Abstract
When one region of the world economy experiences a financial crisis, the world-wide availability of investment opportunities declines. As global investors search for new destinations for their capital, other regions will experience inflows of hot money. However, large capital inflows make the recipient countries more vulnerable to future adverse shocks, creating the risk of serial financial crises. This paper develops a formal model of such flows of hot money and the vulnerability to serial financial crises. It analyzes the role for macro-prudential policies to lean against the wind of such capital flows so as to offset the externalities that occur during financial crises. Summarizing the results of our model in a simple policy rule, the paper finds that a 1 percentage point increase in a country's capital inflows/GDP ratio warrants a 0.87 percentage point increase in the optimal level of capital inflow taxation.
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Notes
We also performed Granger causality tests using alternative measures of interest rates and the results were consistent with those reported in Table 1.
In the reported test, the crisis variable is the union of banking crises as defined by Reinhart and Rogoff (2009) and currency crises as defined by Frankel and Rose (1996). We also performed the tests for each crisis indicator separately and the results were consistent with those reported in the table, though at slightly lower significance levels.
As we will see below, this formulation leads to a time-invariant supply of funds function that greatly simplifies our numerical analysis and therefore allows us to efficiently simulate a setup with multiple borrowing countries.
An alternative specification would be to assume that international investors can seize up to a fraction φ of the asset holdings of borrowers, which would entail the term −φa i t+1 p t i on the right-hand side of the incentive-compatibility constraint. As discussed in Jeanne and Korinek (2010), the implications of the two setups are largely identical.
A detailed derivation of the uniqueness of equilibrium is given in the appendix of Jeanne and Korinek (2010). They find the threshold that guarantees uniqueness to be φ̂≈0.09 for standard parameter values.
For an analysis of such pecuniary externalities in a small open economy setup, see for example Korinek (2010).
See Jeanne and Korinek (2010) for a more detailed derivation.
A separate and important concern is that the costs of an overvalued exchange rate fall disproportionately on exporters, who may have disproportionate lobbying power.
An alternative approach would be to approximate the output process {y¯ t } by a discrete random variable with a larger number of states so as to resemble a continuous random variable. This would allow us to endogenize the threshold ŷ t of the endowment shock below which the economy experiences binding constraints and crises, and to make statements about this threshold. However, this would come at the expense of clarity in our analysis. Furthermore, given that financial crises are rare events, it is difficult to calibrate the precise probability distribution of the left tail of the process {y t }. More generally, all of our results below that relate to the intensive margin of financial crises given y t =y L (that is, how severe they will be) apply equally to the extensive margin, as captured by the probability of a crisis and the threshold ŷ t .
In crisis episodes, the real-world interest rate turns slightly negative, which is consistent with the experience from the most recent financial crisis in 2008, or the East Asian crisis in 1997.
For example, Brazil imposed a 2 percent tax on capital inflows in October 2009, which was later raised to 6 percent. Thailand implemented a 30 percent URR from 2006 to 2008, and Colombia a 40 percent URR in 2007, which was later increased to 50 percent. See Ostry and others (2011).
However, declining exchange rates play a similar role and also entail pecuniary externalities during financial crises. See Korinek (2010).
References
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Additional information
*Anton Korinek is an assistant professor of Economics at the University of Maryland. The author would like to thank the editors Pierre-Olivier Gourinchas and Ayhan Kose for very thoughtful comments on earlier versions of the paper. Rudolfs Bems, Julien Bengui, Gianluca Benigno, Javier Bianchi, Carmen Reinhart and Carlos Végh, as well as two anonymous referees have kindly provided a number of helpful comments and suggestions. The author is grateful to Rocio Gondo Mori and Elif Ture for excellent research assistance. This paper was prepared for the IMF's 11th Jacques Polak Annual Research Conference and the IMF Economic Review.
Appendix
Appendix
VI. Data Sources
The stylized facts reported in the introductory part of the paper are based on four sets of variables that we obtained at annual frequency for 176 countries over the period of 1980–2009: As an indicator for the world interest rate, we use 10-year U.S. Treasury bond yields deflated by the U.S. consumer price index, which is smoothed by taking a three-year moving average so as to remove sudden unexpected changes in inflation. Both variables are taken from the IMF's International Financial Statistics (IFS) database.
The dummy variable for capital flow bonanzas is calculated using IFS data according to the procedure of Reinhart and Reinhart (2008), that is a bonanza occurs when a country's current account balance as a percentage of GDP is in its top quintile. Dummy variable for banking crises are obtained from Reinhart and Rogoff (2009), Table A.3.1170. Dummy variables for currency crises are calculated using IFS data according to the procedure of Frankel and Rose (1996), that is a currency crisis occurs if a country's exchange rate depreciates by more than 25 percent, and if this depreciation in turns is at least 10 percent more than in the preceding period. Finally dummies for all crises are constucted by combining banking and currency crisis dummies.
The Granger causality tests in Table 1 were performed using fixed effects panel regressions. We first included lags of the dependent variable and found that only the first lag was significant. Then we augmented the regression with lagged values of the independent variable. In Table 1 we report the resulting parameters and the associated t-values in parentheses. In both tests that are reported we can reject the hypothesis that the independent variables does not Granger-cause the dependent variable at the 0.1 percent level.
We also investigated a potential causal link between U.S. banking crises and U.S. 10-year bond yields by constructing an indicator of U.S. bank failures from the FDIC list of failed banks as a proxy for banking sector problems in the United States. However, we could not establish Granger-causality since FDIC assistance seemed to occur with a significant lag to the actual occurance of banking sector problems.
VII. Numerical Solution Method
Our numerical solution method is an extension of the endogenous gridpoint bifurcation method of Jeanne and Korinek (2010). Denote the beginning of period bond holdings for a representative agent in region i as b and for an agent in region j as d. The interest rate is a function of aggregate worldwide borrowing R=R(b+d) as given by Equation (2). Denote the total beginning of period liquid wealth holdings of agents in the two regions as m=b+y and n=d+z. Our problem is to obtain policy functions c(m, n), p(m, n), λ(m, n), and b′(m, n). By symmetry this latter function is identical to d′(n, m).
Taking advantage of the efficiency gains provided by the endogenous gridpoint bifurcation method requires setting up the problem in two nested loops.
Outer Loop
At the beginning of iteration k in the outer loop, we start with the policy functions c k (m, n), p k (m, n), λ k (m, n), and b′ k (m, n), which is symmetric to d′ k (m, n). (The initial policy functions can be set arbitrarily.)
Inner Loop
Each inner loop iteration starts with a given set of policy functions c̃ l (m, d′), p̃ l (m, d′), and λ̃ l (m, d′). (The initial functions can be set arbitrarily.) Taking d′ k (m, n) from the outer loop as given, we calculate ĉ(m, n)=c̃ l (m, d′ k (m, n)) and similarly for p̂ and λ̂. In order to take advantage of the endogenous gridpoints method, it is useful to perform our iterations over the grid (b′, d′) of end-of-period wealth levels. For any pair (b′, d′), we calculate the world interest rate R(b′, d′)=R(b′+d′), which is obtained from lenders’ optimality condition (2). Then we define
Then we solve the system of optimality conditions first under the assumption that the borrowing constraint is loose.
In the same way, we can solve for the constrained branch of the system for b′≤0 under the assumption that the borrowing constraint is binding in the current period as
Concatenating constrained and unconstrained results as in Jeanne and Korinek (2010) allows us to obtain policy functions c̃ l+1(m, d′), p̃ l+1(m, d′), and λ̃ l+1(m, d′) as well as w̃′ l+1(m, d′). The steps are iterated until convergence is reached. By employing the endogenous gridpoint bifurcation method, this loop converges very quickly.
Once the inner loop is completed, we observe that the two functions b̃′ l+1(m, d′) and the symmetric d̃′ l+1(n, b′) can be combined to
Finding the root of this equation yields a function b′ k+1(m, n) and the symmetric function d′ k+1(n, m). This step is computationally more costly. However, by alternating iterating on the (efficient) inner loop with iterating on the (computationally costly) outer loop, the problem can be solved in an efficient manner. We substitute d′ k+1(m, n) into c̃ l (m, d′), p̃ l (m, d′), and λ̃ l (m, d′) to obtain c k+1(m, n), p k+1(m, n), λ k+1(m, n) and iterate until convergence.