Abstract
The implementation of macroprudential policies for improving a country’s financial stability has become more common in emerging markets. The aim of this paper is to analyse the effect of macroprudential policy on both capital flow volatility and price stability in emerging market economies. The analysis covers the Global Financial Crisis and post-crisis period. The effects of general macroprudential variables including leverage growth and credit growth and specific instruments, namely loan-to-value caps and reserve requirements on capital inflow, capital outflow and price stability have been tested. Propensity score matching techniques have been used to measure the effectiveness of various macroprudential policy measures on capital flow volatility. Major findings indicate monetary policy instruments are effective in pursuing both monetary policy objectives and macroprudential objectives. Short-term capital account volatility is seen to respond to macroprudential policy instruments. Propensity score matching was only successfully implemented for capital volatility. Results show that increased measures for macroprudential policy are effective for capital outflow and, decreased measures for macroprudential policy are effective, to a lesser extent, for capital inflows. Furthermore, meaningful correlation between increased macroprudential measures during periods of tight monetary policy exists only for capital outflows.
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Notes
The use of macroprudential in published research databases was small due to lags in the publication process (see Galati and Moessner 2011). Therefore, the large majority of the literature review and references in this area were from the working paper series.
It mirrors the risk-taking attitudes or market risk premiums.
See Appendix 6.1 for a detailed explanation of the variables used in the estimation equations.
Unit root test results show that all variables other than VIX and banking sector leverage ratio are found stationary.
This study followed Forbes et al. (2015) and used logit instead of probit model in order to “spread out” the density of scores at very low and high propensity scores. There are seven covariates used.
The share of bond market funding in the USA was more than 50% in 2007, and it was increased to 70% in 2014. This was 14% in euro area in 2007 and increased to 21% in 2014. We see similar trends in emerging markets. For example, in Latin America, it increased from 27% in 2007 to 42% in 2014; in Asian markets, it increased from 25% in 2007 to 35% in 2014. Nevertheless, the share of non-financial corporate indebtedness to GDP in 2000 was around 89% in mature markets and 53% in emerging markets. In 2014, this increased to 95% in mature markets and more than 80% in emerging markets (see IFF 2015, p. 3).
LTV, RR and other instruments are used to represent endogenous variables in binary regression.
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Appendix
Appendix
1.1 Data
There are 25 countries included in this analysis. These are Albania, Brazil, Bulgaria, Chile, China, Czech Republic, Hong Kong, Hungary, India, Indonesia, Kazakhstan, Korea, Latvia, Lithuania, Malaysia, Mexico, Peru, Philippines, Poland, Romania, Russia, Singapore, Thailand, Turkey and Uruguay. The data range covers 2008Q1–2013Q4. All data have been obtained from International Monetary Fund (IMF) International Financial Statistics (IFS), national central banks and national statistical departments. Details for the sources of data are listed below.
Capital inflow (KAI) and outflow (KAO): Portfolio investment inflow and outflow are used and data were obtained from the IMF.
GDP: Data for Argentina and Mexico was obtained from OECD; for all other countries, data were obtained from the IMF.
Interest rate (1): 3-month interbank rate for Poland, Romania Czech Republic, Hungary, Latvia and Lithuania, obtained from Eurostat. Bolivia, India, China, Hong Kong obtained from central banks. Others are monetary policy-related interest rates from IMF.
VIX: Chicago Board Options Exchange (CBOE) Rate Market Volatility Index obtained from Bloomberg and CBOE.
Leverage growth (LEV): Banking leverage is calculated by claims on private sector divided by the sum of transferable deposits included in broad money and other deposits included in broad money. Data obtained from IMF. However, IMF data are not available for Czech Republic, Hong Kong, Hungary, India, Latvia, Lithuania and Singapore. Therefore, domestic economy leverage (domestic credits divided by GDP) was used and data were obtained from central banks and national statistical departments.
Domestic credit growth (CR): Domestic credits include the sum of net claims on central government, claims on other financial corporations, claims on public non-financial corporations and claims on private sector (for Bulgaria, only net claims on central government is used due to availability). Data for Bulgaria, Hong Kong, India, Latvia, Lithuania, Peru, Poland and Singapore obtained from national statistical departments, others from the IMF.
Loan-to-value (LTV) and reserve requirements (RR): Dummy variables are used when there are policy changes, obtained from national central banks and other sources (Lim et al. 2011; Shim et al. 2013).
Exchange rates (EXR): Nominal exchange rates, national currency per US dollars, obtained from IMF International Financial Statistics.
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Uz Akdogan, I. The effects of macroprudential policies on managing capital flows. Empir Econ 58, 583–603 (2020). https://doi.org/10.1007/s00181-018-1541-5
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DOI: https://doi.org/10.1007/s00181-018-1541-5