Tail Quantile Estimation of Heteroskedastic Intraday Increases in Peak Electricity Demand

• 作者: Andréhette Verster Caston Sigauke Delson Chikobvu
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: CONDITIONAL Extreme Value Theory Daily Electricity PEAK Demand VOLATILITY TAIL QUANTILES
• 年，卷（期）: 2012,（4）
• 页码范围: 435-442
• 页数: 8页
• 分类号: R73

摘要

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.