376 research outputs found
A functional limit theorem for dependent sequences with infinite variance stable limits
Under an appropriate regular variation condition, the affinely normalized
partial sums of a sequence of independent and identically distributed random
variables converges weakly to a non-Gaussian stable random variable. A
functional version of this is known to be true as well, the limit process being
a stable L\'{e}vy process. The main result in the paper is that for a
stationary, regularly varying sequence for which clusters of high-threshold
excesses can be broken down into asymptotically independent blocks, the
properly centered partial sum process still converges to a stable L\'{e}vy
process. Due to clustering, the L\'{e}vy triple of the limit process can be
different from the one in the independent case. The convergence takes place in
the space of c\`{a}dl\`{a}g functions endowed with Skorohod's topology,
the more usual topology being inappropriate as the partial sum processes
may exhibit rapid successions of jumps within temporal clusters of large
values, collapsing in the limit to a single jump. The result rests on a new
limit theorem for point processes which is of independent interest. The theory
is applied to moving average processes, squared GARCH(1,1) processes and
stochastic volatility models.Comment: Published in at http://dx.doi.org/10.1214/11-AOP669 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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