Decoding the Australian electricity market : New evidence from three-regime hidden semi-Markov model

Nicholas Apergis, Giray Gozgor, Chi Keung Marco Lau, Shixuan Wang

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)

    Abstract

    The hidden semi-Markov model (HSMM) is more flexible than the hidden Markov model (HMM). As an extension of the HMM, the sojourn time distribution in the HSMM can be explicitly specified by any distribution, either nonparametric or parametric, facilitating the modelling for the stylised features of electricity prices, such as the short-lived spike and the time-varying mean. By using a three-regime HSMM, this paper investigates the hidden regimes in five Australian States (Queensland, New South Wales, Victoria, South Australia, and Tasmania), spanning the period from June 8, 2008 to July 3, 2016. Based on the estimation results, we find evidence that the three hidden regimes correspond to a low-price regime, a high-price regime, and a spike regime. Running the decoding algorithm, the analysis systemically finds the timing of the three regimes, and thus, we link the empirical results to the policy changes in the Australian National Electricity Market. We further discuss the contributing factors for the different characteristics of the Australian electricity markets at the state-level.

    Original languageEnglish
    Pages (from-to)129-142
    Number of pages14
    JournalEnergy Economics
    Volume78
    DOIs
    Publication statusPublished - 18 Feb 2019

    Bibliographical note

    Funding Information:
    The research of the corresponding author was supported by the Economic and Social Research Council (UK) [grant number ES/J50001X/1 ] and the Royal Economic Society Junior Fellowship.

    Publisher Copyright:
    © 2018 Elsevier B.V.

    Copyright:
    Copyright 2019 Elsevier B.V., All rights reserved.

    Fingerprint

    Dive into the research topics of 'Decoding the Australian electricity market : New evidence from three-regime hidden semi-Markov model'. Together they form a unique fingerprint.

    Cite this