459 research outputs found
Large deviations for solutions to stochastic recurrence equations under Kesten's condition
In this paper we prove large deviations results for partial sums constructed
from the solution to a stochastic recurrence equation. We assume Kesten's
condition [Acta Math. 131 (1973) 207-248] under which the solution of the
stochastic recurrence equation has a marginal distribution with power law
tails, while the noise sequence of the equations can have light tails. The
results of the paper are analogs to those obtained by A. V. Nagaev [Theory
Probab. Appl. 14 (1969) 51-64; 193-208] and S. V. Nagaev [Ann. Probab. 7 (1979)
745-789] in the case of partial sums of i.i.d. random variables. In the latter
case, the large deviation probabilities of the partial sums are essentially
determined by the largest step size of the partial sum. For the solution to a
stochastic recurrence equation, the magnitude of the large deviation
probabilities is again given by the tail of the maximum summand, but the exact
asymptotic tail behavior is also influenced by clusters of extreme values, due
to dependencies in the sequence. We apply the large deviation results to study
the asymptotic behavior of the ruin probabilities in the model.Comment: Published in at http://dx.doi.org/10.1214/12-AOP782 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Auto-tail dependence coefficients for stationary solutions of linear stochastic recurrence equations and for GARCH(1,1)
We examine the auto-dependence structure of strictly stationary solutions of linear stochastic recurrence equations and of strictly stationary GARCH(1, 1) processes from the point of view of ordinary and generalized tail dependence coefficients. Since such processes can easily be of infinite variance, a substitute for the usual auto-correlation function is needed
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