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

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  • Richard T. Baillie

    ()
    (Michigan State University and Queen Mary, University of London)

  • Claudio Morana

    (Michigan State University, Universit� del Piemonte Orientale and ICER)

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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Bibliographic Info

Paper provided by Queen Mary, University of London, School of Economics and Finance 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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Keywords: FIGARCH ; Long memory; Structural change; Stock market volatility;

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