Fundamentals and contagion
mechanisms in the euro area
sovereign bonds markets
Econometric Clinic TT3 meeting 2012/06/14
G. Amisano and O. Tristani
DG-Research
European Central Bank
PRELIMINARY
1
Motivation: euro area spreads
GR spread
FR spread
20
0.8
15
0.6
10
0.4
5
0.2
0
2000
2002
2004
2006
2008
2010
2012
2000
2002
2004
IR spread
2006
2008
2010
2012
2008
2010
2012
2008
2010
2012
IT spread
8
3
6
2
4
1
2
0
2000
2002
2004
2006
2008
2010
2012
2000
2002
2004
PT spread
2006
SP spread
3
8
6
2
4
1
2
0
2000
2002
2004
2006
2008
2010
2012
0
2000
2002
2004
2006
2
Motivation
• Facts:
– very large swings hardly compatible with linear models
– relatively small parallel changes in fiscal fundamentals
– Strong comovements across countries
• Obvious questions:
– What determines these movements?
– To what extent are they justified by the evolution of fiscal
fundamentals in each country?
– Can we disentangle the role of common factors and
contagion effects?
3
Our approach
• Starting point: debt crises can be self-fulfilling. They
are more likely at relatively high levels of debt [and
shorter maturity structure] (Cole and Kehoe, 2000)
• Our approach:
– define the “crisis” as a regime (different from “normal”)
– explicitly allow for probability of crisis regime to depend
on fiscal fundamentals ...
– … but also allow for exogenous changes in investors’ risk
aversion (a common factor)
– … and cross-country contagion (the occurrence of the
crisis regime in another country)
4
Our approach
• Advantages:
– Good empirical fit
– Allows for nonlinear effects: difference between
fluctuations within a regime and transitions between
normal and crisis regimes
– Ability to identify a certain form of cross-country contagion
• Drawbacks:
– Reduced form model
– Lacks strong theoretical restrictions
– No policy implications
5
The model (I)
• Spreads of EA countries bonds at different maturities with
respect to German bonds returns: yit
• Each of the country spreads is modelled as VAR: Panel VAR
framework
• Each country VAR has level and volatility affected by a
discrete regime variable: Markov Switching panel VAR
• Common parameters (pooled) and country specific
parameters
• Transition probabilities not constant in time
• Amisano and Fagan (2012), Amisano, Bragoli, Colavecchio
and Fagan (2012).
6
The model (II)
• Each country (i=1,2,..m) vector yit (n x 1) of spreads modelled
as VAR with regime shifts:
y it  c sit  A sit y it 1  Σ1i ,/s2 v it , i  1,2,...,m
it
• Note that all coefficients can be allowed to vary across regimes:
– Intercept (level)
– Autoregressive coefficients (persistence)
– Covariance matrix (volatilities/correlations)
7
The model (III)
• Transition probabilities across regimes depend on
–
–
–
–
Country specific fundamentals
Common observable factors
Persistence of own regime
Other countries regime dynamics (contagion)
8
The model (IV)
• Transition probs as a probit function:
p( sit  1 | s t 1 , z it 1 )   ( i1   i 2  sit 1   i 3  ( sit 1 )  β´z it 1  it )
 ( ) 



1
 1 2
exp z dz
2
 2 
 


 I   s jt  0  (t ype1 contagion)


sit 1    j i
 s jt (t ype2 contagion)
 j i
it ~ NID(0,1)
Cov(it , j )  0, i  j , t ,
9
The model (V)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-2.5
contagion
no
contagion
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
10
The model (V)
•
Some comments on transition probabilities
– When b = 0 and i3 = 0, we have time homogeneous
Markov Switching process with each country evolving
independently
– When i3 ≠ 0 we have contagion from other
countries
• Type 1 mechanism: operates when at least one other
country is in crisis regime
• Type 2 mechanism: more other countries in distress
exert a stronger pull towards crisis.
– Shocks it and variations in fundamentals (Dzit) have
nonlinear effects
11
The model (VII)
•
•
Parameters in the country specific VAR can be either
pooled, country specific or partially pooled (random
effects)
Parameters in the transition probabilities (the gammas):
– Slope parameters (b) pooled
– Other parameters are country specific (i1, i2 , i3) or
(partially) pooled
12
Potential drivers of transitions
•
•
•
•
Global risk appetite. BAA-AAA spread
Business cycle variables (IP): not relevant
Fiscal fundamentals (govt net lending as % GDP)
Monthly data from 2001:m1 to 2011:m10 for 6 countries: FR,
GR, IT, IE, PT, SP
13
Potential drivers of transitions
Attitude with respect to risk (BAA-AAA spread)
3
2.5
2
1.5
1
2000
2002
2004
2006
2008
2010
2012
14
Potential drivers of transitions
Fiscal fundamentals
FR z2
GR z2
-5
-2
-4
-10
-6
-8
2000
-15
2002
2004
2006
2008
2010
2012
2000
2002
2004
IR z2
2006
2008
2010
2012
2008
2010
2012
2008
2010
2012
IT z2
0
-1
-10
-2
-3
-20
-4
-30
-5
-40
2000
2002
2004
2006
2008
2010
2012
2000
2002
2004
PT z2
2006
SP z2
-4
0
-6
-5
-8
-10
-10
2000
2002
2004
2006
2008
2010
2012
2000
2002
2004
2006
15
Preliminary results: summary
•
•
•
•
•
Cross-country analysis: FR, GR, IE, IT, PT, SP
3 factors: net Gov. lending (in % of GDP); risk aversion; lagged
cross-country “contagion”
All countries have 2 regimes,
RA and fiscal fundamentals very relevant
Lagged other country regimes relevant
16
Preliminary results: summary
prior type
prior
mean
prior std
posterior
mean
posterior
std
gamma(1,i)
intercept
Gaussian
-1.5
1
-2.17
0.16
gamma(2,i)
lag own state
Gaussian
3
1
2.47
0.26
gamma(3,i)
lag other
countries
Gaussian
0.5
1
1.28
0.22
beta(1)
RA
Gaussian
0
1
0.06
0.1
beta(2)
Fiscal
Gaussian
0
1
-0.33
0.15
17
What drives changes in regimes? The case for
Italy: contagion (I)
•
Probs to move into crisis regime
0.3
NO CONTAGION
0.25
ACTUAL
0.2
0.15
0.1
0.05
0
2006
2007
2008
2009
2010
2011
18
What drives changes in regimes? The case for
Italy: fundamentals (II)
•
Probs to move into crisis regime
0.3
0.25
0.2
0.15
0.1
SAMPLE MEAN
0.05
RISK_AVERSION
FISCAL
0
2006
2007
2008
2009
2010
2011
19