NDLI: Investigation on the impact of news on volatility in load time series

Content Provider	IEEE Xplore Digital Library
Author	Hao Chen Jie Wu
Copyright Year	2008
Description	Author affiliation: Nanjing Power Supply Co., Nanjing (Hao Chen)
Abstract	Load forecasting is of great importance in power system. With the development of the electricity market, the precision of load forecasting is paid more attention to than ever before. The investigation on characteristics of load time series is beneficial. In this paper, by employing the ARCH type models, the asymmetric characteristics of load series are discussed and an investigation on the impact of news on volatility is represented. Several interesting results are documented. The utility of the ARCH-type models with different empirical conditional distributions in describing time-varying volatility are demonstrated and the reverse leverage effect is reaffirmed by the asymmetric parameters estimation. Owing to the news impact curve (NIC) analysis, the responses in volatility of time series to the sign and the magnitude of the idiosyncratic shocks are highlighted. All the results are mutually confirmed in spite of different model specifications. The empirical results also show the satisfying forecasting ability of the models in view of some standard summery statistics. The asymmetric GARCH models may be a valid and promising method for load time series analysis.
Starting Page	1422
Ending Page	1429
File Size	694999
Page Count	8
File Format	PDF
ISBN	9787900714138
DOI	10.1109/DRPT.2008.4523629
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	DRPT
Subject Keyword	Power system modeling Time series analysis Predictive models Load forecasting Economic forecasting Electrical engineering Electric shock Electricity supply industry Parameter estimation Statistics the News Impact Curve Asymmetric GARCH model GARCH Load Forecasting Reverse Leverage Effect
Content Type	Text
Resource Type	Article

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