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.
Highlights
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 peak price quantiles for GB power
For managers involved in risk and operations, as well as for regulators concerned with market surveillance, modelling and forecasting the tails of price distributions in traded markets may often be a more crucial activity than formulating central expectations
We have characterised the nonlinear effects of exogenous factors on peak hour wholesale electricity price formation as well as forecasting the price distribution
Summary
For managers involved in risk and operations, as well as for regulators concerned with market surveillance, modelling and forecasting the tails of price distributions in traded markets may often be a more crucial activity than formulating central expectations. Quantile regression approaches do not yet appear to have been applied to energy commodity prices It appears to follow both from the evident GARCH properties in power prices suggesting a persistence of shocks, and the in-sample regime-switching models between electricity fundamentals suggesting the need to accommodate varying effects of exogenous factors, that there is a prima facie case for examining the joint specification of both fundamental factors and volatility as explanatory factors. The British market is a transparent one with useful exogenous day-ahead information available to market participants, and, being well-established, there is a long time series provide an extensive out-of-sample analysis of day ahead forecasts With this methodological proposal, it is important to benchmark this quantile regression approach against the most appropriate GARCH models, typical in practice, and the potential use of CAViaR models in this context. In-sample and out-of-sample results are reported and a concluding section summarises the key research contributions
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