Inference of Markovian-regime-switching models with application to South Australian electricity prices
| dc.contributor.advisor | Bean, Nigel | |
| dc.contributor.author | Lewis, Angus | |
| dc.contributor.school | School of Mathematical Sciences | en |
| dc.date.issued | 2018 | |
| dc.description.abstract | Markovian-Regime-Switching (MRS) models are commonly used for modelling economic time series, including electricity prices. In this application it is common to include inde- pendent regimes as these can more accurately capture the dynamics of electricity prices compared to traditional MRS models. The advantage of independent regime MRS specifications is that they allow us to seperate dynamics between regimes. Despite their popularity, parameter inference for MRS models with independent regimes is underdeveloped. Until this thesis, there was no computationally feasible method to evaluate the likelihood of, or find maximum likelihood estimate for, MRS models with independent regimes. Moreover, there are no good discussions of Bayesian methods for such models applied to electricity prices. In this thesis we develop both maximum likelihood and Bayesian inference methodologies for MRS models with independent regimes, and use simulations to investigate their behaviours. We use our methods to investigate the South Australian wholesale electricity market, and find evidence of a significant jump in price volatility which coincides with the closure of South Australia's only coal generation facility, and therefore a significant change in market structure. Our work also suggests that Bayesian methods can be advantageous compared to maximum likelihood, since Bayesian methods can avoid issues with inferring parameters of shifted distributions, which are commonly used in this context. | en |
| dc.description.dissertation | Thesis (MPhil) -- University of Adelaide, School of Mathematical Sciences, 2018 | en |
| dc.identifier.uri | http://hdl.handle.net/2440/120418 | |
| dc.language.iso | en | en |
| dc.provenance | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals | en |
| dc.subject | Regime-Switching Time-series | en |
| dc.subject | independent regime | en |
| dc.subject | forward-backward algorithm | en |
| dc.subject | expectation maximisation | en |
| dc.subject | data-augmented MCMC | en |
| dc.title | Inference of Markovian-regime-switching models with application to South Australian electricity prices | en |
| dc.type | Thesis | en |
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