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

5 Sources: 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

19 Sources: 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 20080

20

40

60

0

20

40

60

1976 1984 1992 2000 2008

Historical average Level

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

22 Source: 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 2010q1Quarter

90% 70%

50% 30%

Norway

0.1

.2.3

.4.5

.6.7

.8.9

1

1970q1 1980q1 1990q1 2000q1 2010q1Quarter

90% 70%

50% 30%

USA0

.1.2

.3.4

.5.6

.7.8

.91

1970q1 1980q1 1990q1 2000q1 2010q1Quarter

90% 70%

50% 30%

UK

0.1

.2.3

.4.5

.6.7

.8.9

11970q1 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

Effe

cts

with

Res

pect

to

-4 -2 0 2 4Marginal 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