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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 = [Xt — Xt-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.