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A MACROPRUDENTIAL
POLICY FRAMEWORK
AMUND HOLMSEN, IDA WOLDEN BACHE AND
KARSTEN GERDRUP
OECD 13 JUNE 2014
Agenda
 Background
 Principles guiding Norges Bank’s advice on the CCB
 Decision basis and indicators
 Towards a new quantitative framework for setting the CCB
2
BACKGROUND
The Norwegian economy
Sources: Statistics Norway and Norges Bank
-2
1
4
7
-2
1
4
7
1995 1999 2003 2007 2011
GDP mainland Average
0
3
6
9
0
3
6
9
1995 1999 2003 2007 2011
Unemployment rate (ILO)
0
3
6
9
0
3
6
9
1995 1998 2001 2004 2007 2010 2013
Key policy rate
Introduction of
inflation target
-1
1
3
5
-1
1
3
5
1995 1998 2001 2004 2007 2010 2013
CPI (core)
Inflation target
4
Terms of trade
60
80
100
120
140
160
60
80
100
120
140
160
1978 1982 1986 1990 1994 1998 2002 2006 2010 2014
Index. Q1 2000 = 100. Q1 1978 – Q1 2014
5Sources: Statistics Norway and Norges Bank
House prices
Index. Q1 1995 = 100. Q1 1995 – Q1 2014
6
100
150
200
250
300
350
400
450
100
150
200
250
300
350
400
450
1995 1997 1999 2001 2003 2005 2007 2009 2011 2013
Norway Sweden
Denmark UK
Netherlands Spain
1) Denmark and Spain: up to and including Q4 2013
Source: Thomson Reuters
Policy actions
7
 Stricter guidelines on prudent mortgage lending (2011)
 Higher capital requirements incl. capital conservation buffer, systemic risk
buffer and SIFI buffer (2013-2016)
 Higher risk-weights on mortgage lending (2014)
 Countercyclical capital buffer activated (2015)
 Preparations for new banking resolution regime
 Monetary policy «leaning against the wind»
CET1 requirements Norwegian banks
4,5 4,5 4,5
2,5 2,5 2,5
3,0 3,0 3,0
1,0 2,0
0
2
4
6
8
10
12
14
16
0
2
4
6
8
10
12
14
16
31 Dec 2011 31 Dec 2012 31 Dec 2013 1 Jul 2014 1 Jul 2015 1 Jul 2016
Countercyclical buffer
Maximum countercyclical buffer
SIFI-buffer
Systemic Risk Buffer
Capital Conservation Buffer
Minimum requirement
1,0
1,0
Percent of risk weighted assets
8
Sources: Ministry of Finance and Norges Bank
Institutional set-up for CCB in Norway
 Norges Bank prepares decision
basis and issues advice on the level
of the CCB
 Information exchange with the
Norwegian FSA
 Ministry of Finance sets the buffer
rate every quarter
9
Formulating a macroprudential policy
 Clear objective
 Explicit criteria and indicators for appropriate policy
 Transparency about policy intentions
 Credibility and accountability
10
Objective of CCB
“The purpose of the countercyclical capital buffer is to strengthen the financial
soundness of banks and their resilience to loan losses in a future downturn and
mitigate the risk that banks will amplify a downturn by reducing their lending.”
Regulation on the CCB (Section 1), 4 Oct 2013
11
Policy parallels
 Clear objective
 Transparency about
– Principles/criteria for appropriate policy
– Key indicators
– Reaction pattern
12
Monetary policy Macroprudential policy
Objective Low and stable inflation Increase resilience of banks to losses in
future downturn and mitigate pro-cyclical
effects of tighter lending
Criteria 1. The inflation target is
achieved
2. The inflation targeting
regime is flexible
3. Monetary policy is robust
1. Banks should become more resilient
during an upturn
2. The size of the buffer should be
viewed in the light of other
requirements applying to banks
3. Stress in the financial system should
be alleviated
Key indicators Forecast of inflation and output • Credit/GDP
• House prices/disposable income
• Real commercial property prices
• Banks’ wholesale funding ratios
Transparency about
policy intentions
Interest rate forecast Explicit statement about reaction pattern
Communicating the reaction pattern
 “If there are signs that financial imbalances continue to build up, Norges Bank will issue
advice to increase the buffer rate (…)”
 “The CCB is not an instrument for fine-tuning the economy.”
 “The buffer rate should not necessarily be reduced even if there are signs that financial
imbalances are receding. In long periods of low loan losses, rising asset prices and
credit growth, banks should normally hold a countercyclical capital buffer.”
 “Any future advice to reduce the buffer rate will be based on an assessment of market
turbulence, loss prospects for the banking sector and the risk of a credit-driven downturn
in the Norwegian economy.”
Norges Bank’s letter to the Ministry of Finance March 2014
13
DECISION BASIS AND INDICATORS
Decision basis
“The decision basis shall contain an overview of the credit-to-GDP ratio and the
extent to which it deviates from the long-term trend, as well as other indicators,
and Norges Bank’s assessment of systemic risk that is building up or has built
up over time.”
Regulation on the CCB (Section 3), 4 Oct 2013
15
Credit as a share of GDP
Percent. 1976 Q1 – 2013 Q4
16
Sources: Statistics Norway and Norges Bank
75
100
125
150
175
200
75
100
125
150
175
200
1976 1984 1992 2000 2008
Crises Credit/GDP
Credit as a share of GDP
Percent. 1976 Q1 – 2013 Q4
17
Sources: Statistics Norway and Norges Bank
75
100
125
150
175
200
75
100
125
150
175
200
1976 1984 1992 2000 2008
Crises
Credit/GDP
Augmented HP filter
One-sided HP filter
10-year rolling average
Credit/GDP – deviation from trend
Percentage points.
18
Sources: Statistics Norway, IMF and Norges Bank
-30
-10
10
30
50
-30
-10
10
30
50
1983 1991 1999 2007
Variation
One-sided HP-trend
Augmented HP-trend
10 year moving average
Reference values for CCB in Norway
Basel “bufferguide”. Per cent of risk weighted assets. 1983 Q1 – 2013 Q4
19Sources: Statistics Norway, IMF, BIS and Norges Bank
0
0,5
1
1,5
2
2,5
3
3,5
0
0,5
1
1,5
2
2,5
3
3,5
1983 1987 1991 1995 1999 2003 2007 2011
Buffer based on deviation from the Basel Committee's recommended HP trend
Buffer based on deviation from alternative HP trend
20
50
100
150
200
50
100
150
200
1976 1984 1992 2000 2008
Key indicators for build-up of CCB
Real commercial property prices Banks’ wholesale funding ratio
Credit / GDP House prices / disposable income
50
100
150
200
50
100
150
200
1976 1984 1992 2000 2008
50
100
150
200
50
100
150
200
1976 1984 1992 2000 2008
0
20
40
60
0
20
40
60
1976 1984 1992 2000 2008
Historical averageLevel
Guided discrection
 «Guided discretion»
– Weight on rules depends on reliability of indicators
– More weight on rules as analytical framework is improved?
 More judgment needed in release phase?
21
Does it work?
Banks’ lending margins on mortgages
18 Jul 2010 – 10 Jun 2014
Credit growth (y-o-y) enterprises
Jan 2008 – Apr 2014
-10
0
10
20
30
40
-10
0
10
20
30
40
2008 2010 2012 2014
Bank debt
Bond debt
22Source: DNB Markets, Statistics Norway and Norges Bank
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
2010 2011 2012 2013
Risk premium 5-yr covered bond
3m NIBOR - key policy rate
Key policy rate
Residential mortgage rate
Estimated cost of mortgage financing
TOWARDS A NEW QUANTITATIVE
FRAMEWORK FOR THE CCB
Modeling approaches
 Empirical cost-benefit analysis:
– Benefits: Smaller probability of systemic crisis and less severe crisis
– Costs: Less financial intermediation in «normal times»
 Policy analysis in structural models:
– ESCB’s 3D model
– IMF’s MAPMOD
24
Logit model estimated on panel of 16 countries Q1 1970 – Q2 2013
25
0
.1.2.3.4.5.6.7.8.9
1
1970q1 1980q1 1990q1 2000q1 2010q1
Quarter
90% 70%
50% 30%
Norway
0
.1.2.3.4.5.6.7.8.9
1
1970q1 1980q1 1990q1 2000q1 2010q1
Quarter
90% 70%
50% 30%
USA0
.1.2.3.4.5.6.7.8.9
1
1970q1 1980q1 1990q1 2000q1 2010q1
Quarter
90% 70%
50% 30%
UK
0
.1.2.3.4.5.6.7.8.9
1
1970q1 1980q1 1990q1 2000q1 2010q1
Quarter
90% 70%
50% 30%
Spain
Estimated crisis probabilities
Marginal effects on crisis probability of
different indicators
Household Credit to GDP Gap
NFE Credit to GDP Gap
Wholesale Funding Gap
House Prices to Inc. Gap
Equity/Assets
-4 -2 0 2 4
Marginal Effect on Crisis Probability (pp)
A MACROPRUDENTIAL
POLICY FRAMEWORK
AMUND HOLMSEN, IDA WOLDEN BACHE AND
KARSTEN GERDRUP
OECD 13 JUNE 2014
Logit model for estimating crisis probabilites
 Panel of 16 industrialized countries 1970Q1 – 2013Q2
– Australia, Belgium, Canada, Finland, France, Germany, Italy, Japan, Korea,
Netherlands, Norway, Spain, Sweden, Switzerland, UK and USA
 28 identified crises
 Explanatory variables
– Total credit to private non-financial sector, households and non-financial enterprises
– Nominal and real GDP
– House prices and disposable income
– Equity prices
– Inflation and interest rates
– Banking sector variables (leverage and market financing)
– Trade weighted global credit and house prices
28

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2014.06.13 - NAEC EDRC Seminar - A Macroprundential policy framework

  • 1. A MACROPRUDENTIAL POLICY FRAMEWORK AMUND HOLMSEN, IDA WOLDEN BACHE AND KARSTEN GERDRUP OECD 13 JUNE 2014
  • 2. Agenda  Background  Principles guiding Norges Bank’s advice on the CCB  Decision basis and indicators  Towards a new quantitative framework for setting the CCB 2
  • 4. The Norwegian economy Sources: Statistics Norway and Norges Bank -2 1 4 7 -2 1 4 7 1995 1999 2003 2007 2011 GDP mainland Average 0 3 6 9 0 3 6 9 1995 1999 2003 2007 2011 Unemployment rate (ILO) 0 3 6 9 0 3 6 9 1995 1998 2001 2004 2007 2010 2013 Key policy rate Introduction of inflation target -1 1 3 5 -1 1 3 5 1995 1998 2001 2004 2007 2010 2013 CPI (core) Inflation target 4
  • 5. Terms of trade 60 80 100 120 140 160 60 80 100 120 140 160 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 Index. Q1 2000 = 100. Q1 1978 – Q1 2014 5Sources: Statistics Norway and Norges Bank
  • 6. House prices Index. Q1 1995 = 100. Q1 1995 – Q1 2014 6 100 150 200 250 300 350 400 450 100 150 200 250 300 350 400 450 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Norway Sweden Denmark UK Netherlands Spain 1) Denmark and Spain: up to and including Q4 2013 Source: Thomson Reuters
  • 7. Policy actions 7  Stricter guidelines on prudent mortgage lending (2011)  Higher capital requirements incl. capital conservation buffer, systemic risk buffer and SIFI buffer (2013-2016)  Higher risk-weights on mortgage lending (2014)  Countercyclical capital buffer activated (2015)  Preparations for new banking resolution regime  Monetary policy «leaning against the wind»
  • 8. CET1 requirements Norwegian banks 4,5 4,5 4,5 2,5 2,5 2,5 3,0 3,0 3,0 1,0 2,0 0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 14 16 31 Dec 2011 31 Dec 2012 31 Dec 2013 1 Jul 2014 1 Jul 2015 1 Jul 2016 Countercyclical buffer Maximum countercyclical buffer SIFI-buffer Systemic Risk Buffer Capital Conservation Buffer Minimum requirement 1,0 1,0 Percent of risk weighted assets 8 Sources: Ministry of Finance and Norges Bank
  • 9. Institutional set-up for CCB in Norway  Norges Bank prepares decision basis and issues advice on the level of the CCB  Information exchange with the Norwegian FSA  Ministry of Finance sets the buffer rate every quarter 9
  • 10. Formulating a macroprudential policy  Clear objective  Explicit criteria and indicators for appropriate policy  Transparency about policy intentions  Credibility and accountability 10
  • 11. Objective of CCB “The purpose of the countercyclical capital buffer is to strengthen the financial soundness of banks and their resilience to loan losses in a future downturn and mitigate the risk that banks will amplify a downturn by reducing their lending.” Regulation on the CCB (Section 1), 4 Oct 2013 11
  • 12. Policy parallels  Clear objective  Transparency about – Principles/criteria for appropriate policy – Key indicators – Reaction pattern 12 Monetary policy Macroprudential policy Objective Low and stable inflation Increase resilience of banks to losses in future downturn and mitigate pro-cyclical effects of tighter lending Criteria 1. The inflation target is achieved 2. The inflation targeting regime is flexible 3. Monetary policy is robust 1. Banks should become more resilient during an upturn 2. The size of the buffer should be viewed in the light of other requirements applying to banks 3. Stress in the financial system should be alleviated Key indicators Forecast of inflation and output • Credit/GDP • House prices/disposable income • Real commercial property prices • Banks’ wholesale funding ratios Transparency about policy intentions Interest rate forecast Explicit statement about reaction pattern
  • 13. Communicating the reaction pattern  “If there are signs that financial imbalances continue to build up, Norges Bank will issue advice to increase the buffer rate (…)”  “The CCB is not an instrument for fine-tuning the economy.”  “The buffer rate should not necessarily be reduced even if there are signs that financial imbalances are receding. In long periods of low loan losses, rising asset prices and credit growth, banks should normally hold a countercyclical capital buffer.”  “Any future advice to reduce the buffer rate will be based on an assessment of market turbulence, loss prospects for the banking sector and the risk of a credit-driven downturn in the Norwegian economy.” Norges Bank’s letter to the Ministry of Finance March 2014 13
  • 14. DECISION BASIS AND INDICATORS
  • 15. Decision basis “The decision basis shall contain an overview of the credit-to-GDP ratio and the extent to which it deviates from the long-term trend, as well as other indicators, and Norges Bank’s assessment of systemic risk that is building up or has built up over time.” Regulation on the CCB (Section 3), 4 Oct 2013 15
  • 16. Credit as a share of GDP Percent. 1976 Q1 – 2013 Q4 16 Sources: Statistics Norway and Norges Bank 75 100 125 150 175 200 75 100 125 150 175 200 1976 1984 1992 2000 2008 Crises Credit/GDP
  • 17. Credit as a share of GDP Percent. 1976 Q1 – 2013 Q4 17 Sources: Statistics Norway and Norges Bank 75 100 125 150 175 200 75 100 125 150 175 200 1976 1984 1992 2000 2008 Crises Credit/GDP Augmented HP filter One-sided HP filter 10-year rolling average
  • 18. Credit/GDP – deviation from trend Percentage points. 18 Sources: Statistics Norway, IMF and Norges Bank -30 -10 10 30 50 -30 -10 10 30 50 1983 1991 1999 2007 Variation One-sided HP-trend Augmented HP-trend 10 year moving average
  • 19. Reference values for CCB in Norway Basel “bufferguide”. Per cent of risk weighted assets. 1983 Q1 – 2013 Q4 19Sources: Statistics Norway, IMF, BIS and Norges Bank 0 0,5 1 1,5 2 2,5 3 3,5 0 0,5 1 1,5 2 2,5 3 3,5 1983 1987 1991 1995 1999 2003 2007 2011 Buffer based on deviation from the Basel Committee's recommended HP trend Buffer based on deviation from alternative HP trend
  • 20. 20 50 100 150 200 50 100 150 200 1976 1984 1992 2000 2008 Key indicators for build-up of CCB Real commercial property prices Banks’ wholesale funding ratio Credit / GDP House prices / disposable income 50 100 150 200 50 100 150 200 1976 1984 1992 2000 2008 50 100 150 200 50 100 150 200 1976 1984 1992 2000 2008 0 20 40 60 0 20 40 60 1976 1984 1992 2000 2008 Historical averageLevel
  • 21. Guided discrection  «Guided discretion» – Weight on rules depends on reliability of indicators – More weight on rules as analytical framework is improved?  More judgment needed in release phase? 21
  • 22. Does it work? Banks’ lending margins on mortgages 18 Jul 2010 – 10 Jun 2014 Credit growth (y-o-y) enterprises Jan 2008 – Apr 2014 -10 0 10 20 30 40 -10 0 10 20 30 40 2008 2010 2012 2014 Bank debt Bond debt 22Source: DNB Markets, Statistics Norway and Norges Bank 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 2010 2011 2012 2013 Risk premium 5-yr covered bond 3m NIBOR - key policy rate Key policy rate Residential mortgage rate Estimated cost of mortgage financing
  • 23. TOWARDS A NEW QUANTITATIVE FRAMEWORK FOR THE CCB
  • 24. Modeling approaches  Empirical cost-benefit analysis: – Benefits: Smaller probability of systemic crisis and less severe crisis – Costs: Less financial intermediation in «normal times»  Policy analysis in structural models: – ESCB’s 3D model – IMF’s MAPMOD 24
  • 25. Logit model estimated on panel of 16 countries Q1 1970 – Q2 2013 25 0 .1.2.3.4.5.6.7.8.9 1 1970q1 1980q1 1990q1 2000q1 2010q1 Quarter 90% 70% 50% 30% Norway 0 .1.2.3.4.5.6.7.8.9 1 1970q1 1980q1 1990q1 2000q1 2010q1 Quarter 90% 70% 50% 30% USA0 .1.2.3.4.5.6.7.8.9 1 1970q1 1980q1 1990q1 2000q1 2010q1 Quarter 90% 70% 50% 30% UK 0 .1.2.3.4.5.6.7.8.9 1 1970q1 1980q1 1990q1 2000q1 2010q1 Quarter 90% 70% 50% 30% Spain Estimated crisis probabilities
  • 26. Marginal effects on crisis probability of different indicators Household Credit to GDP Gap NFE Credit to GDP Gap Wholesale Funding Gap House Prices to Inc. Gap Equity/Assets -4 -2 0 2 4 Marginal Effect on Crisis Probability (pp)
  • 27. A MACROPRUDENTIAL POLICY FRAMEWORK AMUND HOLMSEN, IDA WOLDEN BACHE AND KARSTEN GERDRUP OECD 13 JUNE 2014
  • 28. Logit model for estimating crisis probabilites  Panel of 16 industrialized countries 1970Q1 – 2013Q2 – Australia, Belgium, Canada, Finland, France, Germany, Italy, Japan, Korea, Netherlands, Norway, Spain, Sweden, Switzerland, UK and USA  28 identified crises  Explanatory variables – Total credit to private non-financial sector, households and non-financial enterprises – Nominal and real GDP – House prices and disposable income – Equity prices – Inflation and interest rates – Banking sector variables (leverage and market financing) – Trade weighted global credit and house prices 28