商业银行贷款损失准备缺口及其监管问题

贷款损失准备缺口是衡量商业银行损失准备充足性的重要指标。从整体趋势看,我国商业银行损失准备充足性在不断提高,账面损失准备缺口持续下降。但是账面缺口与实际缺口是有差异的。商业银行目前主要依据财政部和监管部门有关制度,实行预期现金流折现和风险分类基础上的原则性比例计提方法。计提范围和方法的科学性、风险分类和预期损失的准确性、资本充足率达标和利润考核是影响贷款损失准备计提充足性、产生实际缺口的主要因素。监管部门应通过明确表外信贷资产的计提、改进对折现率和预期现金流的测算、明确抵押物的处置方法等完善贷款损失准备计提制度,加强对商业银行风险分类工作的指导和监管,加强损失准备计提充足性的现场检查,建立激励相客机制等,促进商业银行充足拨备。     

贷款减值准备研究

就会计而言,银行需要通过计提减值准备以准确反映贷款价值;从监管角度看,银行需要维持足够的贷款损失准备抵御预期损失。因此,贷款减值准备一直受到会计主管部门与银行业监管机构的共同关注。     

预期信用损失模型对银行资本计提的结构性影响研究——基于50家上市商业银行的实证分析

预期信用损失模型是一项新的贷款损失准备计提方法,对其经济后果的评估非常重要.关于预期信用损失模型的实施对我国商业银行资本计提的具体影响,目前还缺乏深入系统的研究.本文结合我国50家上市商业银行的公开数据,从监管资本、会计损益和资本计提前瞻性的角度分析了预期信用损失模型对我国商业银行资本计提的影响.研究结果表明:总体影响上,商业银行的贷款损失准备计提金额显著增加,利润波动增强,对资本充足率带来一定冲击;从不同特征商业银行看,小型商业银行、使用权重法商业银行、城市商业银行和H股上市商业银行受到的影响更为严重;从资本计提的前瞻性看,贷款损失准备的前瞻性得到加强,顺周期性效应得到一定程度缓解,但并不能完全消除.     

预期信用损失模型实施与银行信贷过度增长

如何通过制定有效的贷款损失准备计提规则来影响银行信贷行为,进而促进经济高质量发展,是政策当局亟需解决的难题。鉴于此,本文利用贷款损失准备会计准则改革——2018年预期信用损失模型实施——作为准自然实验,运用渐进双重差分法,实证检验了预期信用损失模型是否及如何影响我国商业银行信贷过度增长。研究发现,预期信用损失模型可显著降低银行信贷过度增长,且这一结论在经过稳健性检验后仍然成立。从机制分析看,预期信用损失模型主要通过充分性效应和及时性效应两条路径作用于银行信贷过度增长。进一步的分析则表明,预期信用损失模型对于以权重法进行资本计量的银行和资本充足率较低的银行而言,其抑制信贷过度增长的作用更为突出。本文丰富了预期信用损失模型经济后果的有关研究,为会计准则制定与金融监管政策调整提供了有益的政策启示。     

新会计准则对商业银行贷款损失准备的影响

与国际接轨的新会计准则的实施,给我国商业银行的贷款损失准备及其监管带来了很大影响。新会计准则对银行贷款损失准备监管的主要影响有:(1)新会计准则关于贷款损失准备的规定与监管审慎性要求相矛盾;(2)新会计准则规定的贷款损失准备计提方法与监管要求的贷款损失准备计提方法有分歧。新会计准则对商业银行的贷款损失准备的影响主要有:(1)降低了商业银行利用贷款损失准备操纵利润的可能性;(2)使得贷款损失准备具有更大的波动性;(3)引入时间价值,可能导致准备水平的提高;(4)可能导致利息收入和减值准备同步增加,并将影响各会计期间的利润发生一定程度的变化。本文对以上影响进行了分析,并提出了相应的对策建议。     

商业银行贷款损失准备计提效率及其影响因素

本文采用随机前沿分析方法,分析2007~2013年中国A股上市商业银行贷款损失准备计提的相对效率,并探讨计提效率的影响因素。研究发现,贷款损失准备计提的技术非效率在中国商业银行中确实存在,国有银行与股份制银行的计提效率相近,但都高于城市商业银行;银行的规模、管理水平以及监管机构的监管行为对银行贷款损失准备的计提效率有显著的正面影响;同时银行贷款损失准备的计提效率也表现出一定的顺周期性。因此,为提高贷款损失准备计提效率,应全面提高银行自身管理水平,强化对银行贷款损失准备计提的监管,完善计提方法和动态拨备计提体系。     

非预期损失与极端损失视角下的贷款保险定价方法

较之表现为贷款预期损失的信贷风险,商业银行自身对表现为贷款非预期损失与极端损失的信贷风险的覆盖能力非常有限。基于对信贷风险计量的新认识,充分考虑信贷资产非预期损失与极端损失的各种情况,构建起的贷款保险定价模型,能改善贷款保险价格的定价依据,对完善信贷保险机制、创新信贷风险转移定价理论具有积极的现实意义。研究发现：表现为贷款非预期损失与极端损失的信贷风险更适合被保险业务转移,且基于此制定的贷款保险价格存在可能的价格优势。研究同时结合国家对金融保险业的改革愿景,提出了加快贷款保险业务发展、完善贷款保险业务价格形成条件与形成机制的相关政策建议。     

资本监管制度与贷款损失准备计提会计准则的协调性——小微企业信贷诱导有效性视角

本文考察下调小微企业债权风险权重的《商业银行资本管理办法(试行)》的银行信贷供给诱导有效性及其贷款损失准备计提会计准则规范影响的协调性。研究发现,下调小微企业债权风险权重的资本监管规则实施对银行小微企业信贷供给具有显著诱导有效性,但已发生损失模型下贷款损失准备计提对此具有削弱作用,其削弱路径主要来自会计信息的风险信号传递调节效应,与资本约束中介效应无关。进一步研究发现,相比其他银行,市场竞争力较弱的银行、以市场景气度较高小微企业为主要信贷投放对象的银行,具有更为显著的资本监管诱导有效性及贷款损失准备计提削弱效应,显示提高相关制度协调性并充分发挥市场激励作用,有助于进一步缓解小微企业"信贷配给"现象。     

贷款减值的理论基础与减值会计模型

贷款减值理论基础可以从资产内涵、会计目标、稳健主义和风险量化管理理论四个维度来分析。贷款减值不仅是一项会计实践,也事关金融监管与稳定。现行已发生损失减值模型同现代企业估值模型具有内在一致性,但减值确认存在滞后性。预期损失模型旨在前瞻性确认贷款预期损失,消除亲周期性影响。研究表明:预期损失模型对预期改变非常敏感,也无法彻底解决现行模型的亲周期问题;中国目前的信贷商业环境尚不支持预期损失模型的理论构建基础。预期损失模型仍未覆盖贷款利率风险的隐性损失,未来预期会被以公允价值为基础的贷款定价模型所取代。     

我国上市银行贷款损失准备分析

贷款损失准备不仅影响上市银行报告期的利润，而且影响上市银行未来吸收未预计损失的能力。我国现有上市银行在贷款损失准备计提范围、计提方法上存在较大差异，本文在对其进行比较分析的基础上，评价其合理性和贷款损失准备的充足性，并提出相关的政策建议。     

Banks’ procyclical behavior: Does provisioning matter?

A panel of 186 European banks is used for the period 1992–2004 to determine if banking behaviors, induced by the capital adequacy constraint and the provisioning system, amplify credit fluctuations. We find that poorly capitalized banks are constrained to expand credit. We also find that loan loss provisions (LLP) made in order to cover expected future loan losses (non-discretionary LLP) amplify credit fluctuations. By contrast, LLP used for management objectives (discretionary LLP) do not affect credit fluctuations. The findings of our research are consistent with the call for the implementation of a dynamic provisioning system in Europe.     

Countercyclical prudential buffers and bank risk-taking

We investigate the effects of countercyclical prudential buffers on bank risk-taking. We exploit the introduction of dynamic loan loss provisioning in Spain, mandating that banks use historical average loss rates in their estimation of loan loss provisions. We find that dynamic loan loss provisioning is associated with reductions in timely loan loss provisioning. Banks that previously recognized loan losses in a timely fashion exhibit the greatest reductions in timeliness and consequently extend loans to riskier borrowers with lower accounting quality. Our results have policy implications for the debate on the use of financial reporting requirements in mitigating capital pro-cyclicality.     

Provisioning over the business cycle: Some insights from the microfinance industry

This paper investigates the drivers of provisioning in MFIs and their provisioning behaviour over the business cycle. Based on an international sample of MFIs extracted from the MIX database over the 2001–2014 period, we uncover a negative relationship between MFIs' provisioning and the business cycle. Our finding corroborates the fact that MFIs do not build their loan loss provisions (LLP) during economic booms when profit and earnings are high. Since they provision more during downturns, they are more likely to suffer from unexpected losses and experience failure. This is in sharp contrast with the current Basel III countercyclical buffer requirement suggesting that financial institutions, especially banks, should build sufficient buffer in booms so that they can avoid costly capital adjustment when the economy contracts. Deeper analyses suggest however that this behaviour mainly concerns profit-oriented and deposit-taking/regulated MFIs, with business model and target close to conventional banking. This suggests that bank-like and regulated MFIs' loan loss provisions follow similar behavioral patterns to those of the conventional banking sector during the boom-and-bust cycles.     

On the Use of Portfolio Risk Models and Capital Requirements in Emerging Markets: The Case of Argentina

A portfolio-based model (CreditRisk+ of Credit Suisse FirstBoston) and recent Central Bank of Argentina credit bureau dataare used to estimate whether current capital and provisioningregulations match actual risks. Arguing that provisions shouldcover expected losses and that capital requirements should coverpotential losses beyond expected losses subject to some statisticallevel of tolerance, the article assesses how well actual capitaland provisioning requirements match the estimated requirementsgiven by the model. Actual provisioning requirements were foundto be close to implied levels of expected losses. The estimateof potential losses was found to be highly sensitive to theassumptions of the model, especially the parameter relatingthe volatility of a loan's rate of default to its mean value.This volatility parameter cannot be estimated accurately withthe credit bureau data because of the short time span covered,so proxy data were used to estimate it, and two values aroundthat estimate were tried. The difficulty of estimating thiscritical parameter implies that the results should only be regardedas suggestive. Moreover, the methodology only seeks to estimatecredit risk and not interest rate risk or exchange rate risk,nor does it fully take into account the indirect effects ofinterest rates and exchange rates on credit risk. As recentevents in Argentina have demonstrated, estimating credit riskalong these lines should be thought of as just one tool in attemptingto assess the appropriate level of bank provisions and capital.     

The procyclicality of loan loss provisions in Islamic banks

From a sample of Islamic banks around the world from 1997 to 2012, this paper examines whether loan loss provisioning in Islamic banks is procyclical. Our empirical findings highlight that loan loss provisioning in Islamic banks remains procyclical, although the ‘expected’ loan loss model (E-LLM) has been implemented for Islamic banks in several countries. A closer investigation further documents that Islamic banks also use loan loss provisions for discretionary managerial actions, especially related to capital management in which loan loss reserves and provisions are inflated when bank capitalization declines. Eventually, this paper highlights that higher capitalization can mitigate the procyclicality of loan loss provisions in Islamic banks. In other words, loan loss provisioning becomes countercyclical for Islamic banks with higher capitalization. This paper therefore casts doubts on the adoption of the E-LLM for Islamic banks to promote countercyclical effects, because the E-LLM may be influenced by managerial discretion, including opportunistic capital management using loan loss provisions that may undermine the importance of maintaining bank capitalization.     

Evidence of Forward-Looking Loan Loss Provisioning with Credit Market Information

Our paper makes a fundamental contribution by studying loan loss provisioning over the credit cycle as three distinct phases. Looking at the three distinct phases of the financial crisis – the pre-crisis period, crisis period, and post-crisis period – is important as loan loss provisioning is driven by different factors in each, in part due to extensive shifts in (or in the application of) regulatory rule. Controlling for credit market information using data from the Senior Loan Officer Opinion Surveys (SLOOS) we extend the work of previous studies of forward-looking loan loss provisions using the delayed expected loss recognition approach. We contribute to the growing literature on forward-looking loan loss provisioning and early in the cycle loss recognition by incorporating a broader range of available credit information and explicitly controlling for structural breaks in the sample corresponding to the financial crisis.     

Book Reviews

Following the financial and banking crisis of the late 2000s, accounting regulators sought to replace the incurred-loss method of loan-loss provisioning by a more forward-looking expected-loss method. Difficulties arose, including with respect to the weight that expected-loss provisioning should place on objective evidence of loss relative to evidence of a less specific and more judgemental nature. This paper provides evidence relevant to this issue by examining whether loan-loss provisioning by UK banks was less timely under the stricter evidence requirements of the IAS 39 incurred-loss regime implemented in 2005 than under the less strict evidence requirements of the previous UK incurred-loss regime. It does so by reference to the relationship in time between loan write-offs and loan-loss expense. The results do not suggest that provisioning became less timely under the stricter evidence requirements of IAS 39. There is no evidence that provisioning became less timely immediately prior to the crisis of the late 2000s. Also, there is no evidence that general provisioning, permitted under the pre-IAS 39 regime, enhanced the timeliness of loan-loss provisioning. The results do not suggest that stricter requirements regarding the evidence necessary to support recognition of loan losses have resulted in less timely loan-loss provisioning.     

Using Markov Chains to Estimate Losses from a Portfolio of Mortgages

Under Statement of Financial Accounting Standards Number Five, Accounting for Contingencies (SFAS 5), financial institutions record a provision for loan losses and establish loan loss reserves when impairment of a loan is probable and the loss can be reasonably estimated. Increasingly, Markov chain models are being used to estimate these losses. This paper develops and test the suitability and forecast accuracy of alternate Markov chain models of mortgage payment behavior using transition data from the Federal Home Loan Mortgage Corporation (Freddie Mac). In developing the models, the Freddie Mac transition data is examined to see if it satisfies the Markovian assumptions of stationary transition probabilities and homogenous payment behavior. The data examined in this paper did not satisfy these assumptions. With respect to accuracy in forecasting loan losses, the Markov chain approach, when incorporating recent information on transition probabilities, performed better than a random-walk model of loan losses.