A structural model for electricity forward prices Florentina Paraschiv, University of St. Gallen, ior/cf with Fred Espen Benth, University of Oslo

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1 1 I J E J K J A B H F A H = J E I 4 A I A = H? D + F K J = J E =. E =? A A structural model for electricity forward prices Florentina Paraschiv, University of St. Gallen, ior/cf with Fred Espen Benth, University of Oslo ECOMFIN216, Paris

2 Outlook Structural models for forward electricity prices are highly relevant: major structural changes in the market due to the infeed from renewable energy Renewable energies impact the market price expectation impact on futures (forward) prices? We will refer to a panel of daily price forward curves derived over time: cross-section analysis with respect to the time dimension and the maturity space Examine and model the dynamics of risk premia, the volatility term structure, spatial correlations Motivation p.2

3 Literature review Models for forward prices in commodity/energy: Specify one model for the spot price and from this derive for forwards: Lucia and Schwartz (22); Cartea and Figueroa (25); Benth, Kallsen, and Mayer-Brandis (27); Heath-Jarrow-Morton approach price forward prices directly, by multifactor models: Roncoroni, Guiotto (2); Benth and Koekebakker (28); Kiesel, Schindlmayr, and Boerger (29); Critical view of Koekebakker and Ollmar (25), Frestad (28) Few common factors cannot explain the substantial amount of variation in forward prices Non-Gaussian noise Random-field models for forward prices: Roncoroni, Guiotto (2); Andresen, Koekebakker, and Westgaard (21); Derivation of seasonality shapes and price forward curves for electricity: Fleten and Lemming (23); Bloechlinger (28). Motivation p.3

4 Problem statement Previous models model forward prices evolving over time (time-series) along the time at maturity T: Andresen, Koekebakker, and Westgaard (21) Let F t (T ) denote the forward price at time t for delivery of a commodity at time T t Random field in t: Random field in both t and T : t F t (T ), t (1) (t, T ) F t (T ), t, t T (2) Get rid of the second condition: Musiela parametrization x = T t, x. F t (t + x) = F t (T ), t (3) Let G t (x) be the forward price for a contract with time to maturity x. Note that: G t (x) = F t (t + x) (4) Modeling assumptions p.4

5 Graphical interpretation T x= T-t ( tt, ) F ( T), t T t t G ( X), t t t Modeling assumptions p.5

6 Influence of the time to maturity Change due to decreasing time to maturity( x) Change in the market expectation( t) t 1 t 2 Modeling assumptions p.6

7 Model formulation: Heath-Jarrow-Morton (HJM) The stochastic process t G t (x), t is the solution to: dg t (x) = ( x G t (x) + β(t, x)) dt + dw t (x) (5) Space of curves are endowed with a Hilbert space structure H x differential operator with respect to time to maturity β spatio-temporal random field describing the market price of risk W Spatio-temporal random field describing the randomly evolving residuals Discrete structure: G t (x) = f t (x) + s t (x), (6) s t (x) deterministic seasonality function R 2 + (t, x) s t (x) R Modeling assumptions p.7

8 Model formulation (cont) We furthermore assume that the deasonalized forward price curve, denoted by f t (x), has the dynamics: df t (x) = ( x f t (x) + θ(x)f t (x)) dt + dw t (x), (7) with θ R being a constant. With this definition, we note that df t (x) = df t (x) + ds t (x) = ( x f t (x) + θ(x)f t (x)) dt + t s t (x) dt + dw t (x) = ( x F t (x) + ( t s t (x) x s t (x)) + θ(x)(f t (x) s t (x))) dt + dw t (x). In the natural case, t s t (x) = x s t (x), and therefore we see that F t (x) satisfy (5) with β(t, x) := θ(x)f t (x). The market price of risk is proportional to the deseasonalized forward prices. Modeling assumptions p.8

9 Model formulation (cont) We discretize the dynamics in Eq. (7) by an Euler discretization df t (x) = ( x f t (x) + θ(x)f t (x)) dt + dw t (x) x f t (x) f t(x + x) f t (x) x f t+ t (x) = (f t (x) + t x (f t(x + x) f t (x)) + θ(x)f t (x) t + ǫ t (x) (8) with x {x 1,..., x N } and t = t,..., (M 1) t, where ǫ t (x) := W t+ t (x) W t (x). which implies Z t (x) := f t+ t (x) f t (x) t x (f t(x + x) f t (x)) (9) Z t (x) = θ(x)f t (x) t + ǫ t (x), (1) ǫ t (x) = σ(x) ǫ t (x) (11) Modeling assumptions p.9

10 Overview of modeling procedure Theoretical model: Spatio-temporal random field of forward prices Is it realistic? We validate assumptions Empirical analysis: Fit the model to PFCs Examine statistics of: ü Risk premia ü Distribution of noise ü Volatility term structure ü Spatial correlations Refine the model: Volatility term structure Model coloured noise Spatial correlations Fine Tuning Overview of the modeling approach p.1

11 Risk premia Short-term: it oscillates around zero and has higher volatility (similar in Pietz (29), Paraschiv et al. (215)) Long-term: is becomes negative and has more constant volatility (Burger et al. (27)): In the long-run power generators accept lower futures prices, as they need to make sure that their investment costs are covered Magnitude of the risk premia Point on the forward curve (2 year length, daily resolution) Empirical results p.11

12 Term structure volatility We observe Samuelson effect: overall higher volatility for shorter time to maturity Volatility bumps (front month; second/third quarters) explained by increased volume of trades Jigsaw pattern: weekend effect; volatility smaller in the weekend versus working days volatility (EUR) Maturity points Empirical results p.12

13 Explaining volatility bumps 18, 16, 14, 12, 1, 8, 6, 4, 2, 1/29 4/29 7/29 1/29 1/21 4/21 7/21 1/21 1/211 4/211 7/211 1/211 1/212 4/212 7/212 1/212 1/213 4/213 7/213 1/213 1/214 4/214 7/214 1/214 Total volume of trades Front Month 2nd Month 3rd Month 5th Month Figure 2: The sum of traded contracts for the monthly futures, evidence from EPEX, own calculations (source of data ems.eex.com). Empirical results p.13

14 Explaining volatility bumps 9, 8, 7, 6, 5, 4, 3, 2, 1, 1/29 4/29 7/29 1/29 1/21 4/21 7/21 1/21 1/211 4/211 7/211 1/211 1/212 4/212 7/212 1/212 1/213 4/213 7/213 1/213 1/214 4/214 7/214 1/214 Total volume of trades Front Quarter 2nd Quarterly Future (QF) 3rd QF 4th QF 5th QF Figure 3: The sum of traded contracts for the quarterly futures, evidence from EPEX, own calculations (source of data ems.eex.com). Empirical results p.14

15 Statistical properties of the noise We examined the statistical properties of the noise time-series ǫ t (x) ǫ t (x) = σ(x) ǫ t (x) (12) We found: Overall we conclude that the model residuals are coloured noise, with heavy tails (leptokurtic distribution) and with a tendency for conditional volatility. ǫ t (x k ) Stationarity Autocorrelation ǫ t (x k ) Autocorrelation ǫ t (x k ) 2 ARCH/GARCH h h1 h1 h2 Q Q1 Q Q3 1 1 Q4 1 Q Q Q Table 1: The time series are selected by quarterly increments (9 days) along the maturity points on one noise curve. Hypotheses tests results, case study 1: x = 1day. In column stationarity, if h = we fail to reject the null that series are stationary. For autocorrelation h1 = indicates that there is not enough evidence to suggest that noise time series are autocorrelated. In the last column h2 = 1 indicates that there are significant ARCH effects in the noise time-series. Empirical results p.15

16 Autocorrelation structure of noise time series (squared) 1 ACF Q 1 ACF Q1 Sample Autocorrelation.5 Sample Autocorrelation Lag Lag 1 ACF Q2 1 ACF Q3 Sample Autocorrelation.5 Sample Autocorrelation Lag Lag Figure 4: Autocorrelation function in the squared time series of the noise ǫ t (x k ) 2, by taking k {1, 9, 18, 27}, case study 1: x = 1day. Empirical results p.16

17 Normal Inverse Gaussian (NIG) distribution for coloured noise.8.6 Normal density Kernel (empirical) density NIG with Moment Estim. NIG with ML Normal density Kernel (empirical) density NIG with Moment Estim. NIG with ML pdf.4 pdf epsilon t(1) epsilon t(9) Normal density Kernel (empirical) density NIG with Moment Estim. NIG with ML Normal density Kernel (empirical) density NIG with Moment Estim. NIG with ML pdf 1 pdf epsilon t(18) epsilon t(27) Empirical results p.17

18 Spatial dependence structure Figure 5: Correlation matrix with respect to different maturity points along one curve. Empirical results p.18

19 Revisiting the model We have analysed empirically the noise residual dw t (x) expressed as ǫ t (x) = σ(x) ǫ t (x) in a discrete form Recover an infinite dimensional model for W t (x) based on our findings W t = t Σ s dl s, (13) where s Σ s is an L(U, H)-valued predictable process and L is a U-valued Lévy process with zero mean and finite variance. As a first case, we can choose Σ s Ψ time-independent: W t+ t W t Ψ(L t+ t L t ) (14) Choose now U = L 2 (R), the space of square integrable functions on the real line equipped with the Lebesgue measure, and assume Ψ is an integral operator on L 2 (R) R + x Ψ(g)(x) = R σ(x, y)g(y) dy (15) we can further make the approximation Ψ(g)(x) σ(x, x)g(x), which gives W t+ t (x) W t (x) σ(x, x)(l t+ t (x) L t (x)). (16) Fine tuning p.19

20 Revisiting the model (cont) Recall the spatial correlation structure of ǫ t (x). This provides the empirical foundation for defining a covariance functional Q associated with the Lévy process L. In general, we know that for any g, h L 2 (R), E[(L t, g) 2 (L t, h) 2 ] = (Qg, h) 2 where (, ) 2 denotes the inner product in L 2 (R) Qg(x) = R q(x, y)g(y) dy, (17) If we assume g L 2 (R) to be close to δ x, the Dirac δ-function, and likewise, h L 2 (R) being close to δ y, (x, y) R 2, we find approximately E[L t (x)l t (y)] = q(x, y) A simple choice resembling to some degree the fast decaying property is q( x y ) = exp( γ x y ) for a constant γ >. It follows that t (L t, g) 2 is a NIG Lévy process on the real line. Fine tuning p.2

21 Conclusion We developed a spatio-temporal dynamical arbitrage free model for electricity forward prices based on the Heath-Jarrow-Morton (HJM) approach under Musiela parametrization We examined a unique data set of price forward curves derived each day in the market between We examined the spatio-temporal structure of our data set Risk premia: higher volatility short-term, oscillating around zero; constant volatility on the long-term, turning into negative Term structure volatility: Samuelson effect, volatility bumps explained by increased volume of trades Coloured (leptokurtic) noise with evidence of conditional volatility Spatial correlations structure: decaying fast for short-term maturities; constant (white noise) afterwards with a bump around 1 year After explaining the Samuelson effect in the volatility term structure, the residuals are modeled by an infinite dimensional NIG Lévy process, which allows for a natural formulation of a covariance functional. Conclusion p.21