Background: Liberalised electricity markets  (Wholesale) markets established

1. Rational Expectations in Electricity Futures Markets?Empirical Insights from the Interaction between EEX Spot and Futures Prices Christian Redl Energy Economics Group, Vienna University of Technology 34th IAEE International Conference, Stockholm, 20 June 2011

2. Background: Liberalised electricity markets  (Wholesale) markets established • Manifold risks for market participants  Forward/futures markets (delivery is deferred) help hedging price risks  High trading volumes! • Indication of mature and well-functioning market? • Questions of efficiency and determinants of forward/futures prices arise • Crucial to gain insight into price formation • Special characteristics of physical commodity electricity • Associated consequences for market structure • Importance for overall economy • Major research questions: • What are the links between current spot and futures prices? • Are there common drivers? • Which exogenous parameters effect components of electricity price system? • What are the implications for the performance of electricity futures market? (1) Introduction: Motivation

3. (1) Introduction: Economic theories on futures pricing • Theory of storagefor storable commodities (Kaldor, 1939): • Electricity is not storable  Keynes (1930) relates futures pricesF(t,T) to expected spot pricesEt(S(T)) and a forward premium FPt,T: • Et(S(T)) are (?) built on expectations of fundamental market conditions during delivery • Translated to forward prices by applying risk or forward premia (compensation for price risks) • Futures price market’s best estimate of future commodity price? • According to theory (and empirical studies): Clear cut distinction between exogenous (forwards) and endogenous (future spots) variables • My position: Links do emerge between current spots and forwards  Distinction not clear

4. (2) Research context and hypotheses • Links between current spots and forwards? Electricity is not storable... • Corresponding relationship may suggest behavioural pricing? • Power price affected by production costs, demand, market power (Bunn, 2004, Weron, 2006) • Inputsto production (gas, coal and CO2 permits) storable • Linksbetween electricity spots and forwards emerge from derived nature of power • Relations between electricity spots and forwards may not only emerge from links in fuels • Behavioural biases (e.g. employing heuristics, anchoring) reasonably to be expected in electricity markets • Spot price forecasts for extended delivery period prove to be elusive • Given an adaptive (behavioural) adjustment link between current short and long term prices appears not surprising Correlations: (working day prices between Sep. ‘03 to Dec. ‘09)

5. (2) Research context and hypotheses • Links between electricity spots and forwards emerge from derived nature of power • Links from behavioural biases(adaptive adjustment) • Aim to give insights on the relevance of these distinct sources of links... • Analysis will be performed for biggest European power exchange: European Energy Exchange (EEX, Leipzig, Germany) • Granger causality tests and VAR regression models (ARCH not presented) used for analysis

6. Price levels: (3)Data Source: EEX

7. Price returns: (3)Data

8. Potential links  information flows (causal relation)? • Granger causality tests: Spot appears weakly exogenous Interaction between spot and futures prices • Vector Autoregressive (VAR) model for returns: where is a vector of spot and (month-, quarter and year ahead) futures price returns and xt is a vector of exogenous variables (fuel prices, supply (wind) and demand data). Lagged endogenous variables Exogenous variables

9. Vector Autoregressive (VAR) model for returns: Results (4)Interaction between spot and futures prices: VAR results Endogenous interaction effects Electricity Gas CO2 Effects of exogenous drivers (behaviour!) Coal Supply/Demand

10. Exogenous supply, demand and fuel variablesdrive electricity price system • Still, significant effects of electricity prices themselves reject exogeneityof electricity spot and futures prices • Results imply prevalence of behavioural components in electricity markets’ price formation (adaptive adjustment) • Ties in fuels complicate price formation in electricity • Behavioural component confirmed by basis regressions and ARCH models • Futures pricing compound function of rational and behavioural components • Spillover from spots to forwards • Market monitoring (e.g.: Spot market power; “Sticky” expectations influence WTP) • Former market power analyses typically focus on spots: Crucial role of excess supply and withholding on spot results confirmed. However, effects of spillovers not considered  Increased focus on futures markets indicated • Implications for predictive power of forwards and market efficiency • Risk assessment affected (interlinkages)? • Increasing costs of hedging spot price uncertainty • Drivers of bias? Empirical analysis of forward premium necessary • Analyses reveal unexpected transaction costs associated with power trading (5)Conclusions

11. (5) Conclusions and Outlook (Cont’d) • Market equilibrium linked to equilibrium in expectations • Behavioural adjustment applies for all market actors • Future research: Model different groups detailing psychological biases • Sheds light on specific positions taken • Allows testing expectations induced trend (herding) effects • Publications of US CFTC list long and short open interests of different traders • Similar market transparency programmes for EU electricity markets? • Decrease asymmetries and increase data base for new descriptive/theoretical models • Performed empirical analyses relied on aggregated data • Insights enlarged by inclusion of position data by trader categories • Robustness increased by assessing additional maturities and intra-daily prices and non- symmetric adjustments (ARCH-specifications: Results not presented here: Affects significance of some parameters) • Much of this would necessitate higher transparency levels… • Transparency initiatives indicated (Market data availability, short/long positions) • Empirical analyses suggest expanding equilibrium models by oligopolistic environments, behavioural and expanded risk aversion concepts and different information levels

12. Thank you for your attention! Forquestions / remarks etc. … Email: redl@eeg.tuwien.ac.at Tel.: +43-1-58801-37361