Department of Economics and Business Economics

The multivariate supOU stochastic volatility model

Research output: Working paperResearch


  • Rp09 42

    Final published version, 350 KB, PDF document

  • Ole Barndorff-Nielsen
  • Robert Stelzer, TUM Institute for Advanced Study & Zentrum Mathematik, Technische Universität München, Germany
  • School of Economics and Management
Using positive semidefinite supOU (superposition of Ornstein-Uhlenbeck type) processes
to describe the volatility, we introduce a multivariate stochastic volatility model
for financial data which is capable of modelling long range dependence effects.
The finiteness of moments and the second order structure of the volatility, the log returns,
as well as their "squares" are discussed in detail. Moreover, we give several examples
in which long memory effects occur and study how the model as well as the
simple Ornstein-Uhlenbeck type stochastic volatility model behave under linear transformations.
In particular, the models are shown to be preserved under invertible linear
transformations. Finally, we discuss how (sup)OU stochastic volatility models can be
combined with a factor modelling approach.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages21
Publication statusPublished - 2009

    Research areas

  • factor modelling, Lévy bases, linear transformations, long memory, Ornstein-Uhlenbeck type process, second order moment structure, stochastic volatility

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