Local explosion modelling by non-causal process

The noncausal autoregressive process with heavy-tailed errors possesses a nonlinear causal dynamics, which allows for %unit root, local explosion or asymmetric cycles often observed in economic and financial time series. It provides a new model for multiple local explosions in a strictly stationary framework. The causal predictive distribution displays surprising features, such as the existence of higher moments than for the marginal distribution, or the presence of a unit root in the Cauchy case. Aggregating such models can yield complex dynamics with local and global explosion as well as variation in the rate of explosion. The asymptotic behavior of a vector of sample autocorrelations is studied in a semi-parametric noncausal AR(1) framework with Pareto-like tails, and diagnostic tests are proposed. Empirical results based on the Nasdaq composite price index are provided.(This abstract was borrowed from another version of this item.)