Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series an...Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.展开更多
This paper establishes probabilistic and statistical properties of the extension of timeinvariant coefficients asymmetric log GARCH processes to periodically time-varying coefficients(P logGARCH)one.In thesemodels,the...This paper establishes probabilistic and statistical properties of the extension of timeinvariant coefficients asymmetric log GARCH processes to periodically time-varying coefficients(P logGARCH)one.In thesemodels,the parameters of log−volatility are allowed to switch periodically between different seasons.The main motivations of this newmodel are able to capture the asymmetry and hence leverage effect,in addition,the volatility coefficients are not a subject to positivity constraints.So,some probabilistic properties of asymmetric P log GARCH models have been obtained,especially,sufficient conditions ensuring the existence of stationary,causal,ergodic(in periodic sense)solution and moments properties are given.Furthermore,we establish the strong consistency and the asymptotic normality of the quasi-maximum likelihood estimator(QMLE)under extremely strong assumptions.Finally,we carry out a simulation study of the performance of the QML and the P log GARCH is applied to model the crude oil prices of Algerian Saharan Blend.展开更多
基金funded by National Natural Science Foundation of China (51577025).
文摘Wind power forecasting is of great significance to the safety, reliability and stability of power grid. In this study, the GARCH type models are employed to explore the asymmetric features of wind power time series and improved forecasting precision. Benchmark Symmetric Curve (BSC) and Asymmetric Curve Index (ACI) are proposed as new asymmetric volatility analytical tool, and several generalized applications are presented. In the case study, the utility of the GARCH-type models in depicting time-varying volatility of wind power time series is demonstrated with the asymmetry effect, verified by the asymmetric parameter estimation. With benefit of the enhanced News Impact Curve (NIC) analysis, the responses in volatility to the magnitude and the sign of shocks are emphasized. The results are all confirmed to be consistent despite varied model specifications. The case study verifies that the models considering the asymmetric effect of volatility benefit the wind power forecasting performance.
文摘This paper establishes probabilistic and statistical properties of the extension of timeinvariant coefficients asymmetric log GARCH processes to periodically time-varying coefficients(P logGARCH)one.In thesemodels,the parameters of log−volatility are allowed to switch periodically between different seasons.The main motivations of this newmodel are able to capture the asymmetry and hence leverage effect,in addition,the volatility coefficients are not a subject to positivity constraints.So,some probabilistic properties of asymmetric P log GARCH models have been obtained,especially,sufficient conditions ensuring the existence of stationary,causal,ergodic(in periodic sense)solution and moments properties are given.Furthermore,we establish the strong consistency and the asymptotic normality of the quasi-maximum likelihood estimator(QMLE)under extremely strong assumptions.Finally,we carry out a simulation study of the performance of the QML and the P log GARCH is applied to model the crude oil prices of Algerian Saharan Blend.
