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

Richard T. Baillie and Claudio Morana
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Richard T. Baillie: Michigan State University and Queen Mary, University of London

No 593, Working Papers from Queen Mary University of London, School of Economics and Finance

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.

Keywords: FIGARCH; Long memory; Structural change; Stock market volatility (search for similar items in EconPapers)
JEL-codes: C15 C22 F31 (search for similar items in EconPapers)
Date: 2014-06-30
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Related works:
Journal Article: Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach (2009)
Working Paper: Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:593

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