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The authors thank Nigel Chalk, Papa N’Diaye, Jacek Osinski, Alejo Costa, Sean Craig, Andre Meier, and seminar participants at the Hong Kong Monetary Authority for helpful comments, and Alla Myrvoda and Imel Yu for invaluable assistance. We thank the MCM department—particularly Francesco Columba, Alejo Costa, and Cheng Hoon Lim—for sharing the cross-country data with us. The authors bear all responsibility for errors and omissions.
Recent examples of IMF research in this area include IMF (2011a, 2011b and 2011c). This previous research has focused on whether macroprudential policies can dampen the pro-cyclicality of credit (IMF 2011b) and also on identifying, in a structural model, how these policies can reduce the systemic risk of a breakdown in financial intermediation and large output losses (IMF 2011c).
We thank the MCM department—particularly Francesco Columba, Alejo Costa, and Cheng Hoon Lim—for sharing the cross-country data with us.
Unsal (2011) studies the complementarity between monetary policy tools and macroprudential policies in addressing the risks posed to financial stability by large inflows of capital.
As defined by IMF’s AREAER classification. The system classifies member country exchange rate regimes into four broad categories: hard pegs (dollarized economies, currency boards); soft pegs (conventional pegged arrangements, pegged exchange rates within horizontal bands, crawling pegs and crawl-like arrangements, and stabilized arrangements); floating regimes (floating and free-floating); and a residual category (other managed arrangements). The exchange rate classification is actual and de facto—it may differ from members’ officially announced de jure arrangement.
This is also in line with the 2010 IMF survey on the use of macroprudential instruments, which found that economies with fixed or managed exchange rate regimes use the tools—going beyond LTV and DTI caps—more frequently.
One other goal of such policies could also be to limit vulnerabilities in household balance sheets. Data limitations preclude our analysis of this policy dimension. We leave this for future research.
We run separate regressions for the two samples. An alternative would be to introduce a dummy variable in the broader sample to represent the subset of economies with pegged or currency board arrangements and run one regression for the entire sample. But in that alternative set up, in order to compare the impact of the policies for the subset with the impact for the remaining economies, we would need to introduce multiple interactions that strain the degrees of freedom available in the sample. The separate regressions presented here offer a cleaner way of comparing the effectiveness of the policies. The results tables presented below also report diagnostic tests for serial correlation. The null hypothesis is of no second-order autocorrelation in the first differenced error terms. As the tables show we fail to reject the null in the specifications used, alleviating concerns about serial correlation.
Related to our work, Wong et al. (2011) finds that LTV caps can help stabilize property market activity in Hong Kong SAR, but not in Singapore and Korea. They also find that LTV policy is effective in lowering the sensitivity of mortgage default risk to property price shocks.
However, it is worth noting that following the announcement of the SSD in November 2010, the number of transactions dropped by around 30 percent within the first month.
The result should however be interpreted with caution since the small number of sample observations may prevent us from detecting an effect of land sales policy, which tend to work with long lags.