Dating Systemic Financial Stress Episodes in the EU … · Dating Systemic Financial Stress...

Motivation Step 1 Step 2 Results Benchmarking Conclusion

Dating Systemic Financial Stress Episodes inthe EU Countries

Thibaut DUPREY1

Benjamin KLAUS2

Tuomas PELTONEN3

1Bank of Canada2European Central Bank

3European Systemic Risk Board

The views are those of the authors and do not necessarily reflect those of Bank of Canada,

the European Central Bank, the Eurosystem or the European Systemic Risk Board.

Central Bank of Brazil, 9 August 2017


Classifying events for a better analysis of macropru

The analysis of macroprudential policies requires a chronology ofsystemic crises

• 2008 can be safely ( ?) regarded as a systemic financial crisis

• But the classification of all other events rely on expertjudgement...

We provide a mechanistic identification of systemic financial stress


Aim = identify systemic financial stress

Low financial stressHigh financial stress

Hig

hgr

owth

Low

grow

th

tranquil regime financial stress

recession systemic stress

1


Overview

1 Construct 27 financial stress indices for all EU countries

I Financial cycle research : financial stress index literature

2 Identify systemic financial stress episodes

I Business cycle research : identifying business cycle turning pointsusing a suite of non-linear models

• Method 1 : Univariate Markov switching with algorithm

• Method 2 : Markov switching vector autoregressive model

• Method 3 : Threshold vector autoregressive model


STEP 1 : Construct 27 financial stress indices(in the spirit of CISS : Hollo et al., 2012)

Equity sub-index

Bonds sub-index

FX sub-index

Pairwisecross-correlationsρofindices

I

Country-Level Index of

volatility stocks

cumulated drop in stocks

volatility of government bond

cumulated government bond spread

volatility effective exchange rate

cumulated change effective exchange rate

volatility idiosyncratic bank returns

cumulated drop bank stocks

mortgage lending spread

cumulated housing price drop

Bank sub-index

Housing sub-index

norm

alizedin

the[0;1]space

using

theem

piricalcumulativedensity

Financial Stress (CLIFS)

CLIFSt = It ∗ Ct ∗ I′

t

Ct =

1 . . . ρi,j,t...

. . ....

ρi,j,t . . . 1

5∗5

It = [Ii,t . . . Ij,t]1∗5

End of 2006 Dataset publicly available :http://sdw.ecb.europa.eu/browse.do?node=9693347

http://sdw.ecb.europa.eu/browse.do?node=9693347


Country-Level Index of Financial Stress (CLIFS)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1 2 3 4 5 67

8 910

11

12

13

14

15

01/1

965

01/1

967

01/1

969

01/1

971

01/1

973

01/1

975

01/1

977

01/1

979

01/1

981

01/1

983

01/1

985

01/1

987

01/1

989

01/1

991

01/1

993

01/1

995

01/1

997

01/1

999

01/2

001

01/2

003

01/2

005

01/2

007

01/2

009

01/2

011

01/2

013

01/2

015

Cou

ntry

Lev

el In

dex

of F

inan

cial

Str

ess

(CLI

FS

)

1 - first oil shock ; 2 - second oil shock ; 3 - Mexican debt crisis ; 4 - Black Monday ; 5 -crisis of the European exchange rate mechanism ; 6 - Peso crisis ; 7 - Asian crisis ; 8 -Russian crisis ; 9 - dot-com bubble ; 10 - subprime crisis ; 11 - Bankruptcy of LehmanBrothers ; 12 - 1st bailout Greece ; 13 - 2nd bailout Greece ; 14 - Election of AlexisTsipras in Greece ; 15 - Brexit vote.


Contribution of the cross-correlations

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

1 2 3 4 5 67

89

10

11

12

13

14

15

01/1

965

01/1

967

01/1

969

01/1

971

01/1

973

01/1

975

01/1

977

01/1

979

01/1

981

01/1

983

01/1

985

01/1

987

01/1

989

01/1

991

01/1

993

01/1

995

01/1

997

01/1

999

01/2

001

01/2

003

01/2

005

01/2

007

01/2

009

01/2

011

01/2

013

01/2

015

Con

trib

utio

n of

the

cros

s-co

rrel

atio

ns to

the

CLI

FS

1 - first oil shock ; 2 - second oil shock ; 3 - Mexican debt crisis ; 4 - Black Monday ; 5 -crisis of the European exchange rate mechanism ; 6 - Peso crisis ; 7 - Asian crisis ; 8 -Russian crisis ; 9 - dot-com bubble ; 10 - subprime crisis ; 11 - Bankruptcy of LehmanBrothers ; 12 - 1st bailout Greece ; 13 - 2nd bailout Greece ; 14 - Election of AlexisTsipras in Greece ; 15 - Brexit vote.


Does financial stress matter ?Industrial production growth per quantiles of CLIFS

-8

-6

-4

-2

0

2

4

6

8

0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100

Average annual industrial production growth on the y-axis.Quantiles of the country-specific financial stress indices on the x-axis.


STEP 2 : How to identify systemic financial stressepisodes ?

Low financial stressHigh financial stress

Hig

hgr

owth

Low

grow

th

tranquil regime financial stress

recession systemic stress

1


Method 1 : Markov-Switching with selection algorithmHamilton (1989) Markov-Switching framework

1 Identify periods of high financial stress :

CLIFSt = µSt + βCLIFSt−1 + σSt εt

Transition probability across regimes St ∈ {L,H} driven by ahidden two-state Markov chain :

P (St |St−1 ) =

[p =

exp(θp)1+exp(θp)

1− p

1− q q =exp(θq)

1+exp(θq)

]

regime H when µH > µL, and financial stress period when :

1financialstress = {P (St = H) > 0.5}

2 Overlap with at least six consecutive months of real economicstress (drop in industrial production and GDP correction)


Method 2 : Markov switching vector autoregressionbuilds on toolbox of Haroon Mumtaz

Bivariate model to capture joint change in dynamics of industrialproduction growth (gIPI) and CLIFS

gIPIt = µSt1 +

n∑p=1

(βSt

1,1,pgIPIt−p + βSt1,2,pCLIFSt−p

)+ εt,1

CLIFSt = µSt2 +

n∑p=1

(βSt

2,1,pCLIFSt−p + βSt2,2,pgIPIt−p

)+ εt,2

The tranquil or systemic financial stress state St ∈ {L;H} isunobservable : same hidden two-state Markov chain as before.


Method 3 : Threshold vector autoregressive modelbuilds on toolbox of Gabriel Bruneau

Different joint dynamics above (H) or below (L) an estimatedpercentile of the CLIFS

CLIFSt = µSt1 +

n∑p=1

(βSt

1,1,pCLIFSt−p + βSt1,2,pgIPIt−p

)+ εt,1

gIPIt = µSt2 +

n∑p=1

(βSt

2,1,pgIPIt−p + βSt2,2,pCLIFSt−p

)+ εt,2

The observed regime is given by :

St =

{H if CLIFSt−1 ≥ τL if CLIFSt−1 < τ

where τ is estimated.


Robustly identifying systemic financial stress events

For each 27 countries we have up to 12 models• different framework, with different specifications, using CLIFS or

the banking and housing extensions

For each country, combine dummies Sm,t for periods of systemicfinancial stress over all models m

• robust to model uncertainty

Systemic Stress Index SSIt =∑

m Sm,t∑m 1m

∈ [0;1]

Definition of systemic financial stress :• starts when SSIt > 0.5• ends when SSIt < 0.25


Zoom-in : Systemic Stress Indices, selected countries

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

01/1

965

01/1

969

01/1

973

01/1

977

01/1

981

01/1

985

01/1

989

01/1

993

01/1

997

01/2

001

01/2

005

01/2

009

01/2

013

Sys

tem

ic C

rises

Inde

x

Por

tuga

l

(a) Portugal

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

01/1

965

01/1

969

01/1

973

01/1

977

01/1

981

01/1

985

01/1

989

01/1

993

01/1

997

01/2

001

01/2

005

01/2

009

01/2

013

Sys

tem

ic C

rises

Inde

x

Spa

in

(b) Spain


Zoom out : Timing of systemic financial stress in 2008

01/2

007

01/2

008

01/2

009

01/2

010

01/2

011

01/2

012

01/2

013

01/2

014

IEBEUKDKIT

NLATESPTHUCZDEFRSEGRHRLUROSILTFI

LVMTCYBGPLSK


Zoom out further : Financial market stress andintensity of real economic stress

02/1

964

02/1

969

02/1

974

02/1

979

02/1

984

02/1

989

02/1

994

02/1

999

02/2

004

02/2

009

02/2

014

IEBEUKDKIT

NLATESPTHUCZDEFRSEGRHRLUROSILTFI

LVMTCYBGPLSK

-30%

-20%

-10%

No stress


No zoom : Systemic financial stress is costlyBi-product of the Threshold VAR

Response of industrial production to a shock of 1% on CLIFS(black : VAR without regime change ; red : high stress ; blue : tranquil)

Weighted average IRF - Shock to CLIFS in low (blue) or high (red) stress, or VAR in dashed black

Months

0 6 12 18 24

y-o

-y g

row

th r

ate

, %

-6

-5

-4

-3

-2

-1

0Industrial Production Index

Months

0 6 12 18 24

level, fro

m 0

(m

in)

to 1

00 (

max)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1CLIFS


What are systemic financial stress episodes ? Notordinary recessions

Number Length GDP CLIFS mean

Definition of recession : events loss pcent corr

Ordinary recessions

Two quarters 76 11 -0.79 50 -7

Two consecutive quarters 45 7 -1.52 50 -18

Before 2008, two quarters 57 10 -0.77 51 -16

Recessions with

financial market stress

Two quarters 74 18 -4.10 66 28

Two consecutive quarters 42 13 -4.17 70 31

Before 2008, two quarters 39 14 -1.71 72 28


What are systemic financial stress episodes ? Sectoraldecomposition for selected countriesBi-product of the Markov-switching model

Syst fin stress

Equity crash

Sovereign stress

Foreign exchange

Banking stress

Housing stress

IPI drop

GDP drop

(c) Portugal

Syst fin stress

Equity crash

Sovereign stress

Foreign exchange

Banking stress

Housing stress

IPI drop

GDP drop

(d) Spain


Comparison of continuous stress measures withexpert-based crises : AUROC

CLIFS SSI

panel average panel average

Detken et al. (2014)Banking 0.71 0.76 0.82 0.83

Babecky et al. (2012)Banking 0.66 0.72 0.80 0.83Currency 0.71 0.68 0.82 0.74Debt 0.94 0.94 0.91 0.95

Leaven and Valencia (2013)Banking 0.75 0.77 0.87 0.88

Reinhart and Rogoff (2011)Banking 0.70 0.75 0.84 0.87Currency 0.53 0.51 0.67 0.69Equity 0.66 0.68 0.74 0.77


Comparison of model-based systemic financial stressepisodes with expert-based crises

Share of model Share of expertidentified events also identified crises alsocaptured by experts captured by models

Spain 1.00 0.43Portugal 1.00 0.14

Total 0.81 0.43Mean 0.83 0.55

In particular, we capture 96% of the systemic banking crises ofLeaven and Valencia (2012)


Wrap-up

Paper combines measurement of financial stress with detectionof turning points for a mechanistic dating of systemic financialstress episodes

Upsides :• Get model-implied systemic financial stress periods• Integrate real and financial cycle dynamics (=systemic)• Consistent with most expert-based datasets• Robust to alternative measures of financial stress• Robust to model uncertainty• Robust to event reclassification once new data arrive

Downsides :• Hard to capture causal relation between financial stress and real

economic stress

Follow up work : "A new database for financial crises in Europeancountries", ECB Occasional Paper No. 194, July 2017.

https://www.ecb.europa.eu/pub/pdf/scpops/ecb.op194.en.pdf

Dating Systemic Financial Stress Episodes in the EU … · Dating Systemic Financial Stress...

Documents

Transcript of Dating Systemic Financial Stress Episodes in the EU … · Dating Systemic Financial Stress...