The Systemic Risk of Energy Markets Diane Pierret Université Catholique de Louvain & New York University 6th PhD Day, Louvain-la-Neuve 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 / 23 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 / 23 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 / 23 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 / 23 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 / 23 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) = ςi Et−1 (fincons τ − inv τ ) for τ ∈ [t + ν; t + 2ν −1] 6 / 23 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 / 23 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 + σit uit |rEnMt > C , rMt < 0) = µit + σit Et−1 (uit |rEnMt > C , rMt < 0) where µit and σ 2it 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 / 23 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 / 23 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 Natur al Ga s M ar ke t A r ea s 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 Tr a din g P ar t ic ip ant s 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 0 1/2010 2/2010 3/2010 4/2010 36 39 46 48 26 26 27 22 Source: EEX 10 / 23 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 / 23 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 = πi η�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 and qt 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’). 12 / 23 Multiplicative Causality GARCH model Multiplicative Causality GARCH model for the variance with a GARCH component and an interaction component εit = σit uit = � φit git uit where git = (1−αii −βi − γii 2 ) + αii � ε 2it−1 φit−1 � + βi git−1 + γii � ε 2it−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 (ϑij ujt−1 + αij |ujt−1|) + κ�i dt � where dt are deterministic terms including seasonal dummies. 13 / 23 Networks of Causal Relationships Causal relationships reflect physical relationships in the energy market (subsitution, merit-order) and spillover effects Mean Network Variance Network 14 / 23 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 15 / 23 Factor model: Dynamic PCA Dynamic PCA based on the daily correlation matrices estimated with the Dynamic Conditional Correlation (DCC) model Ht = Dt Rt Dt = Dt � At Λt A � t + Rζt � Dt where Dt = diag(σ1t , ..., σnt ), At is a matrix of s eigenvectors associated with the s largest eigenvalues that are contained in the diagonal matrix Λt = diag(λ1t , λ2t , ..., λst ) with λ1t ≥ 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 aijt yjt + 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 aijt yjt . 16 / 23 Restrictions on the dynamic PCA The 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�1t ut the energy market return: rEnMt � yEnMt = y2t = a�2t ut Restricted PCA (Ng et al. (1992)): the 1st component is restricted to be the non-energy return (DAX industrial index) maxa1t a � 1t Rt a1t s.t. a � 1t a1t = 1, ai1t = 0 ∀t,∀i �= DAX industrial The other dynamic components are mutually orthogonal and orthogonal to the industrial component maxajt a � jt Rt ajt s.t. a � jt ajt = 1, a � jt alt = 0 ∀t,∀l �= j 17 / 23 Tail expectations The non-energy market return: rMt � yMt = y1t = a�1t ut The energy market return: rEnMt � yEnMt = y2t = a�2t ut The tail expectation Et−1 (uit |energy crisis) is approximated by s ∑ j=1 [aijt Et−1 (yjt |yEnMt > C , yMt < 0)] + Et−1 (ζit |yEnMt > C , yMt < 0) A nonparametric estimator of tail expectations is Ê (yjt |yEnMt > C , yMt < 0) = ∑Tτ=1 yjτ Φ �� 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). 18 / 23 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 19 / 23 The conditional MES of energy assets MESit (C ) = µit + σit Et−1 � s ∑ j=1 aijt yjt + ζit |yEnMt > C , yMt < 0 � 2009 2010 2011 2.5 5.0 7.5 MES 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 20 / 23 EnSysRISK EnSysRISK = 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 50 100 150 EnSysRISK Brent 21 / 23 ’Net’ impact on the economy MESMt (C ) = µMt + σMt Et−1 � ∑sj=1 aMjt yjt + 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 0 MES DAX .95 MES DAX .5 DeltaMES DAX 2009 2010 2011 -0.75 -0.50 -0.25 0.00 DeltaMES DAX 22 / 23 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 23 / 23 The Energy Systemic Risk Measure: EnSysRISK Econometric methodology and application Causility in means and variances Factor model and tail expectations EnSysRISK and the impact on the economy