Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415530 | Computational Statistics & Data Analysis | 2007 | 13 Pages |
Abstract
The class of fractionally integrated generalised autoregressive conditional heteroskedastic (FIGARCH) models is extended for modelling the periodic long-range dependence typically shown by volatility of most intra-daily financial returns. The proposed class of models introduces generalised periodic long-memory filters, based on Gegenbauer polynomials, into the equation describing the time-varying volatility of standard GARCH models. A fitting procedure is illustrated and its performance is evaluated by means of Monte Carlo simulations. The effectiveness of these models in describing periodic long-memory volatility patterns is shown through an empirical application to the Euro–Dollar intra-daily exchange rate.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Silvano Bordignon, Massimiliano Caporin, Francesco Lisi,