Modelling long memory and structural breaks in conditional variances: An adaptive FIGARCH approach

被引：109

作者：

Baillie, Richard T. ^{[2
,3
,4
]}

Morana, Claudio ^{[1
,5
,6
]}

机构：

[1] Univ Piemonte Orientale, Dipartimento Sci Econ & Metodi Quantitat, Novara, Italy

[2] Michigan State Univ, Dept Econ, E Lansing, MI 48824 USA

[3] Michigan State Univ, Dept Finance, Broad Sch Business, E Lansing, MI 48824 USA

[4] Queen Mary Univ London, Dept Econ, London E1 4NS, England

[5] ICER, Turin, Italy

[6] Ctr Res Pens & Welf Policies, Turin, Italy

来源：

JOURNAL OF ECONOMIC DYNAMICS & CONTROL | 2009年 / 33卷 / 08期

关键词：

FIGARCH; Long memory; Structural change; Stock market volatility; GARCH; VOLATILITY; INFERENCE;

D O I：

10.1016/j.jedc.2009.02.009

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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 the smooth flexible functional form due to Gallant (1984. The Fourier flexible form. American journal of Agricultural Economics 66, 204-208). 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. Ail empirical application to stock market volatility is also included to illustrate the usefulness of the technique. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：1577 / 1592

页数：16

共 51 条

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