A first look at the empirical relation between spot and futures electricity prices in the United States

2015

Energy Economics, 2012

The goal of this paper is to examine to what extent electricity futures prices contain expected risk premiums or have power to forecast spot prices and whether this might be dependent on the type of electricity supply. We analyse futures prices from the Dutch market, a market in which power is produced with storable fossil fuels, and futures prices from the NordPool market, where electricity is mostly produced by hydropower. We show that futures prices from markets in which electricity is predominantly produced by imperfectly storable fuels such as hydro, wind and solar contain information about expected changes in the spot price of electricity, whereas futures prices from markets in which electricity is predominantly produced with perfectly storable fuels contain information about both expected price changes and time-varying risk premiums. These findings provide insight in the applicability of forward price models; one cannot apply the same model to all electricity markets. Forward models for markets with imperfect indirect storability should depend heavily on price expectations and models should include time-varying risk premiums for markets with perfect indirect storability.

International Journal of Finance & Economics

This paper estimates and applies a risk management strategy for electricity spot exposures using futures hedging. We apply our approach to three of the most actively traded European electricity markets, Nordpool, APXUK and Phelix. We compare both optimal hedging strategies and the hedging effectiveness of these markets for two hedging horizons, weekly and monthly using both Variance and Value at Risk (VaR). Our key finding is that electricity futures can effectively manage risk only for specific time periods when using hedging strategies that have been very successful in financial and other commodity markets. More generally they are ineffective as a risk management tool when compared with other energy assets. This is especially true at the weekly frequency. We also find significant differences in both the Optimal Hedge Ratios (OHR's) and the hedging effectiveness of the different electricity markets. Better performance is found for the Nordpool market, while the poorest performer in hedging terms is the Phelix market.

SSRN Electronic Journal, 2017

We model the impact of supply and demand on risk premiums in electricity futures, using daily data for 2003-2014. The model provides a satisfactory fit and allows for unspanned economic risk not embedded in the futures price. The spot risk premium and forward bias implied by the model are on average large and negative but highly time-varying. Risk premiums display strong seasonal patterns, are related to the variance and skewness of the electricity spot price, and help predict future returns. The risk premium associated with supply constitutes the largest component of the total risk premium embedded in electricity futures.

2003

This paper examines the transmission of spot electricity prices and price volatility among the five Australian electricity markets in the National Electricity Market (NEM): namely, New South Wales (NSW), Queensland (QLD), South Australia (SA), Snowy Mountains Hydroelectric Scheme (SNO) and Victoria (VIC). A multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model is used to identify the source and magnitude of innovations

E3S Web of Conferences, 2020

This paper presents an original trading strategy for electricity buyers in futures markets. The strategy applies a medium-term electricity price forecasting model to predict the monthly average spot price which is used to evaluate the Risk Premium for a physical delivery under a monthly electricity futures contract. The proposed trading strategy aims to provide an advantage relatively to the traditional strategy of electricity buyers (used as benchmark), anticipating the good/wrong decision of buying electricity in the futures market instead in the day-ahead market. The mid-term monthly average spot price forecasting model, which supports the trading strategy, uses only information available from futures and spot markets at the decision moment. Both the new trading strategy and the monthly average spot price forecasting model, proposed in this paper, have been successfully tested with historical data of the Iberian Electricity Market (MIBEL), although they could be applied to other ...

Energy Policy, 2019

Against the backdrop of numerous evidence that variable renewable generation decreases electricity prices and increases price volatility, this paper assesses the drivers of electricity prices in spot and futures markets, focusing on the German electricity markets. We take into account nonlinearities in electricity prices by means of structural breaks and threshold regressions. We find that short-run and medium/long-run price drivers differ and, more importantly, that they vary over time. In the case of the spot market, the determinants of prices are renewable infeed and electricity demand, while in the futures market the main drivers are natural gas, coal and carbon prices. Our results give relevant insights for market participants who seek to optimize procurement/ selling strategies in the spot market, and use the futures market to hedge against spot price volatility, which has increased due to a higher renewable generation.

2010

Electricity markets have several interesting features. For example, electricity spot markets are not free markets, but oligopoly; and due to the low elasticity in the demand, the spot prices have large spikes, which may lead to infinite expectation in the spot prices, although the futures prices have much less spikes and tend to have finite expectations. We propose a two-factor model for on-peak electricity spot and futures prices, starting from the electricity demand. More specifically, we first model the the peak demand as a two-factor mean reverting affine jump diffusion process, attempting to describe two different mean reversion features related to the normal peak demand and abnormal spikes in the demand separately. Then we derive the on-peak spot price based on a minimum Nash equilibrium for the demand-and-price function in oligopoly markets, using the existing game theory results. After constructing a risk-adjusted measure via an equilibrium argument, we obtain analytical solutions for futures prices and call and put options on futures prices. Not only can our model capture some intrinsic features of electricity spot prices, such as mean reversion, spikes, and seasonality, but it is also consistent with the empirical observation that under the physical measure, the on-peak spot prices may have either finite or infinite expectations, while the futures prices tend to have finite means under the physical measure. Numerical examples indicate that our model seems to more reasonable than the existing one-factor models.

2002

We conduct an empirical analysis of electricity forward prices using a high-frequency data set of hourly spot and day-ahead forward prices. We find that there are significant risk premia in electricity forward prices. These premia vary systematically throughout the day and are directly related to economic risk factors such as the volatility of unexpected changes in prices and demand as well as the risk of price spikes. In contrast to the popular post-Enron view that electricity markets are easily manipulated, these results support the hypothesis that electricity forward prices are determined rationally by risk-averse economic agents. . We are grateful for helpful discussions with David Hirshleifer, Mitz Igarashi, and Scott Benner. All errors are our responsibility.

IEEE Transactions on Power Systems, 2005

Price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets. Both for spot markets and long-term contracts, price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize profits. This paper provides an approach to predict next-day electricity prices based on the Generalized Autoregressive Conditional Heteroskedastic (GARCH) methodology that is already being used to analyze time series data in general. A detailed explanation of GARCH models is presented and empirical results from the mainland Spain and California deregulated electricity-markets are discussed.