Shifts in Individual Parameters of a GARCH Model Articles
- January 2010
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Most asset return series, especially those in high frequency, show high excess kurtosis and persistence in volatility that cannot be adequately described by the generalized conditional heteroscedastic (GARCH) model,
even with heavy-tailed innovations. Many researchers have argued that
these characteristics are due to shifts in volatility that may be
associated with significant economic events such as financial crises.
Indeed, several authors have investigated the case of pure structural
changes, in which all of the parameters in the GARCH model are assumed
to change simultaneously. In this paper, we take an alternative approach
by studying the case in which changes occur in individual parameters of
a GARCH model. We investigate the impacts of such changes on the
underlying return series and its volatility, and propose an iterative
procedure to detect them. In all cases, the changes affect permanently
the level of the volatility, but in some cases, the changes also alter
the dynamic structure of the volatility series. Monte Carlo experiments
are used to investigate the performance of the proposed procedure in
finite samples, and real examples are used to demonstrate the impacts of
detected volatility changes and the efficacy of the proposed procedure.
Practical implications of the parameter changes in financial
applications are also discussed.