作为后金融危机时代贷款损失拨备制度的重要改革,预期信用损失模型实施的核心在于引入前瞻性风险预估,实现了从“已发生损失”到“预期损失”的会计范式转变。而该项会计准则改革是否以及如何影响货币政策传导效率尚未引起充分关注。文章构建了一个银行局部均衡模型,阐释了预期信用损失模型的实施影响货币政策传导效率的机理,并将中国银行业自2018年起分批次实施预期信用损失模型视为一项准自然实验,采用渐进双重差分模型进行了因果识别。研究发现,预期信用损失模型的实施显著提升了货币政策传导效率。这一效应主要通过前瞻性拨备成本渠道实现,且该渠道的有效性依赖于拨备计提的充分性与及时性;同时,抵押品价值渠道与风险偏好渠道发挥了协同作用。上述效应在市场集中度高、内部治理有效、客户结构分散以及拨备监管压力大的银行中更明显。文章的研究为金融会计准则及货币政策、宏观审慎政策的协调配合提供了重要依据。
贷款损失拨备制度改革会影响货币政策传导效率吗?——来自中国银行业的证据
摘要
参考文献
1 戴德明, 张姗姗. 贷款损失准备、盈余管理与商业银行风险管控[J]. 会计研究, 2016, (8): 25−33.
2 丁友刚, 严艳. 中国商业银行贷款拨备的周期效应[J]. 经济研究, 2019, (7): 142−157.
3 郭峰, 熊瑞祥. 地方金融机构与地区经济增长——来自城商行设立的准自然实验[J]. 经济学(季刊), 2018, (1): 221−246. DOI:10.13821/j.cnki.ceq.2017.04.09
4 刘雪娇, 赵钰颖, 杜兴强. 贷款损失拨备制度改革与前瞻性人力资本需求[J]. 中国工业经济, 2024, (12): 155−173. DOI:10.3969/j.issn.1006-480X.2024.12.009
5 陆军, 黄嘉. 利率市场化改革与货币政策银行利率传导[J]. 金融研究, 2021, (4): 1−18.
6 宋芳秀, 宋奎壁. 公众异质预期、信息成本与货币政策传导[J]. 金融研究, 2024, (7): 1−19.
7 隋建利, 刘碧莹. 未预期货币政策非中性的混频识别: 行动与语言的信息效应[J]. 世界经济, 2020, (11): 176−200.
8 王成龙, 黄瑾, 严丹良, 等. 预期信用损失模型会缓解贷款拨备的顺周期效应吗?[J]. 国际金融研究, 2023, (9): 86−96.
9 徐明东, 陈学彬. 中国微观银行特征与银行贷款渠道检验[J]. 管理世界, 2011, (5): 24−38. DOI:10.19744/j.cnki.11-1235/f.2011.05.003
10 张勇, 涂雪梅, 周浩. 货币政策、时变预期与融资成本[J]. 统计研究, 2015, (5): 32−39.
11 Abad J, Suarez J. The procyclicality of expected credit loss provisions[R]. CEPR Press Discussion Paper, 2018.
12 Acharya V V, Ryan S G. Banks’ financial reporting and financial system stability[J]. Journal of Accounting Research, 2016, 54(2): 277−340. DOI:10.1111/1475-679X.12114
13 Balakrishnan K, Ertan A. Credit information sharing and loan loss recognition[J]. The Accounting Review, 2021, 96(4): 27−50. DOI:10.2308/TAR-2017-0244
14 Banerjee A, Bystrov V, Mizen P. How do anticipated changes to short-term market rates influence banks’ retail interest rates? Evidence from the four major euro area economies[J]. Journal of Money, Credit and Banking, 2013, 45(7): 1375−1414. DOI:10.2139/ssrn.2008632
15 Beatty A, Liao S. Do delays in expected loss recognition affect banks’ willingness to lend?[J]. Journal of Accounting and Economics, 2011, 52(1): 1−20. DOI:10.1016/j.jacceco.2011.02.002
16 Bernanke B S, Blinder A S. Credit, money, and aggregate demand[J]. American Economic Review, 1988, 78(2): 435−439. DOI:10.3386/w2534
17 Bischof J, Laux C, Leuz C. Accounting for financial stability: Bank disclosure and loss recognition in the financial crisis[J]. Journal of Financial Economics, 2021, 141(3): 1188−1217. DOI:10.1016/j.jfineco.2021.05.016
18 Buchetti B, Perdichizzi S, Santoni A. Fast to cut, slow to restore: Bank lending responses to IFRS 9 stage migrations[J]. Economics Letters, 2025, 254: 112446. DOI:10.1016/j.econlet.2025.112446
19 Bushman R M, Williams C D. Delayed expected loss recognition and the risk profile of banks[J]. Journal of Accoun- ting Research, 2015, 53(3): 511−553. DOI:10.1111/1475-679X.12079
20 Cengiz D, Dube A, Lindner A, et al. The effect of minimum wages on low-wage jobs[J]. The Quarterly Journal of Economics, 2019, 134(3): 1405−1454. DOI:10.1093/qje/qjz014
21 Chen J, Dou Y W, Ryan S G, et al. The effect of the current expected credit loss approach on banks’ lending during stress periods: Evidence from the COVID-19 recession[J]. The Accounting Review, 2025, 100(1): 113−138. DOI:10.2308/TAR-2022-0275
22 Dell'Ariccia G, Laeven L, Suarez G A. Bank leverage and monetary policy's risk-taking channel: Evidence from the United States[J]. The Journal of Finance, 2017, 72(2): 613−654. DOI:10.1111/jofi.12467
23 European Systemic Risk Board. Financial stability implications of IFRS 9[R]. ESRB, 2017.
24 Fan Y Y, Jiang Y X, Zhang X Z, et al. Women on boards and bank earnings management: From zero to hero[J]. Journal of Banking & Finance, 2019, 107: 105607. DOI:10.1016/j.jbankfin.2019.105607
25 Hicks J R. Mr. Keynes and the “classics”: A suggested interpretation[J]. Econometrica, 1937, 5(2): 147−159. DOI:10.2307/1907242
26 Kashyap A K, Stein J C. The impact of monetary policy on bank balance sheets[J]. Carnegie-Rochester Conference Series on Public Policy, 1995, 42: 151−195. DOI:10.1016/0167-2231(95)00032-U
27 Kim J B, Ng J, Wang C, et al. The effect of the shift to an expected credit loss model on loan loss recognition timeliness[R]. SSRN, 2021.
28 Kvaal E, Löw E, Novotny-Farkas Z, et al. Classification and measurement under IFRS 9: A commentary and suggestions for future research[J]. Accounting in Europe, 2024, 21(2): 154−175. DOI:10.1080/17449480.2023.2253808
29 Laeven L, Majnoni G. Loan loss provisioning and economic slowdowns: Too much, too late?[J]. Journal of Financial Intermediation, 2003, 12(2): 178−197. DOI:10.1016/S1042-9573(03)00016-0
30 Li X, Ng J, Saffar W. Accounting-driven bank monitoring and firms’ debt structure: Evidence from IFRS 9 adoption[J]. Management Science, 2024, 70(1): 54−77. DOI:10.1287/mnsc.2022.4628
31 López-Espinosa G, Ormazabal G, Sakasai Y. Switching from incurred to expected loan loss provisioning: Early evi- dence[J]. Journal of Accounting Research, 2021, 59(3): 757−804. DOI:10.1111/1475-679X.12354
32 Mengistu M M, Ng J, Saffar W, et al. Bank monitoring of borrowers and borrowers’ investment efficiency: Evidence from the switch to the expected credit loss model[R]. SSRN, 2022.
33 Salazar Y, Merello P, Zorio-Grima A. IFRS 9, banking risk and COVID-19: Evidence from Europe[J]. Finance Research Letters, 2023, 56: 104130. DOI:10.1016/j.frl.2023.104130
引用本文
何靖, 邓可斌. 贷款损失拨备制度改革会影响货币政策传导效率吗?——来自中国银行业的证据[J]. 财经研究, 2026, 52(6): 79-94.
导出参考文献,格式为:
上一篇:大学国际化提高了区域创新能力吗?
本期封面
相关论文





277
313
