Determinants of forward premia in electricity markets:

A taxonomic empirical analysis

Christian Redl1,a, Derek W. Bunnb aEnergy Economics Group, Vienna University of Technology

bEnergy Markets Group, London Business School

Abstract

A taxonomy of electricity forward premia determinants is introduced. Preliminary empirical

models give insights into the corresponding propositions on the forward premium. The risk

attitude of participants in the electricity market is strongly influenced by the agents’

assessment of energy commodities, which serve as fuel input or are of sentimental importance

for energy markets in general. Market participants react sensitively on volatility in the

electricity market itself and, additionally, on a tightening excess supply in the spot market

during the trading period of forward contracts. Furthermore, statistically significant demand

shock proxies underpin adaptive expectations of the actors in the forward market. Finally,

spot price mark-ups contribute to increased forward premia.

JEL classification

Q40; C10; G13

Keywords

Forward markets, predictive power, risk premium

1 Corresponding author. Energy Economics Group, Vienna University of Technology Gusshausstrasse 25-29/373-2, 1040 Vienna, Austria; E-mail: redl@eeg.tuwien.ac.at; Tel.: +43 1 58801 37361; Fax: +43 1 58801 37397

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1. Introduction

The goal of various worldwide liberalisation efforts of the electricity supply industry was the

introduction of competition as precondition for an efficient energy supply. Wholesale markets

were established and the prices are a result of the market forces.

In this new institutional environment risks emerged for market participants unknown

in the previous regulated area. Still, exchange traded futures or OTC traded forward contracts

allow for a management of the price risk by locking in a fixed price. Therefore uncertain

future spot prices can be avoided. In fact, electricity spot prices are characterised by high

volatility and occasional spikes (for a detailed analysis see e.g. Lucia and Schwartz (2002),

Burger et al. (2004), Huisman et al. (2007), Kanamura and Ohashi (2008), Karakatsani and

Bunn (2008), Bowden and Payne (2008), Higgs and Worthington (2008)). This is caused by

convex supply curves and a – in the short term – price inelastic demand. Supply or demand

shocks lead, in turn, to sudden rises in spot market prices. Moreover, as electricity cannot be

stored economically in suitable quantities, dampening effects of stocks are lacking. Hence,

these characteristics give rise to an increased demand for contracts.

The economic theory provides two alternative approaches for pricing these forward

contracts. The most common one is the theory of storage (Kaldor, 1939) which, however,

cannot straightforward be applied to electricity forwards since electricity cannot be stored in

economically suitable quantities. Instead, a second approach in economic theory considers

equilibrium relationships for forward pricing (Keynes, 1930): The (current) forward price can

be split up into a forecast of the future spot price at delivery and a risk premium. Hence, the

ex post forward premium Ft,T-ST is the key variable assessed in the (empirical) literature:

, , , , (1)

Equation (1) shows that the ex post forward premium equals the ex ante premium plus a

random error of the (rational) spot price forecast due to supply and demand shocks.

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We argue that it is this difference which creates the importance of forward markets for

market actors and policy makers alike. As mentioned, the market participants are faced with a

forecasting problem and, depending on the spot price distribution and the attitude towards

risk, either demand a compensation for contracting, are willing to pay a corresponding

premium or to accept a discount to eliminate the risk of uncertain future cash flows. This

brings about important policy implications since it is the forward market, due to this economic

reasoning, which determines investments and welfare. Hence, it is necessary to understand the

components of the risk premium which warrants the attention of policy makers. In turn,

understanding the drivers of the forward premium allows a better regulation of electricity

markets and design of corresponding market rules. Nevertheless, due to the strong

interrelation between current events on the forward and spot markets (and current spot and

forward prices), the short term markets have to be integrated in the analysis of long term

markets.

Our analysis will focus on month-ahead futures – for several reasons: First, most price

data is available for futures with monthly delivery periods. Second, due to the shorter and

subsequent delivery period, forecast errors – in this analysis modelled via supply and demand

shocks – are expected to be lowest in the case of month-ahead futures. More specifically,

prices on the last trading day are considered since monthly averaging of futures prices yields

autocorrelation in the residuals. Finally, considering the full history of prices of a specific

contract, results may not be robust due to the increased time to delivery – and lack of trading.

Specifically, this analysis aims to unravel the components of the ex post forward

premium in a comprehensive taxonomic manner with the help of a structural empirical

modelling approach, whereas the literature on forward premia modelling typically focuses on

risk aversion measured by higher central moments (up to the third) and shocks in

inframarginal generation (typically hydro power) and demand. In our analysis econometric

models are used to assess forward pricing at the biggest regional European power market: the

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Western European Power market with its leading power exchange, the European Energy

Exchange (EEX) based in Leipzig, Germany.

The article proceeds as follows: The next section summarises related literature in the field

of forward markets and compares our approach with existing literature. Section 3 introduces

the market setting. In section 4 the realised forward premia are quantified. Section 5 gives

propositions on the forward premia determinants and section 6 presents econometric models

of the baseload and peak load premia at the EEX. Finally, section 7 concludes.

2. Literature overview

Following Keynes (1930), futures prices are related to expected spot prices. This forward

pricing theory has extended to a broad stream of empirical literature, the most relevant for our

analysis is summarised below.

Findings on (ex post) forward premia

Gjolberg and Johnsen (2001) and Botterud et al. (2009) identify positive forecast errors

respectively positive risk premiums in the Nordic market. Gjolberg and Johnsen (2001) argue

that due to the identified size, differences cannot be explained by risk premiums only but

would indicate informational inefficiencies or the exercise of market power because of the

high concentration of suppliers. Weron (2008) determines the market price of risk in the Nord

Pool futures market using stochastic models. He finds increasing risk premiums with

decreasing time to maturity – this is equivalent to decreasing forward premia over time. Bunn

(2006) identifies positive risk premiums for peak hours when comparing the UK day ahead

and prompt market and the week ahead and day ahead market. He argues, that during peak

hours the demand side has a higher willingness to pay day ahead in order to avoid high

volatility in the intra-day market. Similarly, Longstaff and Wang (2002), Hadsell and Shawky

(2006), Diko et al. (2006), Lucia and Torro (2008), Douglas and Popova (2008), Redl et al.

(2009), Daskalakis and Markellos (2009), and Furio and Meneu (2010) find significant risk

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premiums in long-term electricity markets. Bessembinder and Lemmon (2002) present an

equilibrium model where the forward premium (i.e. the difference between forward and

expected spot prices) is a function of the variance and skewness of spot prices. In turn, these

moments of the spot price distribution serve as risk assessment of market participants.

Douglas and Popova (2008) confirm the theoretical result of Bessembinder and Lemmon

(2002) for the PJM market. Moreover, they propose an augmented model including, among

others, gas storage inventories. Similarly, Lucia and Torro (2008), Redl et al. (2009) and

Furio and Meneu (2010) also confirm (at least partly) the results of Bessembinder and

Lemmon (2002). Additionally, supply and demand shock parameters are included in these

analyses to allow for a better capturing of risk versus forecast forward premium components.

In conclusion, the existing literature on empirical forward premia modelling typically

focuses on risk aversion (measured by higher central moments (up to the third) of the spot

price distribution) and shocks in inframarginal generation (typically hydro power) and

demand. However, our analysis aims to unravel the structural components of the ex post

forward premium in a comprehensive taxonomic manner with the help of an empirical

modelling approach. Therefore, in our evaluation, besides conventional risk assessment

measures and the representation of supply and demand shocks, additional variables are

introduced, which shall capture further sources of the futures-spot price bias. Before we

discuss these propositions on the forward premium components in detail, we will, however,

shortly examine the relevant market setting.

3. Market setting and corresponding price data

The European electricity market is still characterised by several price areas. Reasons for this

price divergence can be found, among others, in limited cross-border transmission capacities

(EC, 2005). However, quite a few regional electricity markets have emerged within the

European Union as some countries are not separated by cross-border transmission capacity

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bottlenecks. One of these is the Central/Western European market comprising Austria,

Germany, France and, to a certain extent, Switzerland forming the biggest market in

Continental Europe. The EEX is the leading exchange in this sub market. In early 2007

implicit auctions between France, Belgium and the Netherlands have been introduced leading

to a coupling of these markets thereby effectively removing the market separation in North

Western Europe and extending the Central European market. Finally, wholesale prices in the

Czech Republic as well as Poland have reached the EEX level. Figure 1 depicts these price

developments and an increasing convergence over time.

Figure 1. Wholesale electricity prices for the considered regional electricity market. Source: Various power exchanges

Fig. 2 shows the price evolution of monthly averages of spot peak load prices as well as

month-ahead peak load prices, noted on the last trading day for delivery during the plotted

month, at the EEX from October 2003 to January 20102. Spot and forward prices were rising

continuously until early 2006. Since fossil fuelled power plants constitute the price setting

technologies in the EEX market, increasing power prices reflected rising primary energy

prices. The highest increases could be observed during 2005 due to the commencement of the

2 In figure 2 the depicted forward price at, e.g. October 2003, was the settlement price of the month-ahead peak load futures on 30 September 2003 for a delivery during peak hours in October 2003.

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European Emission Trading Scheme (EU-ETS). Spot and month-ahead prices were falling,

with a short exception, from March 2006 onwards mainly due to a massive drop-off in

emission allowance prices. In 2008, EEX spot and month-ahead prices again started rising due

to a price jump on the spot market for CO2 allowances when the second EU-ETS period

started in January 2008. Prices have been falling since the fourth quarter of 2008 due to

substantial price decreases in the oil and gas and, correspondingly, the CO2 markets.

Figure 2. Evolution of monthly averages of peak load spot prices (red) and peak load month-ahead futures prices on the last trading day (blue) at the EEX from October 2003 to January 2010. Source: EEX

Figure 3 presents descriptive statistics for daily EEX base and peak load spot prices from

October 2003 to January 2010. The price series are non-normal, positively skewed and show a

high kurtosis. Moreover, the peak load price series is more volatile than baseload series.

Figure 3. Descriptive statistics of daily base (left) and peak load spot prices (right) at the EEX from October 2003 to January 2010.

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It is this distribution which faces the market participants with a forecasting problem of future

spot prices. Moreover, risk-averse agents have an incentive to reduce their risk exposure by

trading on the forward market. As will be shown in the following section, the willingness to

pay in order to reduce this risk exposure is significant.

4. Realised forward premia

First, for each monthly contract the relative ex-post difference between the forward price in

the trading period and spot price in the delivery period is determined:

∆ , (2)

where ΔT is the relative difference between the forward and spot price, FT-1,T is either the

average futures price in month T-1 for delivery in T or the settlement price on the last trading

day in month T-1 for delivery in T and ST is the spot price average in month T.

The differences between forward and corresponding spot prices are significant (see

figure 4). Table 1 summarises some additional statistics. On a monthly average, base load

contracts were traded 9% above actual spot prices in the delivery periods of the futures at

EEX. Month-ahead peak load futures were traded even 12% above spot prices in the delivery

period. The identified differences are significantly different from zero for a double-sided test.

Moreover, errors for base load and peak load are significantly larger than zero. If one looks at

each contract separately, the absolute value of the relative difference ΔT for peak load is

greater than for base load for almost every contract. Due to a higher slope of the supply curve

in peak load (unforeseen) variations in supply and demand induce greater price differences

between forward and spot prices in peak load which is confirmed by the results in Table 1.

Using futures prices on the last trading day instead of monthly averages for determination of

the relative differences ΔT still yields significant positive errors although the magnitude is

lower. On the last trading day, base load contracts were traded 5% above actual spot prices in

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the delivery periods. Peak load futures were traded on the last trading day 7% above spot

prices in the delivery period (see Table 1).

Figure 4. Relative differences of month-ahead peak load futures prices (noted on the last trading day) with respect to the actual spot price during the delivery period at the EEX. Source: EEX, own calculations

Table 1. Summary statistics of forecast errors for monthly averages and for prices on the last trading day of month-ahead futures with delivery from October 2003 to January 2019 traded at EEX.

The above analysis does not consider seasonalities in the (relative) forward premium. Figure 5

shows a seasonal graph of the relative differences for peak load. Without further elaborating

this matter at this point, especially with respect to statistical inference, we note, from visual

inspection, that there is evidence of a seasonal pattern in the forward premium being highest

in January and lowest in the mid seasons April and September.

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Mean 9% 5% 12% 7%

Standard dev. 21% 15% 26% 20%

Minimum -38% -38% -50% -50%

Maximum 87% 65% 98% 72%

t-statistic 3.66* 2.96* 4.04* 3.16*

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Figure 5. Seasonal graph of realised monthly percentage forward premia.

In the following section a categorisation of forward premia determinants is proposed which

shall mirror the risk and market assessment of the market participants and, moreover,

comprehensively describe the structural supply and demand characteristics and its effects on

the market outcome. Within each category several explanatory variables are described which

give further insights on the propositions on the electricity forward premium.

5. Propositions on electricity forward premia and a corresponding taxonomy of

structural components

Our aim is to extend established concepts of forward premia in electricity markets. We have

organised these components into a taxonomy of fundamental influences, behavioural effects,

market conduct, dynamic effects and shock effects. Specifically we provide insights on the

following propositions:

Fundamental influences

Fuels and their risk premia: Proposition: An increase in the gas forward premium is

expected to increase the electricity forward premium, whereas the effect is anticipated

to be more pronounced in peak load compared to base load.

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Given the high importance of fossil fuelled generation technologies in the EEX

market, the risk premium prevailing in the electricity contract market is directly

influenced by the risk premium in the gas market. For our analysis, this influence can

be motivated by either risk management considerations, since – assuming gas fired

price setting technologies – the realised spark spread constitutes the risk exposure of a

generator having contracted gas, or forecast errors of the respective market participant.

An empirical comparison of these categories, still, is challenging. The oil market,

although oil fired power plants are rarely dispatched, given its “sentimental”

importance is also to be included in this analysis (see below discussion on market

sentiments and behavioural issues).

Electricity system fundamentals: Proposition: A positive relationship between scarcity

and the forward premium is expected.

The definition of the forward premium according to equation (1) assumes that market

actors can correctly anticipate the general fundamental drivers and deviations between

forward and spot prices occur due to risk assessments. Moreover, any deviations of the

fundamentals from the expected ones’ are the result of shocks (see below). However, a

sluggish reaction of the market to fundamentals (specifically the margin) is

hypothesised in this analysis and corresponding variables are defined (supply/demand

ratios). We now turn to the hypothesis of the market participants’ expectation

formation in detail.

Behavioural effects

Proposition: We postulate pronounced adaptive expectation formation with respect to the

risk assessments of the market participants. Among others, this is motivated by high

correlations between current spot and forward electricity prices. Furthermore, we argue that

spot price forecasts for a delivery period comprising one month ahead prove to be elusive (for

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research aiming to model expectation behaviour and market participants alike). In this sense,

in our model the realisation of the relevant assessment parameters in the spot market of the

same delivery month a year ago as well as the realisation in the trading month of the forward

contract are used as proxies for the anticipated spot distribution realisations in the delivery

month.

Higher moments: Proposition: Central moments of the spot price distribution beyond

variance and skewness are of importance for the risk assessment of market actors.

Specifically, the kurtosis of spot prices is introduced additionally to capture the effect

of rare extreme deviations from the mean on the forward premium. Generally, a

positive influence of the kurtosis of spot prices on the premium is expected (given

positively skewed spot prices).

Spikes: Proposition: The forward premium increases due to the occurrence of spikes

in the spot market.

To test the reaction of the market to price spikes occurring in the spot market during

the trading period of the futures contracts above an average measure as the kurtosis

does, dummy variables which account for the occurrence of spikes are created.

Different degrees of spikiness respectively thresholds are defined (mean plus one, two

and three standard deviations) (Weron, 2006). The relevant spot price aggregation

level for estimating spikes at the EEX is the daily base or peak load spot price average

(Phelix Base, Phelix Peak), since the underlying of monthly futures contracts is the

monthly average of the Phelix day indices.

Price trends: Asymmetric effects are tested by dividing the whole sample period into

sub samples of underlying price increases/decreases and stable phases. Taking the

rockets and feathers theory as a foundation we would expect a delayed reaction of the

forward premium when spot price trends reverse from increasing to decreasing (see,

e.g., Borenstein et al. (1997) or Zachmann and v. Hirschhausen (2008) for an

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application in the energy sector). Whether the forward market participants adapt to

lasting periods of price trends remains to be empirically assessed.

Oil market volatility: Proposition: Increased volatility in the oil market increases the

electricity forward premium.

Due to the dominance of underlying oil prices for energy commodities in general,

besides the above discussion on risk premia spilling over to electricity markets and

behavioural effects in the expectation formation, price volatility in oil spot markets can

contribute to an increased premium in electricity contracts.

Conduct

Market power: Proposition: The exercise of market power in the spot market positively

influences the forward premium. Different (theoretical) modelling approaches

analysing the competitive effects of the introduction of futures markets in electricity

markets with a highly concentrated supply side yield contrary results (e.g. Allaz and

Vila (1993) vs. Robinson and Baniak (2002)). The estimation of reliable forward

market concentration proxy variables, which would allow empirical insights, is,

however, not doable. On the other hand, estimated base load and peak load price mark

ups for the spot market are available (own research – see figure 6 below which depicts

the evolution of electricity prices (EEX) and estimated monthly averages of system

marginal costs in the regional EU-4-market from 1999-2009). This variable –

especially its relative pattern compared to observed spot prices – can be an indicator of

the abuse of market power of the dominant producers.3 We argue that producers who

can increase spot market prices demand a higher premium to contract forward. On the

3 We note that the marginal cost estimate is an average monthly value, which is compared to the average base or peak load price index. Start up costs or other opportunity cost considerations are, hence, not part of the monthly average cost estimate. On the other hand, brief downward excursions in the day ahead price (e.g. negative daily prices on certain days in 2009) can cause average monthly prices to decrease, which, however, is not reflected in the average SRMC estimate. Therefore, observed market prices can, at certain months, also be below the SRMC estimate.

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other hand the buyers price generator market power as risk which increases the

willingness to pay forward. Yet, the linking of the mark up to the forward premium,

and the corresponding supply vs. demand side causes, is challenging from a

(empirical) modelling point of view.

Figure 6. Evolution of electricity prices (average baseload price at the EEX) and system marginal costs in the regional EU-4-market from 1999-2008. Source: EEX, BAFA, UCTE, own calculations

Dynamic effects

It has to be assessed whether there are any additional seasonal effects, above those which can

be fundamentally modelled (e.g. margin), in the forward premium. In a first step, a winter

dummy variable is intended to capture these additional effects. Still, further seasonal

representations, motivated by figure 4, need to be included in a comprehensive analysis.

Shocks

To be able to account for supply and demand shocks between forward trades and future spot

trades consumption and generation shock indices are introduced. The consumption shock

index is the ratio between actual electricity consumption in the relevant regional market and

the long-term average of the corresponding consumption in the specific month. Similarly, the

generation shock index of hydro and nuclear generation is the ratio between actual generation

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and the corresponding long-term average of generation.4 Proposition: Consumption shocks

negatively influence the forward premium, whereas the influence of generation shocks is

positive. If, ceteris paribus, consumption is unexpectedly high in the delivery month spot

prices should exceed forward prices. On the other hand, if, ceteris paribus, inframarginal

generation in hydro and nuclear plants rises unexpectedly spot prices should fall below

forward prices since the supply curve is shifted to the right.

The following table 2 qualitatively summarises the above introduced propositions on the

effects of forward premia components and respective proxy variables.

Table 2. Summary of forward premia determinants.

Effect on forward

premium Proxy variable

Fundamentals

Premia in fuels + Month ahead gas forward premium

Supply margin - Ratio generation/consumption in the regional market Behavioural effects

Variance ~ Coefficient of variation of spot price

Skewness + Skewness of spot price

Kurtosis + Kurtosis of spot price

Spikes + Count spikes outside 1, 2, 3 standard deviations of mean spot

Trends + Dummy to account for 1, 2, 3 month continuous spot increase

Oil volatility + Coefficient of variation of Brent oil spot price

Conduct Spot market

power + Fundamental cost mark up estimate for regional spot market

Dynamics

Winter seasonals + Dummy to account for winter months

Shocks

Supply shocks + Dummy to account for high inframarginal generation in delivery

month

Demand shocks - Dummy to account for high consumption in delivery month

In the following section structural models aiming to give insights on the above propositions

are presented.

4 The scope of the shock supply variables can be refined by considering other inframarginal generation besides hydro and nuclear power.

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6. A model of ex post forward premia

6.1 Adaptive expectation formation

A model is presented where market participants form myopic expectations. In this sense, they

are influenced by current and historic events on the spot market. These events, in turn,

contribute to the risk and market assessment of the agents and, hence, to the forward

premium. All parameters are observable for the market participants on the last trading day of

month t.

Base load

Sequentially minimising the AIC criterion and excluding the non-significant coefficients from

the comprehensive model – characterised by all parameters discussed in section 5– yields the

following equation for the ex post baseload forward premium:

,   , , (3)

where Ft,T-ST is the ex post forward premium, Ft,T is the futures price on the last

trading day in month t for delivery in month T, ST is the spot price average in month T, cv(St)

is the coefficient of variation of daily spot prices in month t, cv(Brentt) is the coefficient of

variation of daily Brent spot prices in month t, FPGas t-1,t is the realised gas forward premium

of a month ahead futures for month t, and ConsT is the consumption index in month T. Results

for the corresponding model are shown in table 3.

The significant positive influence of volatility in the oil market confirms the

“sentimental” importance of the oil market for energy commodities in general. Interestingly,

its influence is as important as the influence of the volatility on the electricity market itself.

The volatility of electricity spot prices positively influences the futures price and, hence, the

forward premium. The influence of the spot price volatility on the forward premium is in

agreement with the literature (Bessembinder and Lemmon (2002) and the corresponding

empirical literature cited above), however, the sign of this measure seems to be indeterminate

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as in our case it is positive.5 Realised premia in the gas market influence, as expected, the

electricity premia significantly positive, which shows the importance of gas fired power plants

also in baseload. Finally, the consumption shock coefficient gives the expected sign and is

statistically significant. Therefore, we consider this variable as being important for assessing

misjudgements of future demand conditions.

Table 3. Results of regression analysis (3) for ex post forward premia of month-ahead baseload futures at EEX for monthly delivery periods (t-statistics in brackets). All tests are based on heteroscedasticity consistent standard errors. Results are shown for premia determined by futures prices on the last trading day.

Coefficient Variable Base load

b1 Constant -3.25 (-1.09)

b2 Coeff of Var. Spot t 21.74 (3.18)

b3 Coeff of Var. Brent t 74.59 (2.17)

b4 Forward premium gas t 0.33 (1.92)

b5 Cons T -4.85 (-2.77)

R2 (R2corr) 0.21 (0.16)

DW 1.98

F-statistic 4.37

Serial correlation χ212 (p-value) 0.301

Functional form χ21 (p-value) 0.9065

Normality JB (p-value) 0.000

Heteroscedasticity χ24 (p-value) 0.744

Observations 71; 11/03-09/09

Peak load

A similar procedure to the above described one yields the following equation for the ex post

peak load forward premium:

,   ,     ,

(4)

where Ft,T-ST is the ex post forward premium, Ft,T is the peak load futures price on the

last trading day in month t for delivery during peak hours in month T, ST is the peak load spot

price average in month T, Margint is the ratio of regional generation and demand in month t,

5 Note that in our analysis volatility is measured via the coefficient of variation – and not via variance. Among others, this is motivated by allowing a better comparision between different “informational sources” of volatility for market actors (i.e. oil and power market volatility).

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FPGas t-1,t is the realised gas forward premium of a month ahead futures for month t, Spot

market powert is the ratio of the spot price in month t and the fundamental marginal cost

estimate for month t, and ConsT is the consumption index in month T. Results for the

corresponding model are shown in table 4.

Realised premia in the gas market influence, as expected, the electricity peak load

premia significantly positive. Generally, the price setting technologies in peak load hours are,

in fact, gas fired power plants. The significant positive influence gas market confirms the

importance of these generation technologies. Interestingly, the forward premium is positively

influenced by the market power estimate. In fact, spot price mark ups yield increases in the

forward premium. This can be caused by a higher willingness to pay of the buyers, which

price generator market power as a risk factor, a compensation demanded by dominant

producers to be willing to sell forward – and hence loose incentives to exercise their market

power in the spot market due to the contracted generation (Newbery, 1998), or a combination

of both. This result suggests, that the (positive) competitive effect of forward markets is, in

fact, limited.

If market participants perceive an increasing margin (or, correspondingly, a decreasing

scarcity) in the spot market, the forward premium tends to decrease (significant on a 10%

level). A decreasing margin is related to the increased likelihood of spikes occurring in the

spot market and, due to the convex supply curve, an increased skewness of spot prices. This

should increase the willingness to pay of risk averse buyers and represent opportunity costs of

producers having sold forward. Closely related to the spot price distribution is, in turn, the

scarcity of the system.

Due to this interrelationship statistical inference is difficult to obtain if parameters and

higher moments characterising the spot price distribution interfere with a fundamental

equivalent (e.g., in our case, the margin). Hence, the price distribution parameters are not part

of the model (4). Clearly, peak load prices are very sensitive to changes in a variety of

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parameters and statistically significant inference generally proves to be elusive. Finally, the

consumption shock coefficient shows the expected sign and is statistically significant.

Table 4. Results of regression analysis (4) for ex post forward premia of month-ahead peak load futures at EEX for monthly delivery periods (t-statistics in brackets). Results are shown for premia determined by futures prices on the last trading day.

Coefficient Variable Peak load

b1 Constant 204.02 (1.63)

b2 Margin t -185.02 (-1.58)

b3 Forward premium gas t 0.93 (1.89)

b4 Market power spot t 12.65 (1.94)

b5 Cons T -10.28 (-2.41)

R2 (R2corr) 0.18 (0.13)

DW 2.04

F-statistic 3.51

Serial correlation χ212 (p-value) 0.767

Functional form χ21 (p-value) 0.090

Normality JB (p-value) 0.000

Heteroscedasticity χ21 (p-value) 0.419

Observations 71; 11/03-09/09

7. Conclusions and Outlook

We have introduced a taxonomy of electricity forward premia determinants in this paper.

Moreover, preliminary empirical models have been presented to give insights into the

corresponding propositions on the forward premium, which suggest the latter to be affected

by fundamental, behavioural, dynamic, conduct and unexpected components.

In fact, the risk attitude of participants in the electricity market is strongly influenced

by the agents’ assessment of commodities, which serve as fuel input (e.g. gas) or are of

sentimental importance for energy commodities in general (e.g. perception of the oil market

and its volatility). Moreover, market participants react sensitively on volatility in the

electricity market itself and on extreme events occurring in the spot market during the trading

period of forward contracts. This is mirrored in a significant influence of the scarcity of the

system. Interestingly, parameters indicating price mark ups and, correspondingly, the exercise

of market power contribute to increased forward premia which questions the competitive

20

effect of long term markets. Finally, demand shock measures contribute to the forward

premium. This is in line with our assumption on the adaptiveness of the market participants.

The results have to be interpreted with due care. First, they arise of a rather short data

set. As in any empirical analysis, the passing of time will allow a more complete investigation

of the market and an assessment of our propositions. In any case, due to the complex

interactions of market forces and its drivers, empirical models alone cannot encompass all

causal market relationships. Hence, as a next step, besides increasing the quality and

complexity of the empirical models – which will comprise a more subtle representation of the

evaluation of trend effects – we intend to provide further insights by developing a simple

equilibrium model of the market, which shall focus on issues which interact rather strongly

with each other: Market power and price mark ups, risk aversion, supply and demand shocks

and fuel price uncertainty. This research can build upon seminal work by Allaz (1991), Allaz

and Vila (1993), Newbery (1998), Green (1999) and Bessembinder and Lemmon (2002).

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