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Department of Economics, University of Western Ontario, Social Science Centre Rm 4064, London, N6A5C2, Canada

Academic Editor: Sheri Markose

Abstract We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow outperforming other benchmark models, such as linear heterogeneous autoregressive model and GARCH specifications. Finally, we show how to derive closed-form expression for multiple-step-ahead forecasting by exploiting information about the conditional distribution of returns. View Full-Text

Keywords: volatility forecast; non-linear time series models volatility forecast; non-linear time series models

Autor: Sergii Pypko

Fuente: http://mdpi.com/


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