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The Systemic Risk of Energy Markets
Diane Pierret
Université Catholique de Louvain & NYU
The Economics and Econometrics of Commodity Prices,
Rio de Janeiro 2012
Systemic Risk
Systemic risk: the risk of the financial sector as a whole being in distress
and its spillover to the economy at large
Systemic risk is related to
1 dependence, causality, externality, interconnectedness, spillover effects
2 market integration, contagion, commonality
3 shocks, crises, extreme events
Does this exist in energy markets?
How to measure it?
How does this affect the rest of the economy?
1 / 27
Energy Systemic Risk
Energy Systemic Risk: the risk of an energy crisis raising the prices of all
energy commodities with negative consequences for the real economy.
increased dependence of the economy on energy
increased energy market integration
extreme energy market shock from the supply side
negative consequences for the energy sector and the rest of the
economy
Energy Security: ”the uninterrupted availability of energy sources at an
affordable price.” (IEA)
2 / 27
Related literature
This project relates to the literature on
Past energy crises and the impact of energy prices on the economy
(Hamilton, 1983)
Systemic Risk
Network (Nier et al. (2007), Battiston et al. (2009), Billio et al.
(2010), Hautsch et al. (2011), Diebold and Yilmaz (2011))
Co-movements (Billio et al. (2010), Kritzman et al. (2011), Acharya et
al. (2010), Brownlees and Engle (2011))
Energy prices co-movements
Cointegration and causality in the mean (Bunn and Fezzi (2008))
Multivariate Volatility (Chevallier (2012), Bauwens et al. (2012))
Copulae (Benth and Kettler (2010), Boerger et al. (2009), Gronwald et
al. (2011))
3 / 27
Outline
1 The Energy Systemic Risk Measure: EnSysRISK
2 Econometric methodology and application
Causility in means and variances
Factor model and tail expectations
3 EnSysRISK and the impact on the economy
4 / 27
Outline
1 The Energy Systemic Risk Measure: EnSysRISK
2 Econometric methodology and application
Causility in means and variances
Factor model and tail expectations
3 EnSysRISK and the impact on the economy
5 / 27
EnSysRISK
The conditional MES of an energy asset (Acharya et al. (2010), Brownlees and
Engle (2011)) is given by
MESit = Et−1 (rit |energy crisis)
Corresponding systemic prices are derived from past price levels
syspriceit = pit−1 ∗ exp(MESit)
EnSysRISK: the total cost of an energy asset to the non-energy sector during an
energy crisis
EnSysRISKit = max(0,syspriceit ∗wit),
where wit is the quantity exposure of the economy to asset i at time t.
For an energy contract with maturity and delivery period ν , the exposure at time
t is
wit(ν) = ςiEt−1 (finconsτ − inv τ) for τ ∈ [t +ν ; t +2ν−1]
6 / 27
The MES of energy markets (1/2)
Energy crisis: an extreme positive energy market shock from the supply side
MESit(C ) = Et−1 (rit |rEnM,t > C , rM,t < 0) ,
where rEnM,t is the energy market return, rM,t is the return of the non-energy
sector, and C represents the VaR of the energy market at (1−α)%.
An extreme increase in energy prices...
Not only oil prices: “While the security of oil supplies remains important,
contemporary energy security policies must address all energy sources” (IEA
2011)
Integration dimensions: underlying energy (oil, coal, natural gas, electricity,
carbon), maturity/delivery, region
Futures prices
7 / 27
Phelix Baseload index = average of 24 day-ahead spot prices
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
100
200
300
400
500
EEX - Phelix Base Hr.01-24 E/Mwh
EEX - Phelix Peak Hr.09-20 E/Mwh
Futures are “insurance contracts” providing protection against the price
uncertainty in the spot market
!"#$%$ &'(' Spot and future trading in the EEX
!"#$%&'(%)*
!"#$%(%)*
)*+,-./0/,*121#3
%4/,561$74089/6:
;4+<
+,-./%%0)*1
&%2 34%5
=/+,0*,-./0/,*1>1#3
#4?0@-./0/,*1(1#3
)*+,-./0/,*121#3
#4?0@-./0/,*1A1#3
%4/,561$74018B*55:
Source: University St Gallen
The MES of energy markets (2/2)
Methodology: the conditional MES as a function of mean, volatility and tail
expectation
MESit(C ) = Et−1 (µit +σituit |rEnMt > C , rMt < 0)
= µit +σitEt−1 (uit |rEnMt > C , rMt < 0)
where µit and σ2it are the conditional mean and variance of asset return i and
uit = (rit −µit)/σit are the standardized residuals.
Separate causality from common factor exposure:
Causality in µit and σit
Heteroskedasticity and causality are removed to concentrate on the ’pure’
commonality or contagion phenomenon (Forbes and Rigobon (2002), Billio
and Caporin (2010))
Common factors in standardized residuals: uit = f (yt ,ζit)
8 / 27
Outline
1 The Energy Systemic Risk Measure: EnSysRISK
2 Econometric methodology and application
Causility in means and variances
Factor model and tail expectations
3 EnSysRISK and the impact on the economy
9 / 27
EEX futures, energy spot and DAX industrial indices
10 EEX futures
Electricity: Phelix financial futures (M, Q, Y maturity)
Natural gas: Gaspool physical futures (M, Q, Y maturity)
Coal: ARA financial futures (M, Q, Y maturity)
EU emission allowances: EUA financial futures (Y maturity)
3 energy spot indices highly correlated to EEX futures returns
Brent crude oil
European coal
EUA
1 DAX industrial index (energy consumers) = non-energy index
Market areas
NetConnect Germany (NCG):
· Open Grid Europe GmbH
· Bayernets GmbH
· Eni Gas Transport Deutschland
· GRTgaz Deutschland GmbH
· GVS Netz GmbH
· Thyssengas GmbH (planned in 2nd quarter 2011)
GASPOOL:
· Gasunie Deutschland
· Ontras – VNG Gastransport GmbH
· Wingas Transport GmbH & Co. KG
Title Transfer Facility (TTF): (planned in 1st quarter 2011)
· Gas Transport Services B. V.
Active trading participants on the EEX Natural Gas Market
22 c o n n e c t i n g m a r k e t s
Natural Gas
Market Areas
EEX offers trading in natural gas on the Spot Market for the NetConnect Germany, GASPOOL and TTF (planned for 1st quarter 2011) market areas. On the Derivatives Market gas trading transactions can be concluded in the NCG and GASPOOL market areas.
Spot Market Derivatives Market
Trading Participants
EEX has been recording a continued positive trend as regards the number of trading participants: At the end of the year 2010 the number of trading participants in gas trading had increased to a total of 97 comparing with 76 companies at the beginning of the year. Thus, EEX is the gas exchange in Continental Europe with the highest number of trading participants.
60
50
40
30
20
10
01/2010 2/2010 3/2010 4/2010
3639
4648
26 26 2722
Source: EEX 10 / 27
Outline
1 The Energy Systemic Risk Measure: EnSysRISK
2 Econometric methodology and application
Causility in means and variances
Factor model and tail expectations
3 EnSysRISK and the impact on the economy
11 / 27
Price series
MPhelix (EUR/MWh)
2008 2010 2012
25
50
75
100
Source: Datastream
MPhelix (EUR/MWh) QPhelix (EUR/MWh)
2008 2010 2012
25
50
75
100 QPhelix (EUR/MWh) YPhelix (EUR/MWh)
2008 2010 2012
25
50
75
100 YPhelix (EUR/MWh) MGaspool (EUR/MWh)
2008 2010 2012
10
20
30
MGaspool (EUR/MWh)
QGaspool (EUR/MWh)
2008 2010 2012
20
30
40 QGaspool (EUR/MWh) YGaspool (EUR/MWh)
2008 2010 2012
20
30
40YGaspool (EUR/MWh) MARA (EUR/ton)
2008 2010 2012
50
100
150 MARA (EUR/ton) QARA (EUR/ton)
2008 2010 2012
50
100
150 QARA (EUR/ton)
YARA (EUR/ton)
2008 2010 2012
75
100
125
150 YARA (EUR/ton) YEUA (EUR/ton CO2e)
2008 2010 2012
10
20
30
YEUA (EUR/ton CO2e) MLCX EUA spot (EUR/ton CO2e)
2008 2010 2012
100
150
200
MLCX EUA spot (EUR/ton CO2e) Brent crude oil (EUR/barrel)
2008 2010 2012
25
50
75
100 Brent crude oil (EUR/barrel)
MLCX European coal spot (EUR/ton)
2008 2010 2012
100
200
300
MLCX European coal spot (EUR/ton) DAX Industrial (EUR)
2008 2010 2012
2000
3000
4000DAX Industrial (EUR)
12 / 27
Cointegration and Granger-causality in the mean
Augmented Vector Error Correction Model (VECM) for the joint mean of the
(n×T ) matrix of returns rt capturing cointegration, autocorrelation, causality,
and seasonality
rit = πiη �ln(pt−1)+
K
∑k=1
δ �ik rt−k +M
∑m=1
θ �imxt−m +ϕ �i qt + εit
where η are the cointegrating vectors,πi are error-correction parameters,δik is a (n×1) vector of autocorrelation and Granger-causal parameters of order k,xt−m are exogenous variables lagged by m days andqt are deterministic (seasonal) factors.
Similar to Bunn and Fezzi (2008), except that all energy products are here
considered to be endogenous variables (as part of the ’system’).
13 / 27
Multiplicative Causality GARCH modelMultiplicative Causality GARCH model for the variance with a GARCH
component and an interaction component
εit = σituit =�
φitgituit
where
git = (1−αii −βi −γii
2)+αii
� ε2it−1
φit−1
�+βigit−1 + γii
� ε2it−1
φit−1
�I{εit−1<0},
φit = f (u1t−1, ...,ui−1,t−1,ui+1,t−1, ...,unt−1) li (t),
I{εit−1<0} is a dummy variable equal to one when the past shock of asset i is
negative, and li (t) is a deterministic function of time.
In this application:
φit = ci exp
�n
∑j=1,j �=i
(ϑijujt−1 +αij |ujt−1|)+κ �i dt
�
where dt are deterministic terms including seasonal dummies.14 / 27
Networks of Causal Relationships
Causal relationships reflect physical relationships in the energy market
(subsitution, merit-order) and spillover effects
Mean Network Variance Network
15 / 27
Multiplicative Causality GARCH VariancesLog-likelihood GARCH = -27335
Log-likelihood MC-GARCH = -27059
2008 2009 2010 2011
10
20
30
MC-GARCH Electricity GARCH Electricity
2008 2009 2010 2011
10
20
MC-GARCH Natural Gas GARCH Natural Gas
2008 2009 2010 2011
10
20
MC-GARCH Coal GARCH Coal
2008 2009 2010 2011
10
20
30 MC-GARCH Carbon GARCH Carbon
2008 2009 2010 2011
10
20
30
MC-GARCH Brent GARCH Brent
2008 2009 2010 2011
25
50
MC-GARCH DAX GARCH DAX
16 / 27
Outline
1 The Energy Systemic Risk Measure: EnSysRISK
2 Econometric methodology and application
Causility in means and variances
Factor model and tail expectations
3 EnSysRISK and the impact on the economy
17 / 27
Factor model: Dynamic PCA
Dynamic PCA based on the daily correlation matrices estimated with the
Dynamic Conditional Correlation (DCC) model
Ht = DtRtDt = Dt�AtΛtA
�t +Rζt
�Dt
where Dt = diag(σ1t , ...,σnt), At is a matrix of s eigenvectors associated with the s largesteigenvalues that are contained in the diagonal matrix Λt = diag(λ1t ,λ2t , ...,λst) withλ1t ≥ λ2t ≥ ...≥ λst , s ≤ n and Rζt is the correlation matrix of idiosyncratic terms ζt .
Standardized residuals uit = (rit −µit)/σit becomes a function of the first s
dynamic principal components and idiosyncratic terms
uit =s
∑j=1
aijtyjt +ζit
where aijt is the element of the eigenvector associated with asset i and principal
component yjt extracted from Rt , and ζit = uit −∑sj=1 aijtyjt .
18 / 27
Restrictions on the dynamic PCAThe energy crisis condition is defined by 2 factors:
Et−1 (uit |energy crisis) := Et−1 (uit |rEnMt > C , rMt < 0)
the return on the non-energy sector: rMt � yMt = y1t = a�1tut
the energy market return: rEnMt � yEnMt = y2t = a�2tut
Restricted PCA (Ng et al. (1992)): the 1st component is restricted to be the
non-energy return (DAX industrial index)
maxa1t a�1tRta1t
s.t. a�1ta1t = 1, ai1t = 0 ∀t,∀i �= DAX industrial
The other dynamic components are mutually orthogonal and orthogonal to the
industrial component
maxajt a�jtRtajt
s.t. a�jtajt = 1, a
�jtalt = 0 ∀t,∀l �= j
19 / 27
Energy Market Integration
The contribution of the energy trend component (λEnM,t/∑nj=1 λj ,t) as a
dynamic measure of energy market integration (Billio et al. (2010),
Kritzman et al. (2011))
2009 2010 2011
40.0
42.5
45.0
47.5
50.0
52.5
55.0
57.5
60.0
LB b
ankr
uptc
y
Fuku
shim
a pl
ant o
utag
e
BP’
s rig
exp
losi
on
Rus
sia-
Ukr
aine
NG
dis
pute
Hig
hest
cru
de o
il pr
ice
Energy trend component contribution (%)
Decreasing crude oil prices
20 / 27
Energy Systematic Risk
The correlation with the energy trend component (λEnMta2i ,EnMt) as a
measure of energy systematic risk
2009 2010 2011
0.6
0.7
0.8 Cor(Electricity, Energy Market)
2009 2010 2011
0.6
0.7
0.8 Cor(Natural Gas, Energy Market)
2009 2010 20110.7
0.8
0.9 Cor(Coal, Energy Market)
2009 2010 2011
0.4
0.6
0.8 Cor(Carbon, Energy Market)
2009 2010 2011
0.2
0.4
0.6
Cor(Brent, Energy Market)
21 / 27
Tail expectations
The non-energy market return: rMt � yMt = y1t = a�1tut
The energy market return: rEnMt � yEnMt = y2t = a�2tut
The tail expectation Et−1 (uit |energy crisis) is approximated by
s
∑j=1
[aijtEt−1 (yjt |yEnMt > C , yMt < 0)]+Et−1 (ζit |yEnMt > C , yMt < 0)
A nonparametric estimator of tail expectations is
E(yjt |yEnMt > C , yMt < 0) =∑T
τ=1 yjτΦ
��yEnMτ√
λEnMτ− C√
λEnMt
�h−1
�I (yMτ < 0)
∑Tτ=1 Φ
��yEnMτ√
λEnMτ− C√
λEnMt
�h−1
�I (yMτ < 0)
where Φ(·) is the Gaussian cummulative distribution function. The same
estimation procedure applies to E(ζit |yEnMt > C , yMt < 0).
22 / 27
The conditional MES of energy assets
MESit(C ) = µit +σitEt−1
�s
∑j=1
aijtyjt +ζit |yEnMt > C , yMt < 0
�
2009 2010 2011
2.5
5.0
7.5MES Electricity
2009 2010 2011
2.5
5.0
MES Natural Gas
2009 2010 2011
2.5
5.0
7.5
MES Coal
2009 2010 2011
2.5
5.0
7.5 MES Carbon
2009 2010 2011
0.0
2.5
5.0 MES Brent
23 / 27
Outline
1 The Energy Systemic Risk Measure: EnSysRISK
2 Econometric methodology and application
Causility in means and variances
Factor model and tail expectations
3 EnSysRISK and the impact on the economy
24 / 27
EnSysRISKEnSysRISK = the total cost in million euros of each energy commodity class to
the German non-energy sector during a potential energy crisis
2009 2010 2011
75
100
125
150 EnSysRISK Electricity
2009 2010 2011
20
40
60
EnSysRISK Natural Gas
2009 2010 2011
2
4
6
EnSysRISK Coal
2009 2010 2011
10
15
20
25 EnSysRISK Carbon
2009 2010 2011
50
100
150EnSysRISK Brent
25 / 27
’Net’ impact on the economyMESMt(C ) = µMt +σMtEt−1
�∑s
j=1 aMjtyjt +ζMt |yEnMt > C , yMt < 0�
’Net’ impact of the energy crisis:
∆MESMt(C ) = MESMt(C )−MESMt(VaR(rEnMt)0.5)
MES DAX .95 MES DAX .5
2009 2010 2011
-4
-2
0MES DAX .95 MES DAX .5
DeltaMES DAX
2009 2010 2011
-0.75
-0.50
-0.25
0.00 DeltaMES DAX
26 / 27
Summary
Energy Systemic Risk: the risk of an energy crisis raising the prices of all energy
commodities with negative consequences for the real economy
EnSysRISK: the total cost of an energy commodity to the rest of the economy
during an energy crisis
EnSysRISK increases with
high MES
high prices
high dependence of the economy on the energy source
The MES captures co-movements in energy assets
Causal relationships in means and variances reflect possible spillover effects
from one product to another
Tail exposure to common factors: the MES is conditional on extreme energy
market shocks from the supply side (restricted dynamic PCA)
Contact: diane.pierret@uclouvain.be
27 / 27
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