The goal of various worldwide liberalisation efforts of the electricity supply industry was the introduction of competition as precondition for an efficient energy supply. In Europe, the liberalisation process dates back to the 1990s when the first electricity directive concerning common rules for the internal market in electricity was adopted in 1996. Along with the introduction of competition, a transition from a cost based price regulation towards a market orientated price formation took place and market platforms for short- and long-term trade of electricity were established. In competitive power markets, the wholesale price is determined by the generation costs of the marginal technology (i.e. the short run marginal costs (SRMC) of the most expensive plant needed to meet demand – merit order principle).

In this new, competitive environment risks emerged for market participants on either side of the market unknown in the previous regulated area. Long-term contracts like futures or forwards, traded at power exchanges and bilaterally over-the-counter, allow for management of the price risk by effectively locking in a fixed price and therefore avoiding uncertain future spot prices. Due to the prominence of long-term electricity markets, the determination of relevant influence factors on the price formation on these markets is of great interest.

Spot and forward trading in electricity

Electricity is a flow, rather than a stock, commodity; it is produced and consumed continuously and instantaneously. Traded physical products are therefore defined and sold in the form of metered contracts for the constant delivery of a specified amount of power over a specified period of time, e.g. 1 MW for 1 h (MWh). Most “spot” markets deal in such hourly products, although some, e.g. Britain and Australia, have finer granularity at half-hourly intervals. These hourly (or half-hourly) spot prices emerge either from an auction process whereby generators and retailers make offers and bids (which may be held once on the previous day to set all hourly prices for the subsequent day) or continuous trading on an exchange platform from day-ahead until a particular time before actual delivery (e.g. an hour in Britain). Ahead of this spot trading, forward trading is usually in coarser products defined as the delivery of power over longer periods of time.

Thus, “baseload” forward contracts provide for the continuous delivery of power over a year, or a quarter, month, weekday, weekend or a day, whilst “peak” contracts provide continuous delivery only during the daytime for the same range of delivery periods. There may be other blocks of time periods traded, such as the 4 and 2 h products on the power exchange for the British market. Since both retailers and generators face volume uncertainty in real-time; retailers, because customers are free to consume (up to the levels of their fused connections) and generators, because plant can be unreliable and renewable outputs such as wind and solar are stochastic. Typically, therefore, volume risk will be managed through successively finer hedging as delivery time approaches: a year or two ahead, for example, a retailer may buy a baseload contract for its annual average demand; several months ahead this may be re-profiled into monthly peak and baseload contracts; within the week ahead, these may be re-profiled several times, as demand forecasts adapt, into separate daily peak and baseload contracts and finally on the day, re-profiled into hourly spot products to match the expected demand curve.

Spot price formation itself, because consumers are price inelastic in the short term and cannot store electricity once generated, is mainly a function of the demand, technology mix and degree of competition amongst generators. For a particular level of demand at a particular time, there will be a stack of generating technologies available, and price formation is usually taken as the offer of the most expensive plant needed at that time. If the market was competitive, and generators offered at short-term marginal costs, market price volatility would be envisaged as the projection of demand volatility on to the supply function offered by the generators (Stoft, 2002). Given the various plant technologies available for dispatch at any time instant, differentiated in terms of costs and operational constraints, this short-run cost function is intrinsically steeply increasing, discontinuous and convex. In the presence of these characteristics and with the negligible demand elasticity in the short term spot prices are very sensitive to real-time uncertainties, such as demand shocks and plant outages.

Thus, expectations of spot prices involve at least considerations of the underlying fuel (for short term marginal costs) and the reserve margin (for scarcity pricing above marginal cost). Moreover, as almost all electricity markets are oligopolies, at times of scarcity, when the reserve margin (available supply minus demand) is low, those generators with market power may offer and create market prices substantially above marginal costs. This creates a further behavioural and possibly nonlinear element to price formation.

With the emergence of electricity spot markets, the statistical behaviour of electricity prices attracted the attention of speculators, arbitrageurs and risk managers. Spot prices became the focus of research, as their development determines the prices of electricity forwards and, ultimately, those of derivatives such as options and futures which are written on spot and forward contracts. The emergence of electricity spot markets opened up a new field of study within financial mathematics. This field has produced important findings regarding electricity spot prices: prices mostly contain no unit root; prices exhibit mean reverting; prices feature strong seasonalities, high volatility, and heavy tails; prices feature heteroskedastisity, and exhibit long memory behaviour. Sophisticated stochastic models for electricity prices were developed based on these findings with key explanatory variables for price behaviour being weather, fuel prices, emission allowance prices, available generation capacities, imports, transmission congestion, market structure, and national fuel mix. These factors differ substantially between markets and, thus, electricity price models are often only suitable for a specific power exchange.

Due to the aforementioned properties of electricity prices – e.g., strong heteroskedasticity and pronounced price spikes – Markov regime switching models have gained considerable popularity for the modelling of electricity prices. These models depart from regime specifications which take the hypothesis that electricity prices feature regimes with different statistical properties. Two-regime specifications typically assume the existence of: (1) a base regime which explains electricity prices most of the time, and is characterised by below‐average prices and below-average volatility and (2) a peak regime that is short-lived and features above-average prices and volatility. The energy-economic rationale for this type of model is that there is the need for scarcity prices which allow the most expensive power plants, only needed in situations with very high demand and/or low available capacity, to recover their fixed cost. Three-or-more regime specifications are used as a sophistication of the two-regime approach and it has been shown that price and volatility forecasts for electricity prices can be significantly improved with regime switching models as compared to models without regimes.

Correlation between spot and forward prices

A common way to the joint movement between spot and forward prices is through regression and correlation analysis. For most markets, correlations between spot prices and forward prices are usually positive, with different degrees of magnitude depending on the market and time period under consideration. However, correlation between these two is highly unstable which puts into question the assumption of perfect correlation used in many one factor models.

Correlation is just one of many measures of co-movement between two price series. They can play a large role in identifying and measuring co-movement but in many cases it is not sufficient to capture the relation between different markets. The main reason that many models produce unrealistic simulations is that they fail to factor the equilibrium levels between spot and forward prices; movemements in the short term may not move in the same direction, but the basis is not purely random and will revert to a mean level.

Spot Premia

Spot prices are quite different in their properties from the longer term (month-ahead), with the spot pricing reflecting more of the operational aspects and the monthly prices being more based upon fundamental expectations. Intaday it is observed that there is a premium placed upon the Peak product to reflect generators risk of high price spikes during the day and the more competitive conditions for generators during the low demand night-time trading. Using a two regime, Markov switching representation gives a more coherent analysis than a single equation linear model; Peak prices in the high volatility regime have a significant adaptive, behavioural component, whilst fuel fundamentals show up more off-peak and in the less volatile regime. In the monthly analysis, fuel price fundamentals play the major role.

In both the monthly and daily analyses, the importance of taking into account the demand–supply shock, as manifested by a change in margin, between the contracting and delivery time points, is an important control variable for an ex post evaluation. Likewise, the importance of fuel pass-through is a common issue in both analyses, and it carries the seasonal consequences to power of higher winter gas prices. The forward risk premium for power therefore substantially embeds that of its underlying fuel commodity (e.g. gas in the UK).

Whilst market idiosyncrasies around the world undoubtedly lead to different statistical properties, electricity wholesale markets work in similar ways with similar technologies and globally price fossil fuels, and so it has been suggested that generalisations, particularly methodological, of the insights in this work can extend beyond the UK market. In the future, it is likely that forward premia analysis will become even more critical to power market analysis, as high capex, low opex technologies (renewables and perhaps nuclear) replace conventional coal and gas facilities. Investment returns on these technologies will require forward markets to deliver prices substantially above marginal cost, whilst, at the same time, their introduction will create a market that may become much more volatile through intermittent wind and solar supplies. This suggests that not only is it challenging at the moment to model the premium in power futures, looking ahead it is likely to require the incorporation of additional dynamic techniques to reflect the radical market structure changes which are expected to evolve.


  1. The forward premium in electricity futures – Journal of Empirical Finance
  2. A stochastic fuel switching model for electricity prices – Energy Economics
  3. Joint Simulation of Spot Prices and Forward Curves – Energy Risk


  1. Excellent article Patrick! For a novice in the energy trading markets as I am, this article covers most of the fundamentals in a very concise way. Curious to browse the rest of the blog as well. Thanks for the good stuff.

    • Hi Mirko – It’s my pleasure and feel free to have a browse around…to be honest I just cover things that interest me and that I’d like to find more out about.

      If there’s anything you’d like written about then just let me know!

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