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Characteristic function estimation of non-Gaussian Ornstein-Uhlenbeck processes

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  • Emanuele Taufer

    ()
    (DISA, Faculty of Economics, Trento University)

Abstract

Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given their flexibility in modelling stylized features of financial series such as asymmetry, heavy tails and jumps. The use of non-Gaussian marginal distributions makes likelihood analysis of these processes unfeasible for virtually all cases of interest. This paper exploits the self-decomposability of the marginal laws of OU processes to provide explicit expressions of the characteristic function which can be applied to several models as well as to develop e±cient estimation techniques based on the empirical characteristic function. Extensions to OU-based stochastic volatility models are provided.

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Bibliographic Info

Paper provided by Department of Computer and Management Sciences, University of Trento, Italy in its series DISA Working Papers with number 0805.

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Length: 20 pages
Date of creation: Jul 2008
Date of revision: 07 Jul 2008
Handle: RePEc:trt:disawp:0805

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Related research

Keywords: Ornstein-Uhlenbeck process; Lévy process; self-decomposable distribution; characteristic function; estimation;

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References

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  1. Jiang, George J & Knight, John L, 2002. "Estimation of Continuous-Time Processes via the Empirical Characteristic Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 198-212, April.
  2. Matthew P. S. Gander & David A. Stephens, 2007. "Simulation and inference for stochastic volatility models driven by Lévy processes," Biometrika, Biometrika Trust, vol. 94(3), pages 627-646.
  3. Knight, John L. & Satchell, Stephen E., 1997. "The Cumulant Generating Function Estimation Method," Econometric Theory, Cambridge University Press, vol. 13(02), pages 170-184, April.
  4. Knight, John L. & Yu, Jun, 2002. "Empirical Characteristic Function In Time Series Estimation," Econometric Theory, Cambridge University Press, vol. 18(03), pages 691-721, June.
  5. Taufer, Emanuele & Leonenko, Nikolai, 2009. "Simulation of Lvy-driven Ornstein-Uhlenbeck processes with given marginal distribution," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2427-2437, April.
  6. Woerner, Jeannette H.C., 2004. "Estimating The Skewness In Discretely Observed L Vy Processes," Econometric Theory, Cambridge University Press, vol. 20(05), pages 927-942, October.
  7. Pap, Gyula & van Zuijlen, Martien C. A., 1996. "Parameter Estimation with Exact Distribution for Multidimensional Ornstein-Uhlenbeck Processes," Journal of Multivariate Analysis, Elsevier, vol. 59(2), pages 153-165, November.
  8. Ole E. Barndorff-Nielsen & Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280.
  9. Madan, Dilip B & Seneta, Eugene, 1990. "The Variance Gamma (V.G.) Model for Share Market Returns," The Journal of Business, University of Chicago Press, vol. 63(4), pages 511-24, October.
  10. Sucharita Ghosh & Jan Beran, 2006. "On Estimating the Cumulant Generating Function of Linear Processes," Annals of the Institute of Statistical Mathematics, Springer, vol. 58(1), pages 53-71, March.
  11. Griffin, J.E. & Steel, M.F.J., 2006. "Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility," Journal of Econometrics, Elsevier, vol. 134(2), pages 605-644, October.
  12. John L. Knight & Stephen E. Satchell & Jun Yu, 2002. "Estimation of the Stochastic Volatility Model by the Empirical Characteristic Function Method," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 44(3), pages 319-335, 09.
  13. Geurt Jongbloed & Frank H. Van Der Meulen, 2006. "Parametric Estimation for Subordinators and Induced OU Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 33(4), pages 825-847.
  14. Gareth O. Roberts & Omiros Papaspiliopoulos & Petros Dellaportas, 2004. "Bayesian inference for non-Gaussian Ornstein-Uhlenbeck stochastic volatility processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 369-393.
  15. Ole E. Barndorff-Nielsen, 2003. "Integrated OU Processes and Non-Gaussian OU-based Stochastic Volatility Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 30(2), pages 277-295.
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Citations

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Cited by:
  1. Francq, Christian & Meintanis, Simos, 2012. "Fourier--type estimation of the power garch model with stable--paretian innovations," MPRA Paper 41667, University Library of Munich, Germany.
  2. Nikolai Leonenko & EStuart Petherick & Emanuele Taufer, 2012. "Multifractal Scaling for Risky Asset Modelling," DISA Working Papers 2012/07, Department of Computer and Management Sciences, University of Trento, Italy, revised Jul 2012.
  3. Shibin Zhang & Xinsheng Zhang, 2013. "A least squares estimator for discretely observed Ornstein–Uhlenbeck processes driven by symmetric α-stable motions," Annals of the Institute of Statistical Mathematics, Springer, vol. 65(1), pages 89-103, February.

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