2005
DOI: 10.5547/issn0195-6574-ej-vol26-no4-2
|View full text |Cite
|
Sign up to set email alerts
|

Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects

Abstract: This paper investigates the intraday price volatility process in four Australian wholesale electricity markets; namely New South Wales, Queensland, South Australia and Victoria. The data set consists of half-hourly electricity prices and demand volumes over the period January 1, 2002 to June 1, 2003. A range of processes including GARCH, RiskMetrics, normal Asymmetric Power ARCH or APARCH, Student APARCH and skewed Student APARCH are used to model the time-varying variance in prices and the inclusion of news a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

6
40
2

Year Published

2007
2007
2021
2021

Publication Types

Select...
7

Relationship

2
5

Authors

Journals

citations
Cited by 53 publications
(48 citation statements)
references
References 37 publications
6
40
2
Order By: Relevance
“…The log difference of the unadjusted price series, P i , is taken to create the price movement series, 100.log P i log P i 1 /. Many authors (see, for example, Solibakke 2002;Higgs and Worthington 2005) have noted systematic calendar (weekends, (moving) holidays and summer/winter), trend and scale effects in both the mean and variance of the system price movements. To adjust for these documented shifts in both the mean and volatility of the raw electricity price series, a two-stage adjustment procedure is carried out, in which systematic and deterministic effects are removed first from the mean and then from the variance .…”
Section: Energy Market Data and Adjustments For Stationaritymentioning
confidence: 99%
See 1 more Smart Citation
“…The log difference of the unadjusted price series, P i , is taken to create the price movement series, 100.log P i log P i 1 /. Many authors (see, for example, Solibakke 2002;Higgs and Worthington 2005) have noted systematic calendar (weekends, (moving) holidays and summer/winter), trend and scale effects in both the mean and variance of the system price movements. To adjust for these documented shifts in both the mean and volatility of the raw electricity price series, a two-stage adjustment procedure is carried out, in which systematic and deterministic effects are removed first from the mean and then from the variance .…”
Section: Energy Market Data and Adjustments For Stationaritymentioning
confidence: 99%
“…These models fail to capture the full volatility dynamics of electricity prices as well as the price and volatility interrelationships. Another class of models introduces univariate generalized autoregressive conditional heteroscedasticity (GARCH) conditional volatility models, as well as other variations of GARCH modeling, such as exponential (EGARCH) and threshold (TGARCH) (see Chan and Gray 2006;Escribano et al 2011;Habell et al 2004;Higgs and Worthington 2005;Koopman et al 2007;Solibakke 2002). These models capture the price and volatility dynamics of electricity prices as well as price shock transmissions.…”
Section: The Electricity System Price For the Nordic Spot Electricitymentioning
confidence: 99%
“…At times of high demand during summer and winter months or weekdays as compared to weekends, generators at higher marginal costs are scheduled into the pool. Knittel and Roberts (2001), Lucia and Schwartz (2002), Escribano et al (2002), Guthrie and Videbeck (2002), Hadsell et al (2004), Higgs and Worthington (2005) and Koopman (2007) have included seasonal factors in their studies.…”
Section: The Nature Of Electricity Prices and The Scope Of The Surveymentioning
confidence: 99%
“…The paper uses the TARCH process including asymmetry and month-of-year effects to model the daily spot returns. Higgs and Worthington (2005) investigate the intraday price volatility process in four Australian wholesale electricity markets; namely New South Wales, Queensland, South Australia and Victoria. The data set consists of half-hourly electricity prices and demand volumes over the period 1 January 2002 to 1 June 2003.…”
Section: Arch and Garch Modelsmentioning
confidence: 99%
See 1 more Smart Citation