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Different Shades of Green: Estimating the Green Bond Premium using Natural Language Processing

Author

Listed:
  • Emanuela Benincasa

    (University of Zurich and Swiss Finance Institute)

  • Jonathan Fu

    (University of Zurich)

  • Mrinal Mishra

    (University of Zurich)

  • Adityavardhan Paranjape

    (Zurich Insurance Group)

Abstract

We document the existence of a premium in the green bond market based on the greenness of green bonds. Using BERT, a natural language processing method for textual analysis, we develop a novel measure for bonds’ greenness and document that a 10 percent increase in the bond’s greenness corresponds to a decrease in annualized yield by between 4.86 to 8.71 basis points. In addition to greener bonds enjoying higher premiums, we find evidence that issuing a green bond has positive spillover effects on the pricing of subsequent conventional bonds’ issuance. Overall, our findings are consistent with firms relying on “green" debt instruments to lower capital costs and raise cheaper financing.

Suggested Citation

  • Emanuela Benincasa & Jonathan Fu & Mrinal Mishra & Adityavardhan Paranjape, 2022. "Different Shades of Green: Estimating the Green Bond Premium using Natural Language Processing," Swiss Finance Institute Research Paper Series 22-64, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2264
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    More about this item

    Keywords

    Green bonds; BERT model; Sustainable Finance; Bond premium;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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