Forward and Spot Prices in Electricity and Gas Markets

Quinn, Jenna; Reitzes, James D.; Schumacher, Adam C.

doi:10.1007/0-387-23196-x_6

Cited by 5 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, since electricity is largely non-storable, there is no cost-of-carry relationship linking spot and forward prices. For example, Quinn et al (2005) examine spot and forward data from the PJM 1 market and find that electricity forward prices are largely disconnected from current spot prices. draw similar conclusions when examining spot and forward prices at Nord Pool.…”

Section: For Discussion) This Suggests Thatmentioning

confidence: 99%

Modeling electricity forward prices using the multivariate normal inverse Gaussian distribution

Andresen¹,

Koekebakker²,

Westgaard³

2010

JEM

View full text Add to dashboard Cite

show abstract

Section: For Discussion) This Suggests Thatmentioning

confidence: 99%

Modeling electricity forward prices using the multivariate normal inverse Gaussian distribution

Andresen¹,

Koekebakker²,

Westgaard³

2010

JEM

View full text Add to dashboard Cite

show abstract

“…Besides that, this approach has some disadvantages because endogenously generated swap prices are not necessarily consistent with observable market prices. Quinn, Reitzes and Scumacher (2005) argue that electricity swap prices are a function of market expectations of demand and cost conditions during the actual delivery period, and these expectations are not necessarily influenced by current market behaviour (i.e. spot prices), and they present evidence supporting their claim in the PJM market.…”

Section: Literature Reviewmentioning

confidence: 87%

Modelling Electricity Swaps with Stochastic Forward Premium Models

Blanco¹,

Peña²,

Rodríguez³

2018

The Energy Journal

View full text Add to dashboard Cite

We present a new model for pricing electricity swaps. Two general factors affect contracts but unique risk elements affect each contract. General factors are average swap prices and deterministic trend-seasonal components, and unique elements are forward premiums. Innovations follow MNIG distributions. We estimate the model with data from the European Energy Exchange. The model outperforms four competitors, both in in-sample valuation and in out-of-sample forecasting, and in fitting the term structure of volatilities by market segments. Competitor models are (i) diffusion spot prices, (ii) jump-diffusion spot prices with time dependent volatility, (iii) HJM-based and (iv) Lévy multifactor model with NIG distributions. Value-at-Risk measures based on normality strongly underestimate tail risk but our model gives estimates that are more exact.

show abstract

“…Futures prices trade five days a week and are always non-negative, the seasonal variation is less salient, price spikes are less frequent and smaller, the average price increases with maturity and the volatility (Samuelson effect), skewness and kurtosis decrease with maturity (Blanco et al, 2018), but they may present regime-switching and non-Gaussian marginal distributions (Andresen et al, 2010). As Quinn, et al (2005) argue, futures prices are a function of market expectations of demand and cost conditions during the delivery period. The current market situation (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%