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• W4303444764 abstract "Abstract In functional time series analysis, the functional autocorrelation function (fACF) plays an important role in revealing the temporal dependence structures underlying the dynamics and identifying the lags at which substantial correlation exists. However, akin to its counterpart in the univariate case, the fACF is restricted by linear structure and can be misleading in reflecting nonlinear temporal dependence. This paper proposes a nonlinear alternative to the fACF for analyzing the temporal dependence in functional time series. We consider linear and nonlinear data generating processes: a functional autoregressive process and a functional generalized autoregressive conditional heteroskedasticity process. We demonstrate that when the process exhibits linear temporal structures, the inference obtained from our proposed nonlinear fACF is consistent with that from the fACF. When the underlying process exhibits nonlinear temporal dependence, our nonlinear fACF has a superior capability in uncovering the nonlinear structure that the fACF misleads. An empirical data analysis highlights its applications in unveiling nonlinear temporal structures in the daily curves of the intraday volatility dynamics of the foreign exchange rate." @default.
• W4303444764 created "2022-10-07" @default.
• W4303444764 creator A5027781244 @default.
• W4303444764 creator A5087947210 @default.
• W4303444764 date "2022-10-07" @default.
• W4303444764 modified "2023-09-26" @default.
• W4303444764 title "Nonlinear autocorrelation function of functional time series" @default.
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• W4303444764 doi "https://doi.org/10.1007/s11071-022-07927-0" @default.
• W4303444764 hasPublicationYear "2022" @default.
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