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Estimation for Nonnegative Lévy-Driven Ornstein-Uhlenbeck Processes

Part of: Inference from stochastic processes Stochastic analysis

Published online by Cambridge University Press:  14 July 2016

Peter J. Brockwell ,
Richard A. Davis  and
Yu Yang
Peter J. Brockwell*
Affiliation:
Colorado State University
Richard A. Davis*
Affiliation:
Columbia University
Yu Yang*
Affiliation:
Colorado State University
*
∗ Postal address: Department of Statistics, Colorado State University, Fort Collins, CO 80523-1877, USA.
∗∗∗ Postal address: Department of Statistics, Columbia University, New York, NY 10027, USA.
∗ Postal address: Department of Statistics, Colorado State University, Fort Collins, CO 80523-1877, USA.
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Abstract

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Continuous-time autoregressive moving average (CARMA) processes with a nonnegative kernel and driven by a nondecreasing Lévy process constitute a very general class of stationary, nonnegative continuous-time processes. In financial econometrics a stationary Ornstein-Uhlenbeck (or CAR(1)) process, driven by a nondecreasing Lévy process, was introduced by Barndorff-Nielsen and Shephard (2001) as a model for stochastic volatility to allow for a wide variety of possible marginal distributions and the possibility of jumps. For such processes, we take advantage of the nonnegativity of the increments of the driving Lévy process to study the properties of a highly efficient estimation procedure for the parameters when observations are available of the CAR(1) process at uniformly spaced times 0,h,…,Nh. We also show how to reconstruct the background driving Lévy process from a continuously observed realization of the process and use this result to estimate the increments of the Lévy process itself when h is small. Asymptotic properties of the coefficient estimator are derived and the results illustrated using a simulated gamma-driven Ornstein-Uhlenbeck process.

Keywords

Continuous-time autoregressionOrnstein-Uhlenbeck processLevy processstochastic differential equationsampled process

MSC classification

Secondary: 62M10: Time series, auto-correlation, regression, etc. 60H10: Stochastic ordinary differential equations 62M09: Non-Markovian processes

Information

Type
Research Papers
Information
Journal of Applied Probability , Volume 44 , Issue 4 , December 2007 , pp. 977 - 989
Copyright
Copyright © Applied Probability Trust 2007 

References

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