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B ENJAMIN H USTONS ANAA N ADEEM
I NCI O TKER-R OBE
Wo r k s h o p o n M o n e t a r y P o l i c y : S p i l l o v e r s a n d I n d e p e n d e n c e( D e c e m b e r 1 8 , 2 0 1 4 )
ACCOMMODATIVE MONETARY POLICY: BREATHING SPACE OR BREEDING RISKS FOR
EMERGING MARKETS? THE ROLE FOR MACROPRUDENTIAL POLICY
Motivation
Major AE central banks have been implementing extraordinarily supportive monetary policy to restore the functioning of markets and support economic activity/soundness of the financial system
These supportive policies through CMP and UMP tools (+)
Helped alleviate the market turmoil and reduce tail risk
Benefitted the global economy (including EMDEs) through lower borrowing costs for the private and public sectors
But also raised policy challenges for EMFMs (-)
Challenges related to buildup of financial stability risks in EMFMs (e.g., low interest, high liquidity environment in AEs and search-for-yield in EMFMs)
Challenges related to normalization of monetary policy and potential impact on EMFMs (e.g., through capital flow reversals, balance sheet effects)
Questions we raise
Risks? Are there indications of rising financial stability risks in EMFMs associated with CMP/UMP in AEs
Extent? How does the degree of vulnerability/risks depend on how the policy space was used
Policy? What can EMFMs do to protect against the adverse consequences of UMP normalization/prolonged low interest rates
MaPP? Is there a role of macroprudential policy to mitigate potential financial stability risks of loose monetary policy or its normalization
Implications for Policymakers/Macro-financial surveillance
What we aim to do (1)
Consider the transmission channels of AE monetary policy to EMFMs
Examine correlations of AE interest rates with EMFM short and long-term interest rates, ERs, equity/house prices, capital inflows, credit growth…
Control for EMFM’s macro-financial characteristics and policy frameworks (e.g., degree of financial openness, ER regime, capital account openness…)
Derive estimates of EMFM financial cycles
Explore co-movements/correlations across financial cycles (between AE and EM cycles) and between EMFM financial cycles and AE rates
Assess the indication of rising financial risks in EMFMs based on the position in the financial cycle
For a sample of ~40 EMFMs
What we aim to do (2)
Assess how EMFMs used the supportive monetary policy space (indicators before/after UMP)—stylized facts To address macro and financial imbalances? To enhance financial/macro resilience and build policy space? or To build leverage/expand credit to nonproductive sectors?
Go more granular into the likely sources of financial risks (credit, FX, liquidity, etc)—key FSIs
Map the risks to MaPP tools to address the buildup of different risks, as well as to insure against adverse consequences for EMFMs of AE exit
Contributions, challenges, next steps
Key Contributions:
The angle to look into the role of MaPP in mitigating emerging financial risks and mapping the tools to risks for the sample EMFMs
Generation of financial cycles for EMDEs
Analyzing evolution and correlations across financial cycles
Key Challenges:
Generation of financial cycles for EMDEs—Absence of long data series for the key components of financial cycles
Innovation is to use imputation technique to derive the underlying data series to compute the financial cycles
Next steps: Preliminary draft around Spring 2015
Channels of Transmission—Example
Rolling correlations of EM ST rates with AE rates
Rolling correlations of EM LT rates with AE rates
-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
US EU Japan
EM rolling correlations - Long term interest rates
-1.00
-0.75
-0.50
-0.25
0.00
0.25
0.50
0.75
1.00
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
US EU Japan
EM rolling correlations - short term interest rates
Transmission channels controlling for ER regime
Correlations of short term rates Correlations of long term rates
0.00.10.20.30.40.50.60.70.80.91.0
2000-2006 2007-2009 2010-2014
Pegged Managed Floating
0.00.10.20.30.40.50.60.70.80.91.0
2000-2006 2007-2009 2010-2014
Pegged Managed Floating
Transmission channels controlling for financial openness (Correlations of EM short-term rates with AE rates)
-1.00
-0.80
-0.60
-0.40
-0.20
0.00
0.20
0.40
0.60
0.80
1.00
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00
2000-20072008-2014
Financial Openness (percentage of GDP)
IMF results
Financial cycles derived as the simple average of the cyclical component of Credit, Housing price, and Credit-to-GDP data
BIS results
Method: Apply “bandpass filter” to transformed time series of credit, housing, and credit/GDP ratios (Borio et al 2012)
Two Approaches to Financial Cycles
Frequency Analysis Apply a “bandpass filter” to transformed time series of credit,
housing, and credit/gdp ratios Take the simple average of the cyclical component identified in these
three series to get the “financial cycle”
Turning Point Analysis Apply a modified version of the Bry and Boschan Quarterly
algorithm to transformed time series of credit, housing, and credit/GDP ratios to identify series-specific “peaks” and “troughs”
Pool series-specific peaks and troughs and repeat the process to identify “financial cycle” peaks and through.
*The Frequency Analysis approach is used almost exclusively in practice and its results are synonymous with the term financial cycle
Financial Cycle Interconnectivity
Illustrative Granger-Causality Network (1% significance level; 1985-2014)
The network of global financial cycles is highly connected
Exhibits high levels of “feedback” (mutual granger-causation) among advanced economies and between advanced and emerging economies
The financial cycle imputation workflow17
Utilize iterated univariate procedures* (“chained equations”) that work in parallel to predict (“impute”) missing values of the response variable from previously observed response and predicator values
Use chained equations to impute missing data from observed data many times
Gather (incomplete) data
Perform subsequent analysis
Apply bandpassfilter to extract financial cycles from each imputed dataset
Pool results into a single financial cycles for each country
*van Buuren S, Brand JPL, Groothuis-Oudshoorn CGM, Rubin DB (2006b). “Fully Conditional Specification in Multivariate Imputation.” Journal of Statistical Computation and Simulation, 76(12), 1049–1064.
Experimental Imputed EM Housing Price Data
*Black line represents observed data. Red line represents data imputed usingchained equations.
Short imputation chain Long imputation chain
The United Kingdom and the Euro Area are the most connected in the overall network
The closer a node is to the center the more “important”, as measured by betweeness centrality, the node is to the structure of the network,
Betweenness Centrality Graph of Granger-Causality Network (1% Significance Level; 1985-2014)
The U.S. and Korea are most similar in terms of in terms of connectivity.
The other countries (excluding Germany) have similar levels of connectivity.
Nodes groups denote “similarity”, as measured by betweenness centrality scores. Nodes within each group are most similar along this dimension.
Betweenness Community of Granger-Causality Network (1% Significance Level; 1985-2014)