Modeling Long Memory and Structural Breaks in Conditional Variances: An Adaptive FIGARCH Approach
AbstractThis 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 InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 593.
Date of creation: Mar 2007
Date of revision:
FIGARCH ; Long memory; Structural change; Stock market volatility;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- F31 - International Economics - - International Finance - - - Foreign Exchange
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-03-24 (All new papers)
- NEP-ECM-2007-03-24 (Econometrics)
- NEP-ETS-2007-03-24 (Econometric Time Series)
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