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Analysis and Forecasting of Electricity Price Risks with Quantile Factor Models

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  • Derek Bunn
  • Arne Andresen
  • Dipeng Chen
  • Sjur Westgaard

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.

Suggested Citation

  • Derek Bunn & Arne Andresen & Dipeng Chen & Sjur Westgaard, 2016. "Analysis and Forecasting of Electricity Price Risks with Quantile Factor Models," The Energy Journal, , vol. 37(1), pages 101-122, January.
    • Handle: RePEc:sae:enejou:v:37:y:2016:i:1:p:101-122
    DOI: 10.5547/01956574.37.1.dbun
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    References listed on IDEAS

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    1. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    2. Tryggvi Jónsson & Pierre Pinson & Henrik Madsen & Henrik Aalborg Nielsen, 2014. "Predictive Densities for Day-Ahead Electricity Prices Using Time-Adaptive Quantile Regression," Energies, MDPI, vol. 7(9), pages 1-25, August.
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    Cited by:

    1. Gökgöz, Fazıl & Yücel, Öykü, 2024. "Merit-order of dispatchable and variable renewable energy sources in Turkey's day-ahead electricity market," Utilities Policy, Elsevier, vol. 88(C).

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