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Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach Author info | Abstract | Publisher info | Download info | Related research | Statistics Richard T. Baillie
Claudio Morana ()
This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH, or A-FIGARCH, which is designed to account for both long memory and structural change in the conditional variance process. Structural change is modeled by allowing the intercept to follow a slowly varying function, speci?ed by Gallant (1984)'s flexible functional form. A Monte Carlo study ?nds that the A-FIGARCH model outperforms the standard FIGARCH model when structural change is present, and performs at least as well in the absence of structural instability. An empirical application to stock market volatility is also included to illustrate the usefulness of the technique.
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Paper provided by ICER - International Centre for Economic Research in its series ICER Working Papers - Applied Mathematics Series with number
11-2007.
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Length: 26 pages
Date of creation: Mar 2007Date of revision:
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Keywords: FIGARCH ; long memory ; structural change ; stock market volatility. ; Find related papers by JEL classification: C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions G1 - Financial Economics - - General Financial Markets
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Christian Conrad & Menelaos Karanasos & Ning Zeng, 2008.
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Christina Amado & Timo Teräsvirta, 2008.
"Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure ,"
CREATES Research Papers
2008-08, School of Economics and Management, University of Aarhus.
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