Integrated OU Processes

In this paper we study the detailed distributional properties of integrated non-Gaussian OU (intOU) processes. Both exact results and approximate results are given. We emphasise the study of the tail behaviour of the intOU process. Our results have many potential applications in financial economics, for OU processes are used as models of instantaneous volatility in stochastic volatility (SV) models. In this case an intOU process can be regarded as a model of integrated volatility. Hence the tail behaviour of the intOU process will determine the tail behaviour of returns generated by SV models.Background driving Levy process; Chronometer; Co-break; Econometrics; Integrated volatility; Kumulant function; Levy density; Option pricing; OU processes; Stochastic volatility

Persistence of integrated stable processes

We compute the persistence exponent of the integral of a stable L\'evy process in terms of its self-similarity and positivity parameters. This solves a problem raised by Z. Shi (2003). Along the way, we investigate the law of the stable process L evaluated at the first time its integral X hits zero, when the bivariate process (X,L) starts from a coordinate axis. This extends classical formulae by McKean (1963) and Gor'kov (1975) for integrated Brownian motion

Gaussian semiparametric estimation of multivariate fractionally integrated processes

This paper analyzes the semiparametric estimation of multivariate long-range dependent processes. The class of spectral densities considered includes multivariate fractionally integrated processes, which are not covered by the existing literature. This paper also establishes the consistency of the multivariate Gaussian semiparametric estimator, which has not been shown in the other works. Asymptotic normality of the multivariate Gaussian semiparametric estimator is also established, and the proposed estimator is shown to have a smaller limiting variance than the two-step Gaussian semiparametric estimator studied by Lobato (1999). Gaussianity is not assumed in the asymptotic theory.

Long Run Covariance Matrices for Fractionally Integrated Processes

An asymptotic expansion is given for the autocovariance matrix of a vector of stationary long-memory processes with memory parameters d satisfying 0Asymptotic expansion, Autocovariance function, Fourier integral, Long memory, Long run variance, Spectral density

The multifaceted behavior of integrated supOU processes: the infinite variance case

The so-called "supOU" processes, namely the superpositions of Ornstein-Uhlenbeck type processes are stationary processes for which one can specify separately the marginal distribution and the dependence structure. They can have finite or infinite variance. We study the limit behavior of integrated infinite variance supOU processes adequately normalized. Depending on the specific circumstances, the limit can be fractional Brownian motion but it can also be a process with infinite variance, a L\'evy stable process with independent increments or a stable process with dependent increments. We show that it is even possible to have infinite variance integrated supOU processes converging to processes whose moments are all finite. A number of examples are provided.Accepted manuscrip