Macroeconomic implications of expected loss impairment in banking regulation

动机在一家专业服务公司担任经济学家期间，我支持一家英国大型银行开发宏观计量经济学预测模型，以遵守新的IFRS 9会计准则。反思我的理科硕士论文(《英国固定事件宏观经济预测的效率和不偏不倚的分析》)，这个项目引发了人们的疑问，即用于遵守这一规定的预测中的潜在偏差或低效是否会对更广泛的金融体系和经济产生影响。背景和相关文献金融危机导致银行评估损失的方式发生了监管变化。新的国际财务报告准则第9号要求使用预期信贷损失法及早确认损失，这与以前的已发生损失法形成对比。实际上，IFRS 9要求银行在一系列宏观经济情景下计算预期信贷损失(ECL)的无偏概率加权估计。这是一个关键的研究领域，因为经济对大银行的依赖，正如2008年金融危机所表现的那样。管理者如何实施监管和制定预测的方法将牵涉到损失减值和可用信贷水平。关于贷款损失拨备和银行信贷水平对实体经济的影响存在争议：(当前)已发生损失方法被认为在泡沫破裂时迫使资本大幅减少(只有在违约概率=100%时才承认违约)，同时允许在繁荣时期过度放贷，从而助长了顺周期性。相反的论点包括，快速确认不良贷款为纠正行动提供了直接压力。例如，有证据显示招致损失方法对金融危机的严重程度并无重大影响。ECL方法的目的是抑制顺周期效应，因为及早确认信贷损失的能力应会减少累积的损失。在这种情况下，监管的目的是增强金融稳定。由于ECL更好地代表了贷款的经济价值，可以说它对银行财务报表的主要使用者(例如投资者、监管者)最有用。但是，模型实施的要素和预测的内在性质意味着ECL方法可能导致损失评估中存在某些不完善之处，例如：在预期损失的时间和计量方面的管理自由裁量权：IFRS 9有一个三阶段减值模型，这需要管理层对ECL的持续时间做出判断，以根据不同工具的风险水平确认(即12个月或终身)不同的工具。预测方法：许多银行没有完全准备好做出监管要求的“无偏”预测。例如，个别预测者(例如内部经济学家)已被证明存在偏见，没有充分利用公共信息(效率低下)，而城市和非城市预测者在使用信息方面存在差异。供探索的地区银行在确定ECL时使用的预测不完善的性质(例如方向、规模、来源)。从经验上看，这可以使用银行在年报中发布的预测和信用风险摘要。管理自由裁量权是否仅适用于某些类型的风险？例如，它是特定于贷款行业的，还是随着管理者的特点(例如经验)而变化。那么，这些缺陷是如何影响银行信贷水平，并随后影响经济的？金融稳定与银行贷款损失拨备之间的联系已被研究，但新的《国际财务报告准则》第9号条例为这一主题提供了一个新的背景。可以采用动态一般均衡模型来分析更广泛的经济影响。

MotivationWhilst working as an Economist for a professional services firm I supported a large UK bank in developing a macro-econometric forecasting model for the purposes of complying with the new IFRS 9 accounting rules. Reflecting on my MSc dissertation ('An Analysis of the Efficiency and Unbiasedness of UK Fixed-Event Macroeconomic Forecasts'), this project prompted questions on whether the potential biases or inefficiencies in the forecasts being used to adhere to this regulation could be having an impact on the wider financial system and economy.Background and relevant literatureThe financial crisis led to a regulatory change in the way that banks assess losses. The new IFRS 9 standard requires early recognition of losses using the expected credit loss approach, which is in contrast to the previous incurred loss approach. Effectively, IFRS 9 requires banks to calculate an unbiased probability-weighted estimate of expected credit loss (ECL) over a range of macroeconomic scenarios.This is a critical area for study because of the reliance placed on large banks by the economy, as played out by the 2008 financial crisis. How managers implement the regulation and the approach to producing forecasts will implicate both loss impairment and the level of available credit.There is debate on the impact of loan loss provisioning and the level of bank credit on the real economy:The (current) incurred loss approach is argued to contribute to procyclicality by forcing a sharp reduction in capital in the bust (default only recognised when probability of default = 100%), whilst enabling excessive lending in the boom.Converse arguments include the idea that fast recognition of non-performing loans provides immediate pressure for corrective action. E.g. there is evidence that the incurred loss approach did not contribute in a major way to the severity of the financial crisis.The ECL approach is intended to dampen procyclicality effects, as the ability to recognise credit losses earlier should reduce the build-up of losses. In which case, the regulation is intended to increase financial stability. As the ECL better represents the economic value of the loan, it can be argued to be of most use to main users of bank financial statements (e.g. investors, regulators).However, elements of the models implementation and the inherent nature of forecasting means that the ECL approach could result in certain imperfections being built into loss assessment, for instance:Managerial discretion over the timing and measurement of expected losses: IFRS 9 has a three-stage model for impairment, which requires managerial judgement on the length of ECL to recognise (i.e. 12 month or lifetime) for different instruments depending on their level of risk.Approach to forecasting: Many banks are not fully equipped to make 'unbiased' forecasts, as required by the regulation. For instance, individual forecasters (e.g. in-house economists) have been shown to have biases and do not fully use public information (inefficiency), whilst there are differences in the use of information by city vs. non-city forecasters.Areas for explorationThe nature of imperfections in the forecasts used by banks in determining ECL (e.g. direction, magnitude, origin). Empirically, this could use forecasts and credit risk summaries published by banks in annual reports.Does managerial discretion exist only for certain types of risks? E.g. is it specific to the industry of the loan, or does it vary with manager characteristics (e.g. experience).Then, how do these imperfections impact the level of bank credit, and subsequently the economy? The link between financial stability and bank loan loss provisioning has been studied, but the new IFRS 9 regulation provides a new context for this subject. A DSGE model could be implemented to analyse the wider economic implications.