.54 Sovereign 11 0.88 COP 1 0.08 Supranational 253 20.27 CZK 2 0.16 DKK 2 0.16 credit_rating EUR 245 19.63 AAA 299 23.96 GBP 10 0.80 AA + 57 4.57 HKD 20 1.60 AA 82 6.57 HUF 3 0.24 AA− 78 6.25 IDR 8 0.64 A + 105 8.41 INR 19 1.52 A 23 1.84 JPY 105 8.41 A− 50 4.01 KRW 2 0.16 BBB + 37 2.96 MXN 12 0.96 BBB 26 2.08 MYR 54 4.33 BBB− 14 1.12 NGN 1 0.08 BB + 1 0.08 NOK 21 1.68 BB 2 0.16 NZD 20 1.60 B + 1 0.08 PEN 2 0.16 B 2 0.16 PHP 2 0.16 B− 1 0.08 PLN 3 0.24 NR b 470 37.66 RUB 4 0.32 832 G.Dorfleitner et al. 1 3 Funding Open Access funding enabled and organized by Projekt DEAL. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Amihud Y, Mendelson H (1986) Asset pricing and the bid-ask spread. 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