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J. Risk Financial Manag. 2015, 8(3), 311-336;

Volatility Forecast in Crises and Expansions

Department of Economics, University of Western Ontario, Social Science Centre Rm 4064, London, N6A5C2, Canada
Academic Editor: Sheri Markose
Received: 26 February 2015 / Revised: 30 May 2015 / Accepted: 6 July 2015 / Published: 5 August 2015
(This article belongs to the Special Issue Financial Risk Modeling and Forecasting)
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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

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Pypko, S. Volatility Forecast in Crises and Expansions. J. Risk Financial Manag. 2015, 8, 311-336.

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