4.1 Article

On some nonstationary, nonlinear random processes and their stationary approximations

Journal

ADVANCES IN APPLIED PROBABILITY
Volume 38, Issue 4, Pages 1155-1172

Publisher

APPLIED PROBABILITY TRUST
DOI: 10.1017/S000186780000149X

Keywords

derivative process; local stationarity; nonlinearity; nonstationarity; state space model

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In this paper our object is to show that a certain class of nonstationary random processes can locally be approximated by stationary processes. The class of processes we are considering includes the time-varying autoregressive conditional heteroscedastic and generalised autoregressive conditional heteroscedastic processes, amongst others. The measure of deviation from stationarity can be expressed as a function of a derivative random process. This derivative process inherits many properties common to stationary processes. We also show that the derivative processes obtained here have alpha-mixing properties.

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