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  "Date": "2026-08-13",
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  "Title": "Time-Varying Garch Models Through a State-Space Representation",
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  "Description": "Estimates the time-varying (tv) parameters of the\nGARCH(1,1) model, enabling the modeling of non-stationary\nvolatilities by allowing the model parameters to change\ngradually over time. The estimation and prediction processes\nare facilitated through the application of the Kalman filter\nand state-space equations. This package supports the estimation\nof tv parameters for various deterministic functions, which can\nbe identified through exploratory analysis of different time\nperiods or segments of return data. The methodology is grounded\nin the framework presented by Ferreira et al. (2017)\n<doi:10.1080/00949655.2017.1334778>.",
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