The impact of collateral-based monetary policy on green financing cost: an analysis of the People's Bank of China’s approach

ABSTRACT

This paper examines the effects of the collateral-based monetary policy, implemented by the People’s Bank of China, on green financing costs. Employing a Difference-in-Differences (DID) method to examine credit spreads of financial bond in primary and secondary markets from 2017 to 2019, our findings highlight a significant decrease of 26 basis points (bps) in the financing costs of green financial bonds in the primary market, with a 12-bps decline in the secondary market post-policy. Our study underscores the crucial role of implicit credit enhancement provided by the People’s Bank of China's collateral endorsement. Interestingly, the liquidity provision mechanism, where investors pay premiums to secure liquidity support, does not find empirical backing in the specific context of China's bond market. These findings illuminate the effectiveness of collateral-based monetary policies in reducing green financing costs, offering valuable insights for using collateral strategies to green monetary policies.

Key policy insights

• Collateral-based policies’ success in reducing green financing costs suggests a strategic avenue for supporting green monetary policy goals.

• Implicit credit enhancement serves as a crucial mechanism driving this cost reduction.

• The People’s Bank of China's experience exemplifies the robustness of such policies without requiring strict collateral scarcity, highlighting broad policy applicability.

• Collateral-based monetary policy could drive the ‘green premium’, broadening green bond appeal beyond just environmentally-focused investors.

KEYWORDS:

• Climate change
• Central Bank
• People's Bank of China
• green finance
• green bond
• green premium

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contribution statement

Yong Xue: Conceptualization, Methodology, Formal Analysis, Resources, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualization, Funding Acquisition. Xinyi Yun: Formal Analysis, Software, Validation, Resources, Data Curation.

Data availability statement

The data that support the findings of this study are available from the corresponding author, Y.X., upon reasonable request.

Notes

1 As per the PBOC's announcement in 2019, the Loan Prime Rate (LPR) in China is set by quoting banks using the MLF rate plus points, providing a pricing benchmark for bank loans starting from 20 August 2019.

2 For the official announcement, please refer to PBOC’s website page (in Chinese):

http://www.pbc.gov.cn/goutongjiaoliu/113456/113469/3549913/index.html

3 Detailed statistics can be found on the CBI official website: https://www.climatebonds.net/

4 The 26-bps reduction reported in the context of the difference-in-differences (DID) model represents the net impact of the policy on green financial bonds' financing costs. This is estimated as the decrease in the credit spread of green financial bonds minus the concurrent decrease in the credit spread of conventional financial bonds, effectively highlighting the specific effect of the policy on green financial bonds.

5 The PBOC adopts a cautious approach to interest rate adjustments. For instance, during our study period from 2017 to 2019, the open market operation (OMO) rates and the MLF rates were only lowered once in November 2019, with a modest reduction of 5 bps.

6 Since February 2016, the PBOC has institutionalized daily open market operations as a key strategy to regulate market liquidity and signal monetary policy intentions, primarily using 7-day reverse repos. During our study period from 2017 to 2019, the cumulative volume of 7-day reverse repos reached 23.18 trillion CNY, far surpassing the 14-day and 28-day repos at 10.16 and 5.31 trillion CNY respectively.

7 The SUR technique allows for efficient estimation when there are potentially correlated error structures across different regression equations. Under the SUR framework, the steps to test differences in group regression coefficients are: (1) Perform separate regression for each group, (2) apply SUR estimation to the two groups, and (3) test whether the coefficients of interest are equal.

8 As of June 2023, the balance of the MLF was 5.1910 trillion CNY, while the balance of traditional eligible collateral, namely government bonds, stood at 26.2859 trillion CNY, central bank bills at 15 billion CNY, local government bonds at 37.4635 trillion CNY and policy bank bonds at 23.6587 trillion CNY. This market structure demonstrates that traditional eligible collateral already provides an ample supply for MLF bidding.

9 For instance, during our 2017–2019 study period, spanning 636 trading days, the most actively traded among 9 green financial bonds was the "16 SPD Green Financial Bond 03(1628012.IB) ", traded for just 131 days. The other bonds were even less active in the market.

10 The ChinaBond Valuation Yield, issued by the China Central Depository & Clearing Co., Ltd. (CCDC), is a key reference rate in China's bond market, known for its high reliability and credibility due to CCDC's foundational role in China’s bond market.

11 For example, H. Fang et al. (2020) reported a larger price impact compared to our study, but they examined a different aspect of PBOC's policy, aiding financing for real sector enterprises by involving low-rated corporate bonds (issued by business and industry enterprises) as collateral. This price impact difference is actually aligned with our argument for the implicit credit enhancement mechanism, where green financial bonds, issued by banks and other financial institutions, inherently have higher credit quality than corporate bonds, resulting in a lesser implicit credit enhancement effect.

Additional information

Funding

This work was supported by the National Social Science Fund of China under [grant number 22XJL008].