Search

Begin New Search
Proceed to Checkout

Search Results for All:
(Showing results 1 to 5 of 5)



Risk Premiums and Efficiency in the Market for Crude Oil Futures

Richard Deaves and Itzhak Krinsky

Year: 1992
Volume: Volume 13
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol13-No2-5
View Abstract

Abstract:
The New York Mercantile Exchange's Crude Oil futures contract is investigated for the existence and nature of risk premiums and informational efficiency. During 1983-90, there is some evidence that short-term premiums were positive and covaried with recent volatility. As for efficiency, we find nothing inconsistent with weak-form efficiency, but some apparent violations cf semi-strong efficiency. We argue that, for a number of reasons, such rejections should be interpreted with caution.



Spatial Risk Premium on Weather Derivatives and Hedging Weather Exposure in Electricity

Wolfgang Karl Hardle and Maria Osipenko

Year: 2012
Volume: Volume 33
Number: Number 2
DOI: 10.5547/01956574.33.2.7
View Abstract

Abstract:
Due to the dependency of the energy demand on temperature, weather derivatives enable the effective hedging of temperature related fluctuations. However, temperature varies in space and time and therefore the contingent weather derivatives also vary. The spatial derivative price distribution involves a risk premium. We employ a pricing model for temperature derivatives based on dynamics modeled via a vectorial Ornstein-Uhlenbeck process with seasonal variation. We use an analytical expression for the risk premia depending on variation curves of temperature in the measurement period. The dependence is exploited by a functional principal component analysis of the curves. We compute risk premia on cumulative average temperature futures for locations traded on CME and fit to it a geographically weighted regression on functional principal component scores. It allows us to predict risk premia for nontraded locations and to adopt, on this basis, a hedging strategy, which we illustrate in the example of Leipzig. Keywords: Risk premium, Weather derivatives, Ornstein-Uhlenbeck process, Functional principal components, Geographically weighted regression



Electricity futures prices in an emissions constrained economy: Evidence from European power markets

George Daskalakis, Lazaros Symeonidis, Raphael N. Markellos

Year: 2015
Volume: Volume 36
Number: Number 3
DOI: 10.5547/01956574.36.3.gdas
View Abstract

Abstract:
We investigate the economic factors that drive electricity risk premia in the European emissions constrained economy. Our analysis is undertaken for monthly baseload electricity futures for delivery in the Nordic, French and British power markets. We find that electricity risk premia are significantly related to the volatility of electricity spot prices, demand and revenues, and the price volatility of the carbon dioxide (CO2) futures traded under the EU Emissions Trading Scheme (EU ETS). This finding has significant implications for the pricing of electricity futures since it highlights for the first time the role of carbon market uncertainties as a main determinant of the relationship between spot and futures electricity prices in Europe. Our results also suggest that for the electricity markets under scrutiny futures prices are determined rationally by risk-averse economic agents.



Renewable Energy Technologies and Electricity Forward Market Risks

Derck Koolen, Derek Bunn, and Wolfgang Ketter

Year: 2021
Volume: Volume 42
Number: Number 4
DOI: 10.5547/01956574.42.4.dkoo
View Abstract

Abstract:
We analyse how the introduction of the same renewable energy technology at different parts of the electricity supply chain has different price formation effects on wholesale power markets. We develop a multi-stage competitive equilibrium model to evaluate the effects on short-term price formation of a technology shift from conventional to both large-scale renewable energy production (e.g. wind and solar farms) and distributed renewable energy sources (e.g. rooftop solar). We find that wind and solar technologies oppositely affect the forward risk premium, and this is related to technology-varying, risk-related hedging pressures of producers and retailers. We form a multi-factor propositional framework and empirically validate the model by analyzing data from California and Britain; two markets which recently experienced significant increases of renewable power, in terms of utility scale and distributed sources. The work is innovative in showing theoretically and empirically how different types of renewable technologies influence market price formation differently. This has implications for market participants facing wholesale price risks, as well as regulators and policy-makers.



Variance Risk Premium in Energy Markets: Ex-Ante and Ex-Post Perspectives

Giacomo Morelli

Year: 2022
Volume: Volume 43
Number: Special Issue
DOI: 10.5547/01956574.43.SI1.gmor
View Abstract

Abstract:
This paper introduces the ex-ante estimation of the variance risk premium. The novel methodology proposed is applied to forecast variance risk premium in energy markets, capturing the future degree of aversion of investors towards energy variance risks. We analyze the ex-ante variance risk premium of two energy indices, XLE and USO, during the period that spans from 2011 to 2022, and compare them to that of the SPX, the benchmark for the equity market. In the computation of the ex-ante variance risk premium, simple GARCH and Markov-switching GARCH models are exploited to forecast the realized variance, while variance swap rates are retrieved from the volatility indices VXXLE, OVX, and VIX of the three market indices. We find that the ex-ante variance risk premium succeeds to forecast the imminent periods of financial distress empirically detected in the abrupt surges and plunges of the ex-post variance risk premium. In particular, USO shows higher magnitudes of the variance risk premium than XLE and SPX, predicting that investors require on average higher premiums to bear oil variance risks.





Begin New Search
Proceed to Checkout

 

© 2024 International Association for Energy Economics | Privacy Policy | Return Policy