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Long memory and nonlinearity in conditional variances: A smooth transition FIGARCH model

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  • KIlIç, Rehim
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    Abstract

    This paper introduces the Smooth Transition version of FIGARCH model which is designed to account for both long memory and nonlinear dynamics in the conditional variance. Nonlinearity is introduced via a logistic transition function. The model can capture smooth changes in the volatility across different regimes as well as asymmetric response to negative and positive shocks and allows for nonzero thresholds. Simulations find that the Smooth Transition FIGARCH model outperforms the standard FIGARCH model when nonlinearity is present, and ignoring nonlinearity in the data may induce considerable costs in terms of bias and efficiency. Applications to exchange rate and stock market data show that the proposed model performs well both in-sample fit as well as in forecasting one-day ahead volatility.

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    File URL: http://www.sciencedirect.com/science/article/B6VFG-51N2277-1/2/9eb99f251047afa7303a28d37966d95c
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Empirical Finance.

    Volume (Year): 18 (2011)
    Issue (Month): 2 (March)
    Pages: 368-378

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    Handle: RePEc:eee:empfin:v:18:y:2011:i:2:p:368-378

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    Web page: http://www.elsevier.com/locate/jempfin

    Related research

    Keywords: FIGARCH STFIGARCH Volatility Long memory Smooth Transition Asymmetry;

    References

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    1. Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
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    Cited by:
    1. Stavros Degiannakis & Pamela Dent & Christos Floros, 2014. "A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification," Manchester School, University of Manchester, vol. 82(1), pages 71-102, 01.

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