How does monetary policy shock affect banks' loan loss provisioning behavior? Evidence from Chinese commercial banks

IF 5.2 1区 经济学 Q1 ECONOMICS
Yong Ma , Huanqi Lan
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Abstract

This study examines the impact of monetary policy shocks on banks' loan loss provisioning behavior using panel data from Chinese commercial banks. We find robust evidence that a positive monetary policy shock negatively affects banks' loan loss provisions (LLPs), as banks adjust LLPs to enhance reported earnings and conceal risks during periods of monetary tightening. Further analysis reveals that banks with higher risk profiles or weaker risk resilience exhibit larger reductions in LLPs in response to monetary policy shocks. Additionally, stricter macroprudential policies and higher regulatory quality encourage banks to maintain higher levels of LLPs. We also find that during periods of credit contraction, banks are less likely to reduce LLPs to bolster future earnings, as doing so poses greater risks. The findings underscore the importance of maintaining transparent and stable monetary policy to prevent distortions in banks' loan loss provisioning behavior and highlight the need for effective regulations to ensure that banks remain healthy and maintain adequate LLPs, particularly during economic and financial downturns.
货币政策冲击如何影响银行的贷款损失拨备行为?来自中国商业银行的证据
本文利用中国商业银行的面板数据,考察了货币政策冲击对银行贷款损失拨备行为的影响。我们发现强有力的证据表明,积极的货币政策冲击会对银行的贷款损失准备金(llp)产生负面影响,因为银行会在货币紧缩期间调整贷款损失准备金以提高报告收益并隐藏风险。进一步分析表明,风险概况较高或风险抵御能力较弱的银行在应对货币政策冲击时,llp的下降幅度较大。此外,更严格的宏观审慎政策和更高的监管质量鼓励银行保持较高的llp水平。我们还发现,在信贷收缩期间,银行不太可能减少有限责任合伙以增加未来收益,因为这样做会带来更大的风险。研究结果强调了保持透明和稳定的货币政策以防止银行贷款损失准备行为扭曲的重要性,并强调了有效监管的必要性,以确保银行保持健康并维持足够的llp,特别是在经济和金融衰退期间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
中国经济评论
中国经济评论 ECONOMICS-
CiteScore
10.60
自引率
4.40%
发文量
380
期刊介绍: The China Economic Review publishes original works of scholarship which add to the knowledge of the economy of China and to economies as a discipline. We seek, in particular, papers dealing with policy, performance and institutional change. Empirical papers normally use a formal model, a data set, and standard statistical techniques. Submissions are subjected to double-blind peer review.
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