Can market power in the electricity spot market translate into market power in the hedge market? Toby Daglish and Gabriel Fiuza de Bragança New Zealand Institute for the Study of Competition and Regulation and Instituto de Pesquisa Econômica Aplicada Background Forward pricing in electricity markets is troublesome. - Electricity non-storability implies that usual commodity pricing literature (and arbitrage/ cost of carry arguments) may not hold. Electricity markets frequently present additional complications. - Oligopoly, uniform-price auction and vertical integration. Theoretical literature discusses how forward contracts affect spot market power. How does spot market power affect forward prices? The hybrid pricing approach Several papers try to mimic electricity price’s stochastic behaviour in order to value its derivatives. - Focus on seasonality and spikes (short-lived and abrupt oscillations). - Ex. Schwartz (1997), Schwartz and Smith (2000), Deng (2000), Lucia and Schwartz (2002) and Cartea and Villaplana (2005). Alternative: hybrid pricing approach. - Build on an equilibrium framework, explaining instantaneous price behaviour in terms of observable state variables (demand and supply). Keep track of fundamentals. - Assume state variables follow dynamic processes and apply no-arbitrage methodologies to calculate derivatives. Skantze, Gubina, and Ilic (2000), Barlow (2002), Pirrong and Jermakyan (2008), Cartea and Villaplana (2008) and Lyle and Elliott (2009). Equilibrium ground is too simple and based on a competitive spot market. Some definitions There are N firms (K generators and R retailers). Firms can participate in both markets (I=K+R-N gentailers). State variables: The consumers’ demand: Generator i’s cost function: Contracts: Generators/gentailers’ auction problem The conditional cumulative function of market clearing price is: Generator/Gentailer i’s maximization problem: Optimal supply schedule At any time, assume supply function is additively separable: Then, extending Hortaçsu & Puller (2008), we have the following supply schedule: Which is equivalent to: elasticity of net residual demand Equilibrium spot price If we further assume that there are K>2 generators/gentailers and that marginal costs and demand can be approximated by a linear function… …by the spot market clearing condition, we have: Forward pricing There are two state variables: an inelastic demand and a cost shifter, say the water inflows. Interest rate is constant (forward=future). Under these assumptions the spot price equation simplifies to the following: Where Forward pricing II Assume that the demand oscillates around a deterministic function of time (seasonality). Cost shifter oscillates around a long term mean. Then, by Lucia & Schwartz (2002) two factor model, we have: Results . . . Results II Market power and forward prices as a function of contract maturity (same parameters as previous figure with b adjusting for a fixed marginal cost). Results III Market power and forward prices as a function of contract maturity (same assumptions as previous figure except for QC = 10). Empirical Strategy Challenge: to find 2 good proxies for the state variables (demand and cost shifter). Test: See if the theoretical assumptions are supported by the data. The estimation procedure is divided in two steps: First, spot price model estimation. Second, forward price calibration (to find the market price of risk– lambdas). - Simulation: See how changes in concentration affect forward prices. Approximation Assume we have an economy where K=N, which means all retailers participate of the generation market. Assume also that only generators/gentailers transact in the forward market. Ex: New Zealand: Market shares in 2008 Source: Companies' annual reports 2008 and NZ Electricity Commission. Under these assumptions the spot price equation simplifies to the following: Notice that in this case the generators’ quantity contracted does not affect spot prices. But the number of generators still does. Price is equal to average marginal cost. Spot price model estimation Model discretized: The New Zealand electricity market is characterized by the dominance of hydro and gas power . Shadow prices are natural candidates for . In particular, Evans, Guthrie and Lu (2010), show that the shadow price of water is the same as the shadow price of gas under regular conditions. However, shadow prices are not observable. The challenge is to find the best proxy or proxies. Primary candidates? Unfortunately several combinations of variables such as hydro inflows, storage and thermal production fail to attain serially uncorrelated results. Solution: Since marginal costs are closely related to spot prices, we use the latest observed spot price (lagged spot price) as a proxy of marginal cost shifters for empirical purposes. Therefore: Haywards node spot price. Source: Electricity Commission. National offtake in GWh. Source: Electricity Commission. Observed(Lagged) spot price as proxy. Haywards forward price. Monthly and quarterly contracts. Source: www.energyhedge.co.nz (accessed in december/2010). We use daily frequency from 22/01/2004 to 30/11/2010. Consistent with forward price series available. Spot Price NZD/MWh (CPI adjusted) 500 400 300 200 100 0 2004 2005 2006 2007 2008 2009 2010 Demand (National offtake GWh) 130 120 110 100 90 80 70 2004 2005 2006 2007 F(t) 2008 Demand 2009 2010 Results Estimation method: Seemingly Unrelated Regression (SUR) - Objective: To estimate expected spot price conditioned on state variables. No endogeneity. System of Equations Equation: Estimation Method: Seemingly Unrelated Regression Observations: 2505 R-squared Adjusted r-squared S.E. of regression Durbin Watson stat Sample: 22/01/2004 30/11/2010 Included observations: 2505 0.76 0.76 28.04 2.61 Mean dependent var S.D. dependent var Sum squared resid 72.66 56.90 1,967,258 103.12 Total system (balanced) observations: 7515 Coefficient Std. Error t-Statistic Prob. Equation: -14.83 1.75 -8.46 0.00 R-squared 0.62 Mean dependent var 0.76 0.08 9.29 0.00 Adjusted r-squared 0.62 S.D. dependent var 0.99 0.003 368.79 0.00 S.E. of regression 5.84 Sum squared resid 102.97 0.29 358.65 0.00 Durbin Watson stat 1.55 -8.23 0.39 -21.15 0.00 4.96 0.01 674.03 0.00 148.12 2.89 5.59 206.28 26.50 0.01 0.00 0.99 Determinant Residual covariance 3.15E+16 9.44 85,173 Equation: R-squared Adjusted r-squared S.E. of regression Durbin Watson stat 0.75 0.75 28.29 2.62 Mean dependent var S.D. dependent var Sum squared resid 72.66 56.90 2,004,258 Forward Pricing Calibration Theoretical forward pricing formula: Integrated formula/ Calibration: Calculating the lambdas that minimize the non linear least squares for the 1651 forward prices of our sample, we have: Results Conclusion Hybrid pricing models offer a promising framework to relate equilibrium fundamentals to derivative pricing. Our model shows that, in a case where contracts are not significant in influencing clearing spot prices, the spot market power may still affect the whole forward curve. If market power affect forward prices, it may affect the optimal quantity contracted. Unlike the assumptions of most of the IO theoretical literature on the subject, neither forward contracts or forward prices are exogenous. Its is important to fully understand its determinants to evaluate its relationship with market power.