Abstract
This work considers the classification of financial nonstationary time series, where the nonstationarity is due to the presence of a deterministic trend. It is evaluated in a high-dimensional context by looking at the first derivative of the trend function and without requiring a pre-specified form. This is achieved by means of a nonparametric estimator which is used in a two stage procedure: the first stage selects the time series with no trend and the second stage focuses the attention on nonlinear trends. A real data application to US Mutual Funds is conducted to demonstrate the validity and applicability of the procedure.
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Feo, G., Giordano, F., Niglio, M., Parrella, M.L. (2022). Financial Time Series Classification by Nonparametric Trend Estimation. In: Corazza, M., Perna, C., Pizzi, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2022. Springer, Cham. https://doi.org/10.1007/978-3-030-99638-3_39
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DOI: https://doi.org/10.1007/978-3-030-99638-3_39
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