摘要：We develop a practical implementation of the test proposed in
Berkes, Horv¡äath, Kokoszka, and Shao (2006) designed to distinguish between
a change-point model and a long memory model. Our implementation
is calibrated to distinguish between a shift in volatility of returns and
long memory in squared returns. It uses a kernel estimator of the long-run
variance of squared returns with the maximal lag selected by a data driven
procedure which depends on the sample size, the location of the estimated
change point and the direction of the apparent volatility shift (increase versus
decrease). In a simulations study, we also consider other long-run variance
estimators, including the VARHAC estimator, but we find that they lead to
tests with inferior performance. Applied to returns on indexes and individual
stocks, our test indicates that even for the same asset, a change-point model
may be preferable for a certain period of time, whereas there is evidence of
long memory in another period of time. Generally there is stronger evidence
for long memory in the eight years ending June 2006 than in the eight years
starting January 1992. This pattern is most pronounced for US stock indexes
and shares in the US financial sector.
关键词：Change-Point, GARCH, Long Memory, Volatility of Returns.
