We are grateful to Federico Boffa, Peter Dattels, Jose De Gregorio, Kristian Hartelius, Fuad Hasanov, Matthew Jones, Jon Huntley, Herman Kamil, Christoph Klingen, Nicolas Magud, Yan Sun, Fabian Valencia and Esteban Vesperoni for helpful comments. Alexander Demyanets thanks the IMF’s Global Markets Monitoring and Analysis division for its hospitality. All errors are the sole responsibility of the authors.
See Kaminsky and Reinhart (1999), Borio et al. (2001), Jimenez and Saurina (2006), Lown and Morgan (2006), Carlson et al. (2009), Marcucci and Quagliariello (2009), Helbling et al. (2011), and Meeks (2011) and Nkusu (2011).
The advantage of structural VARs is that they allow us to examine the effect of a structural shock in one variable on all the other variables in the system.
Chapter 1 of Global Financial Stability Report (2011) and chapter 4 of World Economic Outlook (2011) discuss recent trends.
Kaminsky and Reinhart (1999), Borio et al. (2001), Jimenez and Saurina (2006), Lown and Morgan (2006), Carlson et al. (2009), Marcucci and Quagliariello (2009), Espinoza and Prasad (2010), Hartelius (2010), Helbling et al. (2011), Meeks (2011) and Nkusu (2011).
The key papers are Marcucci and Quagliariello (2009), Hartelius (2010), Espinoza and Prasad (2010) and Nkusu (2011).
Following is just a selection of papers on the topic; Dornbusch et al. (1995), Krugman (1999), Eichengreen and Hausman (2000), Calvo and Reinhart (2002) and Cspedes et al. (2004), Magud et al. (2011) and Kamil (2012).
Cespedes et al. (2004), for example, propose a model where a real depreciation can have contradictory and potentially offsetting effects on firms’ balance sheets.
Burnside et al. (2001) argue that the incomplete hedge is due to the presence of implicit government bailouts.
Looking at business cycle frequencies, Mendoza (1995) finds a mean correlation of 0.12 between the real exchange rate and terms of trade in developing countries. Cashin et al. (1995) find that the real exchange rate and the real price of commodity exports co-move in a number of commodity-exporting countries.
Recent work has pointed out that gross capital flows are essential to fully understand the dynamics and vulnerabilities associated with a country’s cross-border financing activity. See Levchenko and Mauro (2007), Cardarelli et al. (2009), Tong and Wei (2010), Borio and Diyata (2011), Pirovano et al. (2011), and Forbes and Warnock (2011).
We follow the definitions for the capital and financial account as described in the IMF’s Balance of Payments and International Investment Position Manual. Inflows arise when external liabilities are incurred by the recipient economy (inflows with a positive sign) and external liabilities are reduced (inflows with a negative sign). Net flows are the sum of gross inflows and outflows, where outflows are recorded with a negative sign.
We look at the growth rate in the terms of trade of goods prices to focus on the effect of commodity prices. In any case, for the countries in our sample this variable is highly correlated (88%) with the growth rate in the terms of trade of goods and services.
Since our panels are unbalanced and Levin et al. (2002) require strongly balanced data, we restrict the sample to be of equal time length.
Fisher-type tests assume individual unit root processes within panels and combine independent p-values from individual tests to arrive at the joint test of stationarity. We run the tests with augmented Dickey-Fuller and Phillips-Perron auxiliary regressions. The null in each case is that all panels have unit roots against the alternative that at least one panel is stationary.
See Global Financial Stability Report (2011).
We restrict the specification to one lag for two reasons. First, we do not find evidence of strong macroeconomic effects on NPLs beyond one lag in bi-variate regressions. Second, we want to conserve the degrees of freedom, given the small sample size.
For Arellano-Bond and Arellano-Bover-Blundell-Bond procedures, one-step estimates are reported.
The Arellano-Bond AR(1) test for residual autocorrelation rejects the hypothesis that the errors are not autocorrelated at conventional levels. The AR(2) test does not reject the null that the errors in the level equations are not correlated.
In Kunt and Detragiache (1997), for instance, credit growth is quantitatively the second largest factor in explaining the probability of financial crises. Kaminsky et al. (1997) report that five out of seven studies looking at credit growth as a determinant of currency crises found statistically significant results.
Equity data (MSCI share index in local currency) is from Haver, whereas inflation numbers are from WEO. Lending rates are from IFS.
We also ran the regressions with lending rate only. AB and System GMM methods deliver positive sign and statistical significance.
We do not report standard errors to preserve space; most of the variables in our specification are also significant at conventional levels in the subsamples.
The code used to estimate the model and produce impulse response functions was written by Inessa Love. Love and Zicchino (2006) describe the methodology in greater details.
The identification scheme is close in spirit to Kaminisky and Reinhart (1999). Marcucci and Qualiariello (2008) propose a related identification scheme where they rank default rates first.
See Cardarelli et al. (2009) for a recent contribution.
The IRFs for the other variables are in line with the baseline model.
Heytens and Karacadag (2001) argue that the debt of companies (as a proportion of total debt) for which interest expenses exceed earnings before interest, tax, depreciation and amortization, is an excellent alternative for tracking credit quality.