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Innovation Processes in Logically Constrained Time Series

In: Advances in Directional and Linear Statistics

Author

Listed:
  • Christoph Möller

    (University of Karlsruhe and KIT, Department of Statistics, Econometrics and Mathematical Finance, School of Economicsand Business Engineering)

  • Svetlozar T. Rachev
  • Young S. Kim
  • Frank J. Fabozzi

Abstract

Capturing the relevant aspects of phenomena in an econometric model is a fine art. When it comes to the innovation process a trade of between a suitable process and its mathematical implications has to be found. In many phenomena the likelihood of extreme events plays a crucial role. At the same time, classical extreme value theory is based on assumptions that cannot logically be drawn for the phenomenon in question. In this paper, we exemplify the fitness of tempered stable laws to capture both the probability of extreme events, and the relevant boundary conditions in a back-coupled system, the German balancing energy demand.

Suggested Citation

  • Christoph Möller & Svetlozar T. Rachev & Young S. Kim & Frank J. Fabozzi, 2011. "Innovation Processes in Logically Constrained Time Series," Springer Books, in: Martin T. Wells & Ashis SenGupta (ed.), Advances in Directional and Linear Statistics, chapter 0, pages 173-188, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2628-9_12
    DOI: 10.1007/978-3-7908-2628-9_12
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