2009-04: Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets (Working paper)

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Author(s)
Higgs, Helen
Griffith University Author(s)
Year published
2009
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This paper examines the inter-relationships of wholesale spot electricity prices among the four regional electricity markets in the Australian National Electricity Market (NEM) -- namely, New South Wales, Queensland, South Australia and Victoria -- using the constant conditional correlation and Tse and Tsui's (2002) and Engle's (2002) dynamic conditional correlation multivariate GARCH models. Tse and Tsui's (2002) dynamic conditional correlation multivariate GARCH model, which takes account of the Student t specification, produces the best results. At the univariate GARCH(1,1) level, the mean equations indicate the presence ...
View more >This paper examines the inter-relationships of wholesale spot electricity prices among the four regional electricity markets in the Australian National Electricity Market (NEM) -- namely, New South Wales, Queensland, South Australia and Victoria -- using the constant conditional correlation and Tse and Tsui's (2002) and Engle's (2002) dynamic conditional correlation multivariate GARCH models. Tse and Tsui's (2002) dynamic conditional correlation multivariate GARCH model, which takes account of the Student t specification, produces the best results. At the univariate GARCH(1,1) level, the mean equations indicate the presence of positive own mean spillovers in all four markets and little evidence of mean spillovers from the other lagged markets. In the dynamic conditional correlation equation, the highest conditional correlations are evident between the well-connected markets indicating the presence of strong interdependence between these markets, with weaker interdependence between the not-so-well-interconnected markets.
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View more >This paper examines the inter-relationships of wholesale spot electricity prices among the four regional electricity markets in the Australian National Electricity Market (NEM) -- namely, New South Wales, Queensland, South Australia and Victoria -- using the constant conditional correlation and Tse and Tsui's (2002) and Engle's (2002) dynamic conditional correlation multivariate GARCH models. Tse and Tsui's (2002) dynamic conditional correlation multivariate GARCH model, which takes account of the Student t specification, produces the best results. At the univariate GARCH(1,1) level, the mean equations indicate the presence of positive own mean spillovers in all four markets and little evidence of mean spillovers from the other lagged markets. In the dynamic conditional correlation equation, the highest conditional correlations are evident between the well-connected markets indicating the presence of strong interdependence between these markets, with weaker interdependence between the not-so-well-interconnected markets.
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Copyright © 2010 by author(s). No part of this paper may be reproduced in any form, or stored in a retrieval system, without prior permission of the author(s).
Note
Economics and Business Statistics
Subject
C51 - Model Construction and Estimation
L94 - Electric Utilities
C32 - Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
Q40 - Energy: General
Wholesale spot electricity price markets
Constant and dynamic conditional correlation
Multivariate GARCH