Selected Issues


Selected Issues

Estimating a Financial Conditions Index for Mauritius1

Financial Conditions Indices (FCIs) have become a widely-used instrument to gauge the operational state of the financial sector and to act as a reliable predictor for real economy activity. Constructing a country-specific FCI for Mauritius—which reflects both external and domestic financial conditions—the results show that the FCI tracks economic activity well and is a leading indicator of real GDP growth. This highlights the usefulness of an FCI as a potential forecasting tool for the Mauritian economy and also as an instrument for macroprudential policy, notably in calibrating the Basel III countercyclical capital buffer.

A. Introduction

1. This paper develops a Financial Conditions Index (FCI) for Mauritius—an instrument to gauge the operational state of the financial sector and predict real economy activity. Mauritius is a highly financially-integrated economy, with the financial sector playing an increasingly important role in generating economic activity and employment. With the transition from traditional agriculture and manufacturing activities toward provision of financial services to both residents and nonresidents, Mauritius has become a well-known global financial center.

2. The evolution of Mauritius’ financial services sector has been supported by a vibrant offshore corporate sector. The offshore global business sector is comprised of “global business companies” (GBCs), with assets under management of over 50 times GDP. As documented in IMF Country Report 16/89, there are important linkages between banks and GBCs—both in the form of deposits from GBCs and loans to GBCs. While banks typically do not intermediate the potentially volatile GBC deposits (about 40 percent of total deposits), preferring to retain them as a liquidity buffer instead, this type of relatively inexpensive funding buttresses banks’ profitability and frees up traditional funding for lending. In this respect, the two-way linkage between the global business sector and the financial sector affects the entire economy.

3. Given the strong macro-financial linkages, it is imperative to closely monitor domestic financial developments. Financial developments are broader than monetary developments depicting money supply and interest rates. Indeed, a financial conditions index is a logical extension of the monetary conditions index (MCI) that became popular in the 1990s. Subsequently, researchers increasingly included financial market variables such as asset prices and long-term interest rates as well as liquidity indicators, particularly following the Global Financial Crisis (GFC), giving rise to the construction of explicit financial condition indices. FCIs can be a better indicator of financial conditions than traditional MCIs, for example, in times of financial stress (such as after the GFC), when despite monetary loosening, overall financial conditions remained tight as lending rates did not decrease much reflecting banks’ unwillingness to lend due to balance sheet constraints (Manning and Shamloo, 2015). The purpose of an FCI can be to assess whether financial conditions are lax or tight, or it can be used as a forecasting instrument summarizing the impact of financial factors on economic activity (Hatzius et al., 2010). While individual financial variables may also be good predictors, the pooling of financial information has been shown to improve predictive power (Hatzius et al., 2010).

4. A burgeoning literature exists on FCIs. The literature on FCIs has flourished significantly after the global financial crisis, and covers both advanced economies—such as Canada (Gauthier et al., 2004), the US (Swiston, 2008), Greece (Manning and Shamloo, 2015) and France (Kongsamut et al. 2017)—as well as emerging market and transition economies—e.g. Brazil (Krznar and Matheson, 2017), China (Guihuan and Yu, 2014), Colombia (Gómez et al., 2011), India (Khundrakpam et al., 2017), Malaysia (Abu Bakar and Badrudin (2017), Poland (Ho and Lu, 2013), and Turkey (Kara et al., 2012). Some studies also construct regional FCIs, e.g., for Asia (Osorio et al., 2011), the Euro Area (Angelopoulou et al., 2013), and Central and Eastern Europe (Auer, 2017). In the context of sub-Saharan Africa, Gumata et al. (2012) have developed an FCI for South Africa.

5. In general, tests of the FCIs corroborate the hypothesis that financial conditions matter for future economic growth. In- and out-of-sample tests generally show good predictive power of FCIs, which has made them a popular tool for forecasting, including by policymaking institutions. The IMF, e.g., has been using FCIs among other inputs to project the “growth-at-risk”—both at the global level (IMF, 2017; IMF, 2018a), and for individual countries (e.g. IMF, 2018b).

6. Two approaches stand out in constructing FCIs. First, the weighted-sum approach based on vector autoregressive (VAR) models obtains the weights of the individual financial variables in the FCI from the cumulative impulse-response functions. Second, the common factor approach, typically estimated through Principal Components Analysis (PCA) models the variance structure of the financial variables using optimal linear combinations of them. Occasionally, other methods such as common factor analysis using a Kalman filter (Gumata et al., 2012) or semi-structural models (Krznar and Matheson, 2017) are employed. The VAR approach has the advantage of linking financial conditions and GDP as the variable of ultimate interest in a system of equations but may present econometric challenges, while the PCA allows for inclusion of ample financial variables but is, by construction, agnostic about the relationship to output (Ho and Lu, 2013)2 despite having been found to predict future growth well and occasionally outperforming leading indicators (Gumata et al., 2012).

7. Financial variables to be included fall into several categories reflecting prices, quantities and risk factors (e.g., Kongsamut et al., 2017). Variables that are almost always incorporated in FCIs comprise a market interest rate such as for private sector loans, the nominal or real effective exchange rate, asset prices having wealth effects such as stock prices and their volatility as well as house prices, and risk factors such as credit or bonds spreads, both within the economy and to the exterior. More rarely, monetary variables like money aggregates or bank reserves are accounted for. Where available, the information content of loan officer survey of credit standards has been exploited as well (Swiston, 2008, Hatzius et al., 2010, Angelopoulou et al., 2013, Ho and Lu, 2013). The variable set included in the FCI for South Africa (Gumata et al., 2012) is particularly relevant for variable selection in this study.3

8. Typically, the FCI is purged from effects of real sector developments. To measure the pure impact of financial variables on economic activity and exclude feedback from past economic events on the former, almost all studies purge the FCI by regressing either the individual (raw) variables on GDP growth and inflation or the readily-constructed FCI on these variables and utilizing the residuals for the purged FCI. Some studies (e.g. Hatzius et al., 2010) purge even from monetary conditions by including the policy rate among the regressors. The financial variables are typically demeaned and occasionally also divided by their standard deviation to remove the influence of the unit of measurement (as in Khundrakpam et al., 2017). Sometimes a moving average of the indicator is used to reduce excessive short-term volatility and focus on more persistent deviations (Gómez et al., 2011; Manning and Shamloo, 2015).

9. This study computes VAR- and PCA-based FCIs in their purged and unpurged variants and uses them for growth prediction and macroprudential policy purposes. Upon obtaining the different FCIs, the study then runs in-sample and “pseudo out-of-sample” forecasts to test the predictive quality of the FCIs for future economic activity. Introducing a new aspect in the literature, the study uses the computed FCIs for macroprudential policy purposes, notably as a complement to the prevailing credit-to-GDP gap in the setting of the countercyclical capital buffer (CCB) for banks, which Mauritius plans to introduce as part of its adoption of the Basel III reform package.

10. The paper is organized as follows. Section two presents methodological aspects surrounding FCI construction and provides information on the data used and variable selection. Section three presents the estimation results, notably the evolution of the various indices since the mid-2000s in relation to GDP growth. Section four tests whether the new FCI is a good predictor of short-run economic activity using in-sample forecasts. It also assesses the usefulness of the FCIs for detecting boom-bust episodes in Mauritius and for informing macroprudential policy, examining whether an FCI could be used as a trigger in activating the Basel III countercyclical capital buffer. Section five concludes with some policy recommendations.

B. Methodology

11. The variables used in this paper to construct the FCIs consist of global and domestic factors. Global variables represent external financial conditions that would likely affect the Mauritian economy through the exposure of its international financial center to China, India, Europe and the US. Domestic variables capture the various channels through which monetary policy affects the real economy. The global factors are the JP Morgan Emerging Market Bond Index (EMBI), the US three-month Treasury Bill rate (US TBILL), and the China and Emerging Markets (EMS) Morgan Stanley Capital International indices (China and EMS MSCIs). The domestic factors are nominal effective exchange rate (NEER), the index of the Mauritius stock exchange (SEMDEX), the average lending rate and the growth rate of credit to the private sector.4 We initially considered more global and domestic factors but decided to settle on those factors with the highest, and significant, correlation coefficient with Mauritius’ real GDP growth. Also, many of the excluded global (domestic) factors are highly correlated with the selected global (domestic) factors. The sample spans 2002Q3–2018Q2.

12. To construct the FCI for Mauritius, we use the following methodologies: Weighted-sum approach using Vector Auto-Regression (VAR) and factor analysis approach using Principal Component Analysis (PCA).

Vector Auto-Regression (VAR)

13. VAR modelling allows obtaining the weights of the individual financial variables in the FCI. The weights are obtained from the cumulative impulse-response functions of GDP growth to a one standard deviation shock to each of the variables. The VAR approach has the advantage of relating financial conditions directly with GDP developments. However, the list of covariates may empirically be restricted by the degrees of freedom, whereby having too many variables or rather short-time series runs the risk of overfitting. As a result, only a few financial variables are typically included.

14. To construct the weighted-sum FCI, a recursive VAR model consisting of the eight variables plus annualized quarterly real GDP and the consumer price index (CPI) is estimated. The inclusion of GDP and CPI takes into account the impact of current and past economic activity on financial conditions. The derived FCI is therefore stripped of the feedback from current and past economic activity. The identification of structural shocks is achieved through a Cholesky decomposition, which assumes that domestic financial conditions do not have contemporaneous effects on growth and inflation, and that domestic developments (real and financial) do not contemporaneously affect external variables. Specifically, we employ the following Cholesky ordering: US TBILL, JPM EMBI, MSCI EMS, MSCI China, GDP growth, consumer price index, SEMDEX, NEER, lending rate, and credit to the private sector.5

15. Augmented Dickey-Fueler tests confirm that all variables except the lending rate non-stationary. The US TBILL is first-difference stationary. The rest of non-stationary variables (EMBI, China and EMS MSCIs, SEMDEX, NEER and credit to the private sector) enter the VAR as year-on-year percentage change while the lending rate enters in levels, and the US TBILL enters in first difference.

16. The weighted-sum FCI is then calculated as follows:


The FCI in each period t is a weighted average of the k different financial variables (in this case k=8) denoted xjt, where wj is the weight and uj is the mean of the financial variable over the sample period.6 The weight wj is the cumulative two-quarter impulse response of real GDP growth to a one-unit shock to fjt.

Principle Component Analysis (PCA)

17. Principal Components Analysis models the variance structure of the financial variables. This is achieved by using optimal linear combinations of the observed financial variables, i.e. the principal component accounts for a maximum amount of the variables’ total variance.7 As mentioned, the PCA typically allows for inclusion of more financial variables compared to the VAR approach. However, constructing FCIs using PCA is done without explicit regard for the impact on economic activity—it may be that a variable has a large factor loading implying that the variable explains most of the common factor’s variance but irrespective of whether the variable matters for growth in a given case. Also, including too many similar indicators runs the risk of giving too much weight to a certain set of drivers, which, in addition, may be less relevant for future economy activity. To mitigate this problem and thus safeguard a parsimonious specification, only one variable depicting developments in a given area (e.g. stock market developments, bond spreads) is chosen.

18. The principal component methodology is used to extract common factors (Ft) that represent the greatest common variation in a group of k financial variables (Xt). The model can be presented as follows:


Where Xt is a k × 1 vector of variables’ means, μ is the mean of the observables over the sample period, β is a k × m matrix of coefficients, and Ft is a vector of m × 1 unobserved common factors, Ut is a k × 1 vector of errors. The model assumes that the errors are orthogonal to the common factors, which in turn are assumed to have mean zero.

19. The factor-based FCI includes both domestic and global financial variables, as in the VAR framework (except GDP and inflation). It is derived from PCA-based common factors calculated for the period of 2003Q3–2018Q2 using the eight global and domestic financial variables. Two sets of common factors are calculated. One using the static approach and the second using the dynamic approach. To introduce dynamics into the common factors, we add lags of each financial variable in the matrix Xt of observables.8

20. Given that the number of common factors derived from the PCA is a multiple of variables used (including lags), we chose the optimal number of common factors based on the Bai and Ng (2002) selection criteria. The three Bai and Ng criteria suggest 8 common factors, which in this case account for close to 99 percent of variation in the data. We set a minimum of a cumulative 90 percent variation in the data that the combined common factors have to explain. In the case of the static PCA, the six first common factors account for more than 90 percent of variation in the financial variables while in the case of the dynamic PCA the five first factors account for more than 90 percent of variation in the data. For that reason, we selected the first six (five) common factors and combine them into single common factors using weighted averaging with weights equal to the factors’ respective coefficients of variation.

The combined common factors are then each stripped of feedback from past economic activity to construct the two PCA-based FCIs using the following equation:


where A(L) is the lag operator reflecting current and lagged GDP and inflation. yt denotes y-o-y GDP growth rate and πt denotes y-o-y inflation rate. The respective error terms εt are the PCA-based FCIs capturing only exogenous developments in financial conditions that would predict future economic activity.

21. Financial conditions in Mauritius are strongly correlated with external factors. Figure 1 presents the correlation coefficients (or factor loadings) between the two factor-based FCIs (purged) and the financial variables.9 These factor loadings represent the relative importance of each financial variable in the factor based FCIs. Positive factor loadings imply that a higher value of the financial variable is associated with better financial conditions in Mauritius. Negative factor loadings on the other hand imply that lower values of the financial variables are associated with better financial conditions. The loadings suggest that financial conditions in Mauritius are positively affected by: the EMBI; the China and Emerging Markets MSCIs; the Mauritius stock market index, an appreciating nominal effective exchange rate and credit to the private sector.10 Financial conditions in Mauritius are negatively affected by a higher domestic lending rate and a higher US T-Bill rate.

Figure 1.
Figure 1.

Mauritius: PCA FCIs Factor Loadings

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A001

Sources: Bank of Mauritius, Bloomberg, Statistics Mauritius, and IMF staff calculations.

C. Financial Condition Index for Mauritius

Overview of the Constructed FCIs

22. Figures 2 and 3 depict the measures of FCI constructed from VAR and PCA. The two PCA FCIs are constructed using static and dynamic PCA, respectively, and they are stripped of feedback effect from real economic activity in Figure 2, but not in Figure 3.

Figure 2.
Figure 2.

Mauritius: GDP Growth, PCA (Purged) and VAR FCIs, 2003–18

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A001

Sources: Bank of Mauritius, Bloomberg, Statistics Mauritius and IMF staff estimates.
Figure 3.
Figure 3.

Mauritius: GDP Growth, PCA (Unpurged) and VAR FCIs, 2003–18

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A001

Sources: Bank of Mauritius, Bloomberg, Statistics Mauritius and IMF staff estimates.

23. An increasing FCI implies looser financial conditions while a decreasing index indicates tighter financial conditions. The constructed FCIs are highly and significantly correlated with each other (Table 1).

Table 1.

Mauritius: Correlations Between FCIs, 2003Q4–2018Q2

article image
*=significance at the 10 percent levelSource: IMF staff estimates.

24. The constructed FCIs appear to track GDP growth well. This is a testament to their potential forecasting power and the importance of the financial sector in Mauritius’ economy. For example, the FCIs decreased significantly from 2007Q3 to 2008Q4, three quarters before Mauritian GDP began its largest contraction post-2000 (Figures 2 and 3).

Forecast Evaluation

25. The forecasting power of most of the constructed FCIs is confirmed by their correlation with real GDP (Table 2). The VAR-based FCI correlates the most with two- to four-quarter ahead growth rates, suggesting potential predicting power for near-term growth. Unsurprisingly, the unpurged dynamic PCA-based FCI has the highest contemporaneous correlation with GDP growth, while the purged dynamic PCA-based FCI is more related to future growth. This reflects the fact that the purged PCA-based FCI is stripped of feedback from current and previous one to two quarters’ real economic activity.

Table 2.

Mauritius: Correlations Between FCIs and Real Activity, 2003Q4–2018Q2

article image

= statistically significance at the 10 percent level

26. To evaluate the strength of the constructed FCIs in forecasting GDP, the following diffusion index model is used:


where y is real GDP growth and F is the FCI of interest. The optimal number of lags for each estimation is chosen based on the Bayesian Information Criterion (BIC).11 The model in equation (4) is estimated with and without the FCI, and the relevant root mean squared errors (RMSEs) are compared.

27. As can be seen in Table 3 below, root mean squared errors are smaller when FCIs are included in the estimation. The highest improvement in RMSE occurs when the VAR-based FCI is included in the regression, suggesting that the VAR-based FCI has a marginally better forecasting power than the PCA-based FCIs.

Table 3.

Mauritius: Root Mean Squared Errors

article image
Source: IMF staff calculations.

D. Using the FCI for Macroprudential Policy Purposes

28. The Basel III Countercyclical Capital Buffer (CCB) could be a useful tool in the authorities’ macroprudential policy toolkit. Although Mauritius does not have a comprehensive macroprudential policy framework, the authorities have deployed key macroprudential instruments such as the loan-to-value ratio or debt-service-to-income ratio. They have also introduced elements of the Basel III capital and liquidity regulation and are considering adopting the CCB, which is an additional capital buffer that should be built when credit to the private sector grows disproportionately. The CCB helps to cushion the mounting losses in an ensuing downturn. The Basel Committee on Banking Supervision (BCBS, 2010) recommends using a specification of the credit-to-GDP gap for activating the CCB.12 We apply the BCBS gap calculation but with a lower smoothing factor that is arguably more appropriate for a country like Mauritius, where credit cycles generally correspond to about five years (see Figure 4).13 Several country authorities deviate from the pure BCBS buffer guide and consider other indicators for setting the CCB such as asset prices or financial sector conditions more broadly.

Figure 4.
Figure 4.

Mauritius: FCI, Credit Gap, and Credit Growth, 2004–18

(In percent)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A001

Sources: Bank of Mauritius, Statistics Mauritius, and IMF staff estimates.

29. Given its predictive power for economic growth, FCI’s performance as a predictor for boom-bust episodes is assessed. Specifically, we check whether the FCI can forecast more accurately unsustainable boom conditions that are followed by downturns than the credit-to-GDP gap. Such downturn episodes are manifested in depressed GDP growth, rising NPLs and falling bank profits. In Mauritius, such an episode occurred during 2010–16. Favorable conditions led to high credit growth and a subsequent moderate bust which more than doubled the NPL ratio to 8.0 percent in early 2016, while the return on assets fell slightly. A macroprudential policy framework and key instruments that could have supported the policy response were not in place at the time.

30. Indeed, the FCI serves as a leading indicator for credit accelerations. As can be seen from Figure 4, both the dynamic (purged) PCA-based FCI and the VAR-based FCI lead the turning points in private sector credit growth by about four quarters, notably in 2007Q3 with respect to the peak in year-on-year credit growth in 2008Q4, in 2008Q4 with respect to the trough in 2010Q1, and again in 2009Q4/2010Q1 with respect to the rebound in credit in 2010Q4. By contrast, the credit gap variable moves with the credit boom variable, and its magnitude generally implies a less-than-fully activated CCB (according to BCBS guidance). The signals from the FCI for the later years are somewhat less clear, although the VAR-based FCI signals a return to single-digit credit growth rate in early 2014.

Figure 5.
Figure 5.

Mauritius: FCI, Credit Gap, and NPL Ratio, 2009–18

(In Percent)

Citation: IMF Staff Country Reports 2019, 109; 10.5089/9781498311991.002.A001

Sources: Bank of Mauritius, Statistics Mauritius, and IMF staff estimates.

31. The FCI also appears to be a better predictor for deteriorating loan quality than the credit gap. The FCI increased in early 2013, just when the NPL ratio was beginning to rise.14 It then kept falling for three years, when banks’ portfolio deterioration intensified. The credit gap turned positive at 1 ppt. at year-end 2013, although its magnitude would not have implies an activation of the CCB (critical minimum threshold of 2 ppt). The two FCIs then stayed depressed in late 2015/early 2016, coinciding with the peak in the NPL ratio and the trough in the return on assets. Thus, while both concepts (FCIs and credit gap) emit coherent signals, the FCI appears to be a better predictor of credit booms and busts as well as the one instance of a run-up in NPLs.

32. The FCI could be used to help inform the setting of the CCB. While it is challenging to give concrete guidance on setting the CCB based on only one recent episode of mild banking distress, the Mauritian authorities will have to devise a buffer guide if they introduce the CCB. Given Mauritius’ shorter financial cycles and the experience of a sudden NPL hike, the authorities could opt for an earlier or more accelerated buffer build-up than advised by the BCBS guidance (2010). An important consideration in this regard should be the weakness of the credit gap in timely predicting the past credit boom/bust episodes. An analysis of the credit gap could thus be usefully complemented with the FCI (and possibly other indicators such as development in credit standards and asset prices).

E. Conclusions

33. This paper develops a financial conditions index for Mauritius using different approaches. Two sets of FCIs derived from the standard VAR and PCA approaches display a reassuringly high degree of correlation. External financial conditions dominate domestic conditions in the FCIs. The three emerging market indices have the highest factor loadings and contribute the most to the PCA-based FCIs. Among the domestic variables, the stock market index is the most important, followed by the nominal effective exchange rate index. Bank-specific variables (deposit and lending rate as well as credit growth) feature less prominently. These results are in line with expectations, given that Mauritius’ financial sector is highly dependent on external capital flows.

34. The FCI is a robust predictor of real GDP growth in Mauritius. Both VAR- and PCA-based FCIs predict changes in economic activity well. This was particularly true for the 2008–09 economic downturn that was signaled by a marked decline in the FCIs already in 2007 as well as the subsequent quick recovery when the FCI turned around even before GDP growth reached its trough. While the unpurged FCI is, by construction, more correlated with current economic growth, the purged dynamic FCI and the VAR-based FCI are significantly correlated with the four-quarter ahead real GDP growth rate, illustrating their capacity to predict near-term economic activity. This finding is corroborated by lower forecasting errors in models including FCIs relative to simple autoregressive models.

35. The FCI can also help inform macroprudential policy decisions. Decisions on setting the countercyclical capital buffer of Basel III could be informed by analyzing developments in the FCI. As historically Mauritius has not experienced drastic swings in financial credit, testing the constructed FCIs for predicting boom-bust episodes is difficult. Nevertheless, the FCI signaled lax financial conditions in 2009 and again in 2012 that likely contributed to accelerated credit growth in 2012–13 and a subsequent acceleration in NPLs during 2014–16. Hence, the FCI together with the Basel credit gap and perhaps other relevant information could be used for activating the countercyclical capital buffer (and/or for other macroprudential policy purposes).

36. In sum, the economic forecasting framework for Mauritius could be usefully augmented with the FCI. While there are other variables that can be thought of as leading indicators of economic activity in Mauritius, the FCI conveniently aggregates key external and domestic factors that tend to influence future domestic real and financial activity.


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Prepared by Salifou Issoufou and Torsten Wezel.


However, it may be sensible to exclude candidate variables that have a low predictive power of GDP growth in a VAR (Gómez et al., 2011).


The FCI for South Africa uses: U.S. stock prices and their volatility (VIX) as well as South African stock prices; several spread measures—the EMBI spread, the spread between 3-mo. LIBOR and 3-mo. U.S. Treasury Bills, and South Africa’s sovereign spread; private sector credit, and non-performing loans; the nominal effective exchange rate; a bank funding rate, and the domestic house price index (Gumata et al., 2012).


The credit series exhibits a structural break in 2003Q2 due to a re-definition by the BOM of elements included in bank credit to the private sector. A comparison of FCIs based on the original and a shortened credit series omitting the structural break shows that the differences are marginal.


Note that the ordering would change based on the software used to estimate the VAR as some software use a lower triangular matrix while others use upper triangular matrix when implementing the Cholesky ordering. This ensures that the response of the variable to a shock would be zero contemporaneously if the response variable is ordered in such a way that it is not affected by the shock variable on impact.


Although the sample spans 2002Q3–2018Q2, the fact that some of the variables enter as y-o-y percentage changes mean that the usable sample is limited to 2003Q3/Q4–2018Q2.


Put differently, a principal component is a weighted average of the variables where the weights (“loadings”) are derived so that the index explains the maximum amount of variation of all included financial variables (Krznar and Matheson, 2017). In practice, only the first few principal components are considered for the FCI, assuming they capture a large share of the variation cumulatively (e.g. a minimum of 70 percent, as suggested by Gómez et al., 2011, and Khundrakpam et al., 2017).


Essentially, Xt is replaced by Zt = [XtXt-p] in equation (2), where p in the number of lags. We include 1 lag based on results from performing the Akaike Information Criterion (AIC) lag selection test.


All the factor loadings are statistically significant except for the nominal effective exchange rate.


The positive coefficient on the NEER implies that an appreciation of the Mauritian rupee is associated with more relaxed financial conditions.


Optimal lags based on AIC are higher, and more unstable, than those based on BIC. We opted for higher parsimony (and better degree of freedom) by using optimal lags based on BIC. Using AIC-suggested lags does not alter the relative ranking of FCIs.


According to the BCBS (2010), banks should start building the CCB when the credit-to-GDP gap surpasses 2 percentage points, up to a maximum of 10 percentage points, at which point the maximum size of the CCB of 2.5 percent of risk-weighted assets is normally reached. Banks can reduce the buffer when allowed so by the regulator. This is normally the case when the credit boom episode is over or when bank losses rise in a downturn.


Specifically, we use a smoothing factor (lambda) of 1,600 that is standard for quarterly data instead of a factor of 400,000 as recommended by the BCBS. The reason is that credit cycles in Mauritius have been as short as five years (e.g. a complete cycle during 2005–10, and again 2010–16), which contrasts with the BCBS’ assumption of an average credit cycle of 20 years justifying its choice of a very high lambda. There is evidence that in such cases a lower smoothing factor helps obtain reasonably-sized credit gaps (see Wezel, 2019).


Consistent information on NPLs is available only from 2009.

Mauritius: Selected Issues
Author: International Monetary Fund. African Dept.