Author(s)

Caston Sigauke, Andréhette Verster, Delson Chikobvu

Department of Mathematical Statistics and Actuarial Science University of the Free State, South Africa.
Department of Statistics and Operations Research, University of Limpopo South Africa.

Modelling of intraday increases in peak electricity demand using an autoregressive moving average-exponential generalized autoregressive conditional heteroskedastic-generalized single Pareto (ARMA-EGARCH-GSP) approach is discussed in this paper. The developed model is then used for extreme tail quantile estimation using daily peak electricity demand data from South Africa for the period, years 2000 to 2011. The advantage of this modelling approach lies in its ability to capture conditional heteroskedasticity in the data through the EGARCH framework, while at the same time estimating the extreme tail quantiles through the GSP modelling framework. Empirical results show that the ARMA-EGARCH-GSP model produces more accurate estimates of extreme tails than a pure ARMA-EGARCH model.

Conditional Extreme Value Theory; Daily Electricity Peak Demand; Volatility; Tail Quantiles

C. Sigauke, A. Verster and D. Chikobvu, "Tail Quantile Estimation of Heteroskedastic Intraday Increases in Peak Electricity Demand," Open Journal of Statistics, Vol. 2 No. 4, 2012, pp. 435-442. doi: 10.4236/ojs.2012.24054.