Helen Higgs
Articles by this Author
Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market.
- By Helen Higgs
- Published 08/7/2009
- Price modeling
-
Rating:
It is commonly known that wholesale spot electricity markets exhibit
high price volatility, strong mean-reversion and frequent extreme
price spikes. This paper employs a basic stochastic model, a mean-reverting
model and a regime-switching model to capture these
features in the Australian national electricity market (NEM),
comprising the interconnected markets of New South Wales,
Queensland, South Australia and Victoria. Daily spot prices from 1
January 1999 to 31 December 2004 are employed. The results show
that the regime-switching model outperforms the basic stochastic and
mean-reverting models. Electricity prices are also found to exhibit
stronger mean-reversion after a price spike than in the normal period,
and price volatility is more than fourteen times higher in spike periods
than in normal periods. The probability of a spike on any given day
ranges between 5.16% in NSW and 9.44% in Victoria.
Modeling price and volatility inter- relationships in the Australian wholesale spot electricity markets
- By Helen Higgs
- Published 08/7/2009
- Price modeling
- Unrated
Published in: Energy Economics
Publication year: 2009
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 (Tse, Y.K., Tsui, A.K.C., 2002. A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20 (3), 351–362.) and Engle's (Engle, R.,2002. Dynamic conditional correlation: a sample class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20 (3), 339–350.) dynamic conditional
correlation multivariate GARCH models. Tse and Tsui's (Tse, Y.K., Tsui, A.K.C., 2002. A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20 (3), 351–362.) 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 fourmarkets 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 wellinterconnected markets.
