It is shown that by aggregating simple random parameters, processes such as autoregressive micro-relationships or Ornstein-Uhlenbeck processes, one can obtain various seasonal long memory Gaussian models. The investigation concerns the discrete as well as the continuous time setting. In both situations the precise asymptotic behaviour of the covariance is studied. The regularity of sample paths is evaluated when possible.

No abstract

Abstract. The EGARCH model of Nelson [29] is one of the most successful ARCH models which may exhibit characteristic asymmetries of financial time series, as well as long memory. The paper studies the covariance structure and dependence properties of the EGARCH and some related stochastic volatility models. We show that the large time behavior of the covariance of powers of the (observed) ARCH process is determined by the behavior of the covariance of the (linear) log-volatility process; in particular, a hyperbolic decay of the later covariance implies a similar hyperbolic decay of the former covariances. We show, in this case, that normalized partial sums of powers of the observed process tend to fractional Brownian motion. The paper also obtains a (functional) CLT for the corresponding partial sums' processes of the EGARCH model with short and moderate memory. These results are applied to study asymptotic behavior of tests for long memory using the R/S statistic.Mathematics Subject Classification. 60F17, 62M10, 91B70, 91B84.

No abstract