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Appendix I. Figures
Appendix II. Korean Banking Sector
We are highly indebted to Subir Lall for his invaluable suggestions; this project would not have materialized without his support. In addition, we have greatly benefited from the inputs of Seung-Cheol Jeon and Minsu Kim. We also thank the participants of the seminars held at the International Monetary Fund and the Bank of Korea.
DeFiore and Farr, 2011, argue that monetary policy has real effects in a flexible-price equilibrium because it affects the cost of external finance, causing adverse effects on domestic demand as well as higher bankruptcy rates and a larger waste of monitoring resources for the economy.
Of course, most central banks have room for discretion and do not adhere mechanically to the rule implied by the prevailing framework. This was evident in the crisis response of most central banks, but largely only once the economic impact of the financial crisis started becoming evident, rather than in the run up.
The limitation of this indicator is that spreads can also be affected by factors such as the availability of liquidity or a general decline in risk aversion.
This period corresponds to the times that Bank of Korea has adopted an inflation targeting framework and various financial products, such as mortgage loans, were available in the market. The period ends in 2007 to exclude the effects of the global financial crisis.
We should note that in practice, it may be difficult for a central bank to detect the sources of a shock. Nevertheless, the finding that an ITFS performs either better than or similar to (and no worse than) the benchmark inflation targeting (ITB) rule, would be of reassurance for central bankers implementing this new IT framework.
Akram and others, 2007, argue that gains or losses from responding directly to financial stability indicators are highly shock-dependent. There are gains in terms of inflation and output stability in the case of a house price shock, while there are costs in terms of relatively large variation in inflation and output in the case of a credit shock.
For instance, Curdia and Woodford, 2010, argue that a monetary policy response to credit would not help to stabilize the economy, since it is not clear whether encouraging or discouraging credit to the private sector helps to smooth the nature and persistence of disturbances in the economy.
The country risk premium is measured by the exchange equalization fund spread. These funds are securities issued in dollar denomination in order to stabilize the foreign exchange market, and they are traded frequently in the market. The spread on these securities, relative to the U.S. Treasury bond, reflect the country risk premium. Along with the CDS premium, spreads on exchange equalization funds have been used as the main proxies in the aftermath of the global financial crisis.
Also, households’ balance sheet worsened less, since housing, which constitutes the majority of the household wealth, is highly regulated in Korea.
Trade is a very strong channel for Korea, and its importance during the 2000s was on an increasing trend. The share of imports and exports rose from around 55 percent of its GDP in 2001 to almost 90 percent in 2007.
This setup is the key aspect of the financial accelerator mechanism which was developed in BGG. The setup links the balance sheets of the nonfinancial sector to that of the financial sector, and is key in capturing the amplifying effect of economic fluctuations.
The price of domestic and foreign consumption goods relative to the price of the composite consumption good are denoted by pd,t and pf,t, respectively. Note that the real variables are defined as the nominal variables relative to the price level.
Whether τh,t is a direct or indirect cost is irrelevant for the model.
The banks’ balance sheet and borrowing/lending decisions is modeled in accordance with the characteristics of the banking sector in Korea. For a broad but definitive picture of Korean banking sector balance sheet structure and borrowing/lending decisions please refer to Appendix II.
Even though a majority of the empirical analyses applied to the US economy place oil prices within the system of endogenous variables, Granger causality test results indicate that price, output and the policy rate do not Granger cause oil prices in Korea. Hence we treat oil prices as an exogenous variable.
We calculated mortgage spread as the difference between mortgage lending rate and the policy rate; corporate borrowing spread as the wedge between bank lending rate to corporations and the policy rate; and bank leverage as the total assets to equity ratio of the total banking system. Calculating the mortgage and corporate borrowing spreads relative to the policy rate or the deposit rate does not change the impulse response dynamics, since the deposit rate closely follows the dynamics of the policy rate.
We have a slightly larger sample than the one used for calibrations in order to gain from degrees of freedom. However, for some financial sector indicators, data, such as mortgage lending, are available only by 2003. We control for the post-2007 period through a global financial crisis dummy.
Both Akaike and Schwarz information criterion indicate that the SVAR with two lags is the most efficient one. All the inverse roots of the autoregressive polynomial lie within the unit circle, indicating that the estimated VAR is stationary.
Impulse responses plotted on this figure are obtained from the following VAR: [Price, output, policy rate, mortgage spread], and the response of the macroeconomic variables to a monetary policy shock is invariant of the financial variable used in the system of equations.
In the data, a monetary policy shock affects spreads with a lag. Nevertheless, such a discrepancy would not be of a concern looking at the rather insignificant magnitude of the impulse responses generated both from the data and our model. Further, more than 70 percent of mortgage contracts are floating rate, linked to the 90-day CD-rate in Korea.
Please refer to the external finance premium shock for further details on how credit market dynamics interact with real variables leading to a lingering effect in the normalization of real and financial variables.
The interest rate rules are in linearized form.
The weight of the coefficients in this policy rule and the following are chosen randomly, within the range used earlier in the literature for some other financial indicators.
The shock follows an autoregressive process with a persistence parameter of 0.95.
In all ITFS rules, except the one under scenario two, output volatility is smaller than how it would be under the benchmark case. Scenario two produces similar results as that of ITB.
See Appendix for the impulse response figures of these shocks.