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Modeling Long Memory and Structural Breaks in Conditional Variances: An Adaptive FIGARCH Approach

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Author Info
Richard T. Baillie () (Michigan State University and Queen Mary, University of London)
Claudio Morana (Michigan State University, Università del Piemonte Orientale and ICER)

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Abstract

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, specified by Gallant (1984)'s flexible functional form. A Monte Carlo study finds 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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Publisher Info
Paper provided by Queen Mary, University of London, Department of Economics in its series Working Papers with number 593.

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Date of creation: Mar 2007
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Handle: RePEc:qmw:qmwecw:wp593

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Related research
Keywords: FIGARCH>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
F31 - International Economics - - International Finance - - - Foreign Exchange

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This page was last updated on 2008-9-2.


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