Transcript Rapporto MET 2009

INTEGRATION AND CONVERGENCE
IN EUROPEAN ELECTRICITY
MARKETS
C. A. BOLLINO - D. CIFERRI - P. POLINORI
Department of Economics, Finance and Statistics - University of Perugia
30th USAEE/IAEE NORTH AMERICAN CONFERENCE
Oct 9-12, 2011,WASHINGTON, DC
Research Questions
• The liberalization process of electricity markets in Europe is
more than a decade old.
• In the meantime, mergers and restructuring of big players in
generations at the international level make likely that decisions
and price strategies are taken simultaneously on several markets,
based on a common set of available information.
• There is large evidence that organized electricity spot markets
are far from the ideal competitive model and therefore all sorts of
behaviors and shocks may influence price formation in those
markets, ranging from international fuel price, local meteo, and
local market power behavioral shocks.
• In this respect, we want to investigate whether there exists some
information signaling among different European markets.
Data
• Data of (the log of) Electricity prices for DE, AT, FR, and IT are
used
• Estimation sample period: from 1/4/2004 to 8/3/2010
DE
AT
FR
IT
Mean
3.75
3.78
3.79
4.20
Median
3.71
3.73
3.74
4.22
Maximum
5.05
4.99
5.05
4.92
Minimum
0.07
2.51
2.04
0.16
Std. Dev.
0.42
0.41
0.44
0.30
Skewness
-0.25
0.13
-0.07
-2.22
Kurtosis
5.56
2.90
3.21
25.76
Euro/MWh
20 40 60 80
Euro/MWh
EEX
100120
40 60 80
Average hourly electricity prices
0 1 2 3 4 5 6 7 8 9101112131415161718192021222324
Ora
mGER_we
0 1 2 3 4 5 6 7 8 9101112131415161718192021222324
Ora
mGER
mITA_we
0 1 2 3 4 5 6 7 8 9101112131415161718192021222324
Ora
Source: Data Stream,
mITA
mAUT
20 40 60 80
POWER NEXT
Euro/MWh
20 40 60 80
Euro/MWh
EXAA
mAUT_we
IPEX
0 1 2 3 4 5 6 7 8 9101112131415161718192021222324
Ora
mFRA_we
mFRA
100200300400
IPEX
0
0
Euro/MWh
EEX
100200300400
Euro/MWh
Hourly electricity prices
12may200412dec2005
16:53:20 14jul2007
09:46:40 02:40:00
11feb200913sep2010
19:33:20 12:26:40
12may200412dec2005
16:53:20 14jul2007
09:46:40 02:40:00
11feb200913sep2010
19:33:20 12:26:40
mydate
mydate
EEX
IPEX
IPEX
100200300400
POWERNEXT
0
0
Euro/MWh
EXAA
100200300400
Euro/MWh
EEX
12may200412dec2005
16:53:20 14jul2007
09:46:40 02:40:00
11feb200913sep2010
19:33:20 12:26:40
12may200412dec2005
16:53:20 09:46:40
14jul2007 02:40:00
11feb2009 13sep2010
19:33:20 12:26:40
mydate
mydate
EXAA
EXAA
POWERNEXT
POWERNEXT
Red line week end and Blu line week day
Numbers of hour in which markets set prices grater than 400Euro/MWh: EEX = 20; EXAA = 23; POWERN. = 42
Source: Data Stream,
The Empirical Strategy
• According to stochastic definitions of convergence and common
trends based on cointegration analysis (Bernard, 1991), a
necessary (but not sufficient) condition for convergence among
countries and/or markets is that there should be n-1
cointegrating vectors for a sample of n countries or markets.
• We use a multivariate specification for the four equation of
electricity spot prices according to a Vector Auto Regressive
(VAR) process of order p
p
y t   Aly y t l  εt
l 1
where
yt  [ DE, AU, FR, IT ]
The Empirical Strategy
• The previous equation can be represented in its
isomorphic Vector Error Correction (VEC) form:
p 1
Δy t  Π  y t 1   Pl y Δy t l  εt
y
l 1
• On the basis of the rank of matrix Π y , it is possible to
identify different long-run equilibrium path for the
electricity prices in the models.
• In any intermediate result with a reduced rank of matrix
we have a long run representation of the integration
process between markets.
The Empirical Strategy
y
• In the presence of cointegration, the rank of matrix Π
is reduced to r < 4 and can be decomposed as:
• ∏y = α β’
• where:
• α shows the feedback coefficients
• β shows the theory based long-run relationship
coefficients
Preliminary Analysis and VECM specification
• We test for unit root behavior of each of the four series (ADF and
PP). In each case, we are unable to reject the unit root-null
hypothesis at conventional nominal significance levels.
• VECM specification: we allow for a lag length of 22 (in levels)
and an unrestricted constant term in the VECM specification.
• The choice of optimal lag length has been done according with
the lag-exclusion (the initial specification includes 31 lags)
method at 10% level of significance
• The results of the main univariate and multivariate diagnostic
tests (for serial correlation, normality, heteroskedasticity and
ARCH components) indicate that estimated residuals resemble
white-noise process in a satisfactory way at both single equation
and system level.
Long- run properties
(a) Cointegration rank
Trace test
Maximum eigenvalue test
p-r
r
Eigenvalue
Statistics
95% cv
Statistics
95% cv
4
0
0.0359
138.279
40.175
78.250
24.159
3
1
0.0182
60.029
24.276
39.474
17.797
2
2
0.009
20.554
12.320
20.547
11.225
1
3
0.000
0.008
4.130
0..008
4.130
(b)Test of exclusion
r
dgf
5% .v.
DE
3
3
7.815
69.601
AT
(0.000)
68.726
FR
(0.000)
33.561
IT
(0.000)
19.986
(0.000)
(c)Test of stationarity
R
dgf
5%c.v.
DE
3
1
3.841
9.848
(c)Test of
AT
(0.002)
10.090
FR
(0.001)
8.656
IT
(0.003)
11.420
(0.001)
weak exogeneity
r
dgf
5%c.v.
DE
3
3
3.841
5.678
AT
(0.128)
7. 923
FR
(0.048)
12.968
IT
(0.005)
16.377
(0.000)
Hypothesis testing on Beta vectors
 pDE 
1  1 0 0  

p
AT 



 pi ,t 1  1 0  1 0 
 p FR 
1 0 0  1 

 pIT  t 1
LR χ2(3)=5.925[0.115]
The long run equilibrium conditions
• This representation implies three different bilateral integration
processes between Germany market (the common trend) and the
other ones.
• Using a standard -distributed LR ratio test with 3 degrees of
freedom, the test statistics (5.925), calculated using the Bartlett
small-sample correction (with estimated factor of 4.72), indicate
that the restrictions are not rejected by the data at the usual
significance levels (p-value of 0.115)
Granger-Causality
• We test also for Granger-Causality in the whole system in order to
verify if DE-prices “Granger-cause” the other variables in the model
• Thus, we perform the usual F-test on the significance of lagged
values of DE in the equations of AU, FR and IT.
• Under H0: " DE" does not Granger-cause « AU, FR, IT"
Test statistic l = 1.6592;
•
pval-F( l; 57, 8264) = 0.0014
The test confirms that DE cause “AU, FR, IT"
• As robustness, we develop also a test for Instantaneous Causality.
• H0: No instantaneous causality between “DE" and “AU, FR, IT“
Test statistic: c = 828.0805 pval-Chi( c; 3) = 0.0000
USAEE/IAEE North American Conference, Washington, DC - October 9-12, 2011
Cointegration vectors
Persistence Profiles
• Persistence profiles (Pesaran and Shin,1996) consider the effect
of a system-wide shocks on cointegrating relations, and it is given
by:
h( j z t , N ) 
j AN  AN  j
j   j
for i  1, 2.., r and N  0,1, 2...
• The value of this profile is equal to 1 on impact, but should tend
to zero as N→ ∞ if βj is indeed a cointegrating vector.
• The persistence profiles are also useful in the case of time series
that are close to being I(1). If this is the case, the persistence
profiles eventually converge to zero, but can be substantially
different from zero for a protracted period.
• The persistence profiles, viewed as a function of N, provides
important information on the speed at which the system-wide
shocks effect disappears.
Persistence Profiles
Simulation period: 5 months
The restricted VECM
• The next step is to estimate a parsimonious VECM by 3SLS in
which statistically irrelevant parameters are deleted through the
SER/TP method. The AIC criterion with t=1,6 is used as a
significance threshold level for short-run parameters.
• The choice is motivated by the opinion that, in the reduction
process of the model, it is preferable to maintain the coefficients
with uncertain significance.
VECM model estimated 3SLS- The long run
Equations are obviously affected by the cointegration residuals
which identify the long run convergence equilibrium between each
market and the German system.
Moreover the (absolute) values of the feedback coefficients indicate
that the speed of adjustment towards equilibrium is higher for
Austria.
Finally, there is evidence of some influence of each error on the
other market equations.
The Long Run
 DE
AT
FR
IT
1, t 1
-
-0.725
(0.035)
-0.084
(0.043)
-
2,t 1
-
0.122
(0.018)
-0.248
(0.021)
-0.078
(0.020)
3, t 1
-
-0.064
(0.013)
-0.071
(0.006)
-0.365
(0.019)
Note: Standard errors in round brackets
Dynamic Simulation
• If we consider the VECM representation, we can always rewrite
the model in MA representation as:
X t  C   i  C * ( L) t  det ...
where C     with rank  n  r
• With an appropriate choice of the matrix C we are able to
perform FEVD analysis separating (n-r) permanent shocks from
(r) transitory shocks
Dynamic Simulation: FEVD
AT
FR
IT
Mean
Global shock
79.91
78.03
71.40
76.45
Regional shock
20.01
21.48
28.39
23.29
(Idiosyncratic shock)
(12%)
(85%)
(50%)
Simulation period: 5 months
Conclusions
• German market behavior appears as the common trend for other
regional markets, thus providing signaling information.
• This can be explained in two ways: (i) DE is the largest market in
Central Europe and it is taken as a reference; (ii) pricing in
electricity markets is dominated by peak-load plants, which
typically exhibit CCGT technology (i.e. gas fired) and gas
marginal price is largely influenced by German market operators
• Persistence appears to be higher in FR . This is no surprise, given
that the French electric system is the most un-flexible (because of
its very high nuclear share).
Conclusions
• The permanent shock is associated to the common trend of the
system (that is the German electricity spot price) and represents the
global-external shocks that hit in a symmetric way the other
markets.
• Individual temporary shocks identify idiosyncratic disturbances.
Idiosyncratic shocks are then aggregated so as to quantify the overall
relevance of regional factors in explaining spot prices fluctuations.
• The figures represent the percentage of the variance of each variable
of the system explained by global, regional and idiosyncratic shocks,
where the latter (in italics) are expressed as a percentage of regional
disturbances.
• The last column (mean) presents the average contribution of the
shocks over the entire simulation period (5 months).
Conclusions
• FEVD analysis shows that IT is the market with lowest share of
global shock compared to other countries. Thus the signaling
effect of global shocks in price formation is the least important in
the Italian case
• The fact that roughly 1/4 of FEVD is not explained by a global
shock (which is typically the fuel price shock) indicates that there
are other factors, like non competitive strategic behavior,
influencing equilibrium prices, which motivates future research.