This Selected Issues paper provides a real exchange rate and competitiveness assessment for Uruguay. It looks at the recent developments in key external competitiveness indicators such as the bilateral real effective exchange rates, export volumes, export market shares, export unit values, unit labor costs as well as foreign direct investment performance. The paper pursues an assessment of the real exchange rate following a broad-based strategy of applying four different approaches, including the purchasing power parity approach, the macroeconomic balance approach, the external sustainability approach, and the equilibrium real exchange rate approach.


This Selected Issues paper provides a real exchange rate and competitiveness assessment for Uruguay. It looks at the recent developments in key external competitiveness indicators such as the bilateral real effective exchange rates, export volumes, export market shares, export unit values, unit labor costs as well as foreign direct investment performance. The paper pursues an assessment of the real exchange rate following a broad-based strategy of applying four different approaches, including the purchasing power parity approach, the macroeconomic balance approach, the external sustainability approach, and the equilibrium real exchange rate approach.

IV. Dynamic Loan Loss Provisioning in Uruguay1

A. Background

1. The fallout from the global financial crisis has raised concerns about procyclicality in banking. Procyclicality includes backward-looking loan loss provisioning rules that do not recognize the buildup of credit risks in boom phases and thus fail to provide incentives against excessive risk-taking. Procyclical lending and provisioning occurs when a period of high credit demand and lax lending standards is followed by a downturn triggering a rise in non-performing loans and specific loan loss provisions. Empirical evidence shows that credit risks build up during an upswing (Jimenez and Saurina, 2006) and that banks postpone provisioning during upswings until lending conditions deteriorate (Cavallo, Majnoni, 2001; Laeven and Majnoni, 2003). This belated recognition of loan losses coupled with tightened lending policies may then lead to a credit crunch (Bikker and Metzemakers, 2005). Financial institutions and their regulators alike have come to realize that backward-looking provisioning rules do not adequately recognize the build-up of credit risks during expansionary phases and thus fail to provide the right incentives for prudent loan origination.

2. Dynamic loan loss provisioning is an instrument to mitigate procyclicality in lending and provisioning. The basic idea is to require banks to make provisions against loans outstanding in each period in line with the estimate of long-run expected loan loss rather than actual loss (Mann and Michael, 2002). During an economic upswing, the stock of dynamic provisions grows rapidly as loan origination is high and loan losses are typically low. The reverse is true during economic slowdowns, and additional provisions for loan losses are covered by drawing on the stock of dynamic provisions. Once the stock of dynamic provisions has reached a sufficiently high level, the monthly charges for provisions should become effectively independent of actual loan losses, with the dynamic provisioning rate equaling the expected average loss rate of the loan portfolio.2 Hence, dynamic provisioning has a profit smoothing property.

3. Reaping the merits of dynamic provisioning requires careful calibration. While it gives incentives for banks to extend loans more carefully due to higher reserves on performing loans, dynamic provisioning is not able to prevent credit bubbles by itself (Brunnermeier et al., 2009), as this might require prohibitively high provisioning rates. Generally, provisioning rates need to be set according to the loan default history spanning a full credit cycle in an attempt to avoid over- or underprovisioning of eventual loan losses. Miscalibration of dynamic provisioning rates either causes an excessive burden on banks or leads to an insufficient cushion in a crisis.

4. This study assesses the size of dynamic provisions in Uruguay and compares the Uruguayan system to that of Spain and Peru. The paper first seeks to determine the magnitude of macroeconomic shocks necessary to exhaust Uruguayan banks’ stock of dynamic provisions, using the credit risk model of the Banco Central del Uruguay (BCU). Second, it simulates how the buildup of dynamic provisions would have evolved, had the Spanish and the Peruvian provisioning formulas been applied to the Uruguayan dataset of dynamic provisions.

5. The main results of the simulations can be summarized as follows. The present stock of dynamic provisions would suffice to fully absorb a medium-sized shock, obviating the need to make additional loan loss provisions. Moreover, the alternative dynamic provisioning formulas result in distinct accumulation paths that in part correspond better to the idea of having dynamic provisions vary throughout the credit cycle.

B. Uruguay’s System of Dynamic Provisioning

6. Uruguay introduced dynamic loan loss provisioning in September 2001, following the Spanish model launched one year earlier.3 The regulation4 specifies that banks contribute to their individual provisioning funds the difference between the monthly statistical losses on loans to the non-financial private sector and the realized net loan loss in that month. The statistical losses are calculated by multiplying 1/12 of the expected rate of loss for five loan categories,5 βi, ranging from 0.1 percent for low-risk loans to 1.8 percent for credit card loans by the respective loan volumes, Ci. Formally,


where Dyn. Pt represents the contribution to the dynamic provisions fund and LLt is the net loan loss6 incurred in the current month. The dynamic provisions fund of each bank is bounded between 0 and 3 percent of total provisionable loans.

7. The countercyclical system took effect toward the end of the previous credit cycle. As a result, when the financial crisis of 2002/03 hit, the small cushion of dynamic provisions could only absorb a fraction of the staggering loan losses (see Chart 1). During the crisis, the dynamic provisions funds remained more or less depleted. With the subsequent recovery, however, the overall stock of dynamic provisions quickly approached the 3 percent limit, reaching it in June 2009 at the system level.

Chart 1.

Bank Loans and Dynamic Provisions

Note: All graphs exclude Banco Hipotecario del Uruguay (BHU).

8. During the post-crisis period, the accumulation of dynamic provisions was m possible by the decline in loan delinquencies. Lower loan losses, including recovery of loans already written off, and reclassification of loans toward better categories contribute the drop in specific provisions. Chart 2 depicts the evolution of specific and dynamic provisions since early 2005, after the catching-up in dynamic provisions had abated. Dynamic provisions rose faster that they would have if net loan recoveries had been zero throughout (dotted line).

Chart 2.

Evolution of Specific and Dynamic Provisions


C. The Sufficiency of Dynamic Provisions under Macroeconomic Shocks

9. Using the credit risk model of the BCU, the loan portfolios of the 13 Uruguayan banks are subjected to a set of macroeconomic shocks. These shocks are set to produce default rates and consequently loan losses (assuming a fixed loss given default) that will exhaust the individual stocks of dynamic provisions in place. The credit risk model has the following main input variables:7 (i) the rate of GDP growth, (ii) the Uruguayan Peso-US Dollar exchange rate, and (iii) the Uruguayan Bond Index (UBI).8 Different credit risk models are in place for peso and for dollar loans, and the BCU routinely applies an adverse scenario and a crisis scenario. Here, the level of additional loan losses is set such that it depletes each bank’s stock of dynamic provisions. The target level of loan losses determined this way, the model is then solved backward for the set of shocks that will produce exactly such losses.

10. In the exercise, the three variables are assumed to co-move in line with historic evidence. For every additional percentage point in negative GDP growth relative to the adverse (baseline) scenario of the BCU, the exchange rate is set to depreciate by 0.6 percentage point and the bond spread to rise by 30 basis points. This variation in the exchange rate is based on its correlation with GDP growth during the 2002-09 period, while for the Uruguayan Bond Index the implied correlation of the two variables between the adverse and crisis scenario of the BCU is used.9

11. The simulation outcome shows that the banks could withstand a relatively severe shock without having to make additional provisions impairing capitalization (Table 1). On average, the set of shocks that will deplete the stock of dynamic provisions consists of a 5 percent drop in economic activity, an exchange rate depreciation of about 15 percent, and a rise in the UBI to slightly above 800 basis points (250 b.p. higher than in June 2009). Having generally attained the maximum level of their dynamic provisions funds, it is reassuring that 85 percent of banks are within one standard deviation of the size of the average shock.

Table 1.

Set of Shocks Depleting the Stocks of Dynamic Provisions

article image

as of July 2009. For technical reasons, the stock of provisions may temporarily exceed the 3 percent limit.

12. Using the BCU’s standard stress test scenarios illustrates that full coverage is ensured under the weaker set of shocks (Table 2). While banks’ dynamic provisions would fully cover the additional loan losses predicted by the adverse scenario, the rate of coverage in the crisis scenario and under a shock corresponding to the 2002-03 financial crisis is only 41 and 13 percent, respectively.

Table 2.

Coverage of Expected Loan Losses under Different Stress Test Scenarios

article image

Increase = depreciation of the local currency.

Uruguayan Bond Index; ┼ millions of US dollars

Nonetheless, in view of Uruguay’s relatively favorable performance during the current global economic crisis (only one quarter of moderately negative GDP growth), the cushion afforded by the stock of dynamic provisions can be regarded as comfortable, and possibly on the high side in view of the consistently low loan losses.

D. A Comparison with the Spanish and Peruvian Systems

13. The size of the dynamic provisions funds has for years converged toward the limit of 3 percent, reaching it at the system level in June 2009 (Chart 1). However, the basic idea of dynamic provisioning is that the stock of dynamic provisions should diminish during an economic slowdown associated with stagnant or falling credit. In early 2009, when both GDP and credit growth in Uruguay temporarily turned negative, dynamic provisions did not fall significantly. In the end, Uruguay avoided a recession, with the economy starting to grow again in the second quarter, and consequently the credit cycle did not fully come to a close, with nonperforming loans barely rising. This said, to ascertain whether a different formula or parameters would have produced a different evolution in the buildup of these provisions (and a possible drawdown on them), we apply to the Uruguayan data the dynamic provisioning formulas used in Spain and in Peru.

14. The Spanish formula10—in place since July 2000—is conceptually similar in that it nets required provisions and impaired loans, but it has two diverging elements. First, specific provisions rather than net loan losses are subtracted from the required contribution to the dynamic provisions fund. Second, as the formula computes overall general provisions,11 it features a component requiring banks to provision between 0 and 2.5 percent (depending on the loan risk) of the monthly increment in provisionable loans (the “alpha” part) in addition to the countercyclical component (the “beta” part, with contribution rates ranging from 0 to 1.64 percent):


where αi is the latent loss rate expected in a cyclically neutral year for loans in risk category i; ΔCit is the change in the stock of loans in risk category i in the current period t; βi is the average rate specific provision for loans of category i, ideally based on a full lending cycle; and Spec. Pt is the specific provision made in the current period. The additional alpha component leads to a quicker buildup of dynamic provisions during an upswing but also to stronger downward pressure on such provisions whenever credit growth turns negative. The Spanish fund is capped at 1.5 percent of provisionable loans—half the Uruguayan limit.

15. To apply the Spanish formula, the magnitude of the expected losses during a cyclically neutral period has to be calibrated. In the simulation, the alpha parameters are set to be 0.1 percentage points higher than the Uruguayan beta parameters, which at their inception were calibrated based on the average annual loan loss of 1 percent during 1990-2000. The increment is predicated on the average loan loss rate of 1.1 percent recorded over the credit cycle of 2001-2008.12 To make the two formulas operationally comparable, the specific provisions include changes in defaulted loans to correct for declines in provisions caused by write-offs and thus unrelated to upward reclassifications of loans.

16. Introduced in November 2008, the Peruvian formula differs substantially from the other two concepts. It does not feature a cumulative fund (i.e. one that is built gradually over time). Further, the “procyclical” element only enters into effect if GDP growth rises above a certain threshold.13 In the non-activation period, banks maintain a stock of general provisions of between 0.7 and 1.0 percent of loans (again, depending on the risk category of loans), to which during the activation period between 0.3 and 1.5 percent of loans is added. Once the procyclical component has been deactivated during an economic slowdown, banks are allowed to offset rising specific provisions against the stock of general provisions until the prescribed level of provisions during the non-activation period is reached again. In the simulation, the activation period begins in October 2003, after a 2.8 percent increase in GDP over the previous four quarters.14 The surcharges applied to the Uruguayan beta parameters are 1 percent for consumer loans, 1.5 percent for credit card loans, and 0.5 percent for other non-guaranteed loans.

17. Imposing the properties of the two alternative formulas yields different paths for the stock of Uruguay’s dynamic provisions.

  • The Spanish formula, while tracing the buildup of dynamic provisions closely, exhibits greater swings due to abrupt changes in specific provisions (strong increase from early 2005 through early 2006, and temporary drops in mid-2006 and late 2008). Thanks to the inclusion of the alpha component linked to the change in credit volume, the increment in the stock during 2007-08 when credit growth accelerated is noticeably higher under the Spanish formula.

  • The Peruvian formula yields a smoother path than the other two methods since it abstains from subtracting loan losses or specific provisions. The stock of provisions jumps in October 2003 when the procyclical component is activated, and thereafter traces the actual Uruguayan provisions closely. The latter is a coincidence because the additional contribution due to the higher beta parameters is offset by the absence of a fall in specific provisions from 2005 (which is not incorporated in the Peruvian formula).

  • Additionally, a hybrid version of the Uruguayan and Spanish formulas is constructed, which joins the additional alpha component to the Uruguayan methodology (fine dotted line). The stock of dynamic provisions reconstituted accordingly, the hybrid system makes the stock of provisions rise faster in times of high credit growth (as does the Spanish formula), while the stock also falls more quickly when credit shrinks, as was the case in early 2009 (this can be seen from a slightly declining distance between the two graphs after the peak in December 2008).

Chart 3.

Stock of Dynamic Provisions Under Different Formulas


18. The simulation outcome allows to draw a number of conclusions. The Uruguayan formula yields a path that is less volatile than Spanish formula (at least when applying the Spanish formula in the post-crisis period) and produces smaller stocks than under Peruvian formula which prescribes a minimum level of general provisions at all times. However, to the extent that high credit growth is a precursor of emerging loan impairment, the additional alpha component of the Spanish formula does have a place, as is illustrated by the hybrid system. The alpha part also helps lower the stock of dynamic provisions during a downturn and thus align them with the credit cycle, even if loan delinquencies do not rise markedly.

E. Conclusions

19. This paper finds that the current stock of Uruguay’s dynamic provisions is capable of cushioning a medium-sized shock. As the stocks of most banks are at their regulatory limit, it would take a veritable crisis situation for banks to experience loan losses that can no longer be covered by their dynamic provisions. Thus, in terms of safeguarding financial stability the cushion afforded by Uruguay’s dynamic provisions is arguably large enough.

20. In addition, the paper shows that alternative formulas used in Spain and Peru produce diverging accumulation paths. In part, the results implied by these formulas conform better with the credit cycle, not least due to featuring properties that are more directly linked to credit growth. Adding such an element to the Uruguayan formula would provide greater variability of dynamic provisions over the credit cycle.

21. The exercises are unable to answer the question whether the stocks produced by any of the formulas are adequate. Obviously, there is a tradeoff between safeguarding financial stability at all times, suggesting ample provisions, and the efficiency of the banking system that hinges on reasonable loan loss provisions. In the case of Uruguay, the jury is still out since the credit cycle either has not fully come to a close yet in view of only slightly rising loan delinquencies. This said, the question arises whether the magnitude of loan losses during the 2002-2003 may reoccur or whether milder shocks are to be expected thanks to Uruguay’s strides in modernizing bank regulation (see Adler et al., 2009).

22. To the extent that future downturns in the banking sector are less severe than past crises, the system may be overprovisioned. For example, arbitrarily cutting the rate of loan losses during the 2002-03 crisis in half would yield an average loan loss over the cycle of 0.4 percent—below the assumed loss rate of 1 percent. This ties in with the finding that the stock of dynamic provisions could withstand a sizable shock before having to allow additional losses. However, it seems too early to reach a firm conclusion on this, and the authorities should continue to reassess their system to ensure that the stock of provisions is in line with potential credit risks looking forward.


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Prepared by Torsten Wezel.


For numerical examples see Mann and Michael (2002).

Dynamic provisioning systems were subsequently adopted by Colombia in 2007, and by Bolivia and Peru in 2008.


For details on the current provisioning system see Banco Central del Uruguay (2008).


The five loan categories and the corresponding provisioning rates are: A. loans with public sector guarantees (0.1 percent), B. loans with other guarantees (0.5 percent), C. other loans (1.1 percent), D. consumer loans (1.4 percent), and E. credit card loans (1.8 percent).


The net loan loss is calculated as loan losses net of deactivations of specific provisions and recoveries of written off loans.


In addition, the BCU credit risk model includes the unemployment rate, the level of foreign interest rates, as well as the inflation rate. For ease of optimization, these variables were kept constant in the optimization process.


The UBI measures the spread of a portfolio containing several US denominated Uruguayan international bonds of different maturities with respect to comparable US Treasury bond yields.


Basing the variation on the correlation between GDP and the Bond Index for 2002-09 (-0.6) would introduce extreme dynamics in the model. The assumed rate of correlation of -0.3 corresponds to the historic correlation found for the post-crisis period (2004-09).


For a detailed description of the Spanish system see Fernandez de Lis et al. (2000) and Saurina (2009).


With Spain’s adoption of International Financial Reporting Standards in 2005, the formulas for computing general and dynamic provisions was merged into one (Saurina, 2009).


The Spanish formula prescribes to take the rate of loan loss in a cyclically neutral year, which would be 2007 when the output gap closed. However, due to high loan recoveries the loss rate in that year was actually negative (-0.5%). Thus, we deviate from the Spanish methodology and take the average loss rate over the cycle.


The procyclical component is activated [deactivated], if either the average annualized rate of GDP growth has been above [below] 5 percent in the past 30 months or the change in GDP growth has been greater than 2 percent [-4 percent] in the past 12 months. For more details, see Superintendencia de Banca, Seguros y AFP Peru (2008).


The procyclical phase would likely come to an end in the third quarter of 2009 given that the average GDP growth rate has fallen by 3.8 percent during the 12 months ending in June 2009.