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Analysis and Forecasting of Electricty Price Risks with Quantile Factor Models

Derek Bunn, Arne Andresen, Dipeng Chen, Sjur Westgaard

Year: 2016
Volume: Volume 37
Number: Number 1
DOI: 10.5547/01956574.37.1.dbun
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Abstract:
Forecasting quantile and value-at-risk levels for commodity prices is methodologically challenging because of the distinctive stochastic properties of the price density functions, volatility clustering and the importance of exogenous factors. Despite this, accurate risk measures have considerable value in trading and risk management with the topic being actively researched for better techniques. We approach the problem by using a multifactor, dynamic, quantile regression formulation, extended to include GARCH properties, and applied to both in-sample estimation and out-of-sample forecasting of traded electricity prices. This captures the specification effects of mean reversion, spikes, time varying volatility and demonstrates how the prices of gas, coal and carbon, forecasts of demand and reserve margin in addition to price volatility influence the electricity price quantiles. We show how the price coefficients for these factors vary substantially across the quantiles and offer a new, useful synthesis of GARCH effects within quantile regression. We also show that a linear quantile regression model outperforms skewed GARCH-t and CAViaR models, as specified on the shocks to conditional expectations, regarding the accuracy of out-of-sample forecasts of value-at-risk.



Fundamental and Financial Influences on the Co-movement of Oil and Gas Prices

Derek Bunn, Julien Chevallier, Yannick Le Pen, and Benoit Sevi

Year: 2017
Volume: Volume 38
Number: Number 2
DOI: 10.5547/01956574.38.2.dbun
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Abstract:
As speculative flows into commodity futures are expected to link commodity prices more strongly to equity indices, we investigate whether this process also creates increased correlations amongst the commodities themselves. Considering U.S. oil and gas futures, we investigate whether common factors, derived from a large international data set of real and nominal macroeconomic variables by means of the large approximate factor models methodology, are able to explain both returns and whether, beyond these fundamental common factors, the residuals remain correlated. We further investigate a possible explanation for this residual correlation by using some proxies for trading intensity derived from CFTC publicly available data, showing most notably that the proxy for speculation in the oil market increases the oil-gas correlation. We thus identify the central role of financial activities in shaping the link between oil and gas returns.



The Variation in Capacity Remunerations Requirements in European Electricity Markets

Conor Hickey, Derek Bunn, Paul Deane, Celine McInerney, and Brian Ó Gallachóir

Year: 2021
Volume: Volume 42
Number: Number 2
DOI: 10.5547/01956574.42.2.chic
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Abstract:
This paper provides the first EU wide analysis of the variation in Capacity Remuneration Requirements throughout Europe which aim to resolve the �missing money� problems in various member states. The findings of this analysis point to an asymmetric investment case for gas-fired peaking power plants throughout the EU. Under the assumptions of the European Commission Reference Scenario, pan-European power optimisation and investment models are specified for 2030. The results show that future investment in gas generators will depend on the availability of capacity payments. Capacity remuneration mechanisms can provide this "missing money," but we show that capacity remuneration requirements vary considerably across countries. We consider and model the impacts of country specific climate policy targets, sovereign risk, capital allowances, corporate taxes and future gas network tariffs on investor returns and therefore remuneration requirements. In the context of harmonised energy trading, this raises questions of how generation adequacy should be achieved, particularly in the context of higher penetrations of renewables.





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