Wednesday, May 2, 2012

Dynamic Loan Loss Provisioning: Simulations on Effectiveness and Guide to Implementation

Dynamic Loan Loss Provisioning: Simulations on Effectiveness and Guide to Implementation. By Torsten Wezel, Jorge A. Chan Lau, and Francesco Columba
IMF Working Paper No. 12/110
May 01, 2012

Summary: This simulation-based paper investigates the impact of different methods of dynamic provisioning on bank soundness and shows that this increasingly popular macroprudential tool can smooth provisioning costs over the credit cycle and lower banks’ probability of default. In additon, the paper offers an in-depth guide to implementation that addresses pertinent issues related to data requirements, calibration and safeguards as well as accounting, disclosure and tax treatment. It also discusses the interaction of dynamic provisioning with other macroprudential instruments such as countercyclical capital.



Reducing the procyclicality of the banking sector by way of macroprudential policy instruments has become a policy priority. The recent crisis has illustrated how excessive procyclicality of the banking system may activate powerful macro-financial linkages that amplify the business cycle and how increased financial instability can have large negative spillover effects onto the real sector. Moreover, research has shown that crises that included banking turmoil are among the longest and most severe of all crises.

Although there is no consensus yet on the very definition of macroprudential policy, an array of such tools, especially those of countercyclical nature, has been applied in many countries for years. But it was only during the financial crisis that powerful macro-financial linkages played out on a global scale, conveying a sense of urgency.

In the wake of the crisis, policymakers therefore intensified their efforts to gear the macroprudential approach to financial stability towards improving banks’ capacity to absorb shocks—a consultative process that culminated in the development of the Basel III framework in December 2010 to be phased in over the coming years. In addition to improving the quality of bank capital and liquidity as well as imposing a minimum leverage ratio, this new regulatory standard introduces countercyclical capital buffers and lends support to forward-looking loan loss provisioning, which comprises dynamic provisioning (DP).

The new capital standard promotes the build-up of capital buffers in good times that can be drawn down in periods of stress, in the form of a capital conservation requirement to increase the banking sector’s resilience entering into a downturn. Part of this conservation buffer would be a countercyclical buffer that is to be activated only when there is excess credit growth so that the sector is not destabilized in the downturn. Such countercyclical capital has also been characterized as potentially cushioning the economy’s real output during a crisis (IMF, 2011). Similarly, dynamic provisioning requires banks to build a cushion of generic provisions during an upswing that can be used to cover rising specific provisions linked to loan delinquencies during the subsequent downturn.

Both countercyclical capital and DP have been applied in practice. Some countries have adjusted capital regulations in different phases of the cycle to give them a more potent countercyclical impact: Brazil has used a formula to smooth capital requirements for interest rate risk in times of extreme volatility, China introduced a countercyclical capital requirement similar to the countercyclical buffer under Basel III, and India has made countercyclical adjustments in risk weights and in provisioning. DP was first introduced by Spain in 2000 and subsequently adopted in Uruguay, Colombia, Peru, and Bolivia, while other countries such as Mexico and Chile switched to provisioning based on expected loan loss. Peru is the only country to explicitly use both countercyclical instruments in combination.

The concept of DP examined in this paper is intriguing. By gradually building a countercyclical loan loss reserve in good times and then using it to cover losses as they arise in bad times, DP is able to greatly smooth provisioning costs over the cycle and thus insulate banks’ profit and loss statements in this regard. Therefore, DP may usefully complement other policies targeted more at macroeconomic aggregates. The implementation of DP can, however, be a delicate balancing exercise. The calibration is typically challenging because it requires specific data, and even if these are available, it may still be inaccurate if the subsequent credit cycle differs substantially from the previous one(s) on which the model is necessarily predicated. Over-provisioning may ensue in particular instances. This said, a careful calibration that tries to incorporate as many of the stylized facts of past credit developments as possible goes a long way in providing a sizeable cushion for banks to withstand periodic downswings.

This paper provides strong support for DP as a tool for countercyclical banking policies. Our contribution to this strand of the literature is threefold. We first recreate a hypothetical path of provisions under different DP systems based on historical data of an emerging banking market and compare the outcome to the actual situation without DP. These counterfactual simulations suggest that a well-calibrated system of DP mitigates procyclicality in provisioning costs and thus earnings and capital. Second, using Monte-Carlo simulations we show that the countercyclical buffer that DP builds typically lowers a bank’s probability of default. Finally, we offer a guide to implementation of the DP concept that seeks to clarify issues related to data requirements, choice of formula, parametrization, accounting treatment, and recalibration.

Other studies that have used counterfactual simulations based on historical data to assess the hypothetical performance under DP include Balla and McKenna (2009), Fillat and Montoriol- Garriga (2010), both using U.S. bank data, and Wezel (2010), using data for Uruguay. All studies find support for the notion that DP, when properly calibrated, can help absorb rising loan losses in a downturn and thus be a useful macroprudential tool in this regard. Some other studies (Lim et al., 2011; Peydró-Alcalde et al., 2011) even find that DP is effective in mitigating swings in credit growth, although this should not be expected of DP in general.


This paper has provided a thorough analysis of the merits and challenges associated with dynamic provisioning—a macroprudential tool that deserves attention from policymakers and regulators for its capacity to distribute the burden of loan impairment evenly over the credit cycle and so quench an important source of procyclicality in banking. Our simulations that apply the Spanish and Peruvian DP formulas to a full cycle of banking data of an advanced emerging market leave little doubt that the countercyclical buffer built under DP not only smoothes costs but actually bolsters financial stability by lowering banks’ PD in severe downturn conditions. We also show that for best countercyclical results DP should be tailored to the different risk exposures of individual banks and the specific circumstances of banking sectors, presenting measures such as bank-specific rates or hybrid systems combining the virtues of formulas.

While the simple concept of providing in good times for lean years is intuitive, it has its operational challenges. When calibrating a DP system great care must be taken to keep countercyclical reserves in line with expected loan losses and so avoid insufficient buffers or excessive coverage. As many of the features and needed restrictions are not easily understood or operationalized, we offer a comprehensive primer for regulators eager to implement one of the variants of DP analyzed in the paper. The discussion of practical challenges also includes thorny issues like compliance with accounting standards. In fact, policymakers have long tended to dismiss DP on grounds that it is not legitimate from an accounting perspective and therefore focused on other tools such as countercyclical capital. To remedy this problem, we propose ways to recalibrate the formula periodically and so keep it in line with expected loan loss. Further, while recognizing that countercyclical capital has its definite place in the macroprudential toolkit, we argue that DP acts as a first line of defense by directly shielding bank profits, thereby lowering the degree to which other countercyclical instruments are needed. However, there should be no doubt that due to the limited impact of DP in restraining excessive credit growth complacency in supervision due to DP buffers should be avoided and that DP needs to be accompanied by other macroprudential tools aimed at mitigating particular systemic risks.

Clearly, further research is needed on the interaction between DP and countercyclical capital as well as other macroprudential tools to answer the question in what ways they can complement one another in providing an integrated countercyclical buffer. As an early example, Saurina (2011) analyzes DP and countercyclical capital side-by-side but not their possible interaction. Another area of needed research is the impact of DP on credit cycles and other macroeconomic aggregates. Newer studies (e.g., Peydró-Alcalde et al., 2011; Chan-Lau, 2012) evaluate the implications of DP for credit availability, yet broader-based results are certainly warranted. The ongoing efforts by a number of countries towards adopting DP systems and other forms of forward-looking provisioning will provide a fertile ground for such future research.