Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae

Manner, H, Alavi Fard, F, Pourkhanali, A and Tafakori, L 2019, 'Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae', Energy Economics, vol. 78, pp. 143-164.


Document type: Journal Article
Collection: Journal Articles

Title Forecasting the joint distribution of Australian electricity prices using dynamic vine copulae
Author(s) Manner, H
Alavi Fard, F
Pourkhanali, A
Tafakori, L
Year 2019
Journal name Energy Economics
Volume number 78
Start page 143
End page 164
Total pages 22
Publisher Elsevier
Abstract We consider the problem of modelling and forecasting the distribution of a vector of prices from interconnected electricity markets using a flexible class of drawable vine copula models, where we allow the dependence parameters of the constituting bivariate copulae to be time-varying. We undertake in-sample and out-of-sample tests using daily electricity prices, and evidence that our model provides accurate forecasts of the underlying distribution and outperforms a set of competing models in their abilities to forecast one-day-ahead conditional quantiles of a portfolio of electricity prices. Our study is conducted in the Australian National Electricity Market (NEM), which is the most efficient power auction in the world. Electricity prices exhibit highly stylised features such as extreme price spikes, price dependency between regional markets, correlation asymmetry and non-linear dependency. The developed approach can be used as a risk management tool in the electricity retail industry, which plays an integral role in the apparatus of modern energy markets. Electricity retailers are responsible for the efficient distribution of electricity, while being exposed to market risk with extreme magnitudes.
Subject Applied Statistics
Probability Theory
Statistical Theory
Keyword(s) Back-testing
Dvine copula
Electricity prices
Nonlinear dependence
SCAR model
DOI - identifier 10.1016/j.eneco.2018.10.034
Copyright notice © 2018 Elsevier B.V. All rights reserved.
ISSN 0140-9883
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