Extremes of Nonlinear Time Series

Kamil Feridun Turkman, Manuel González Scotto and Patrícia de Zea Bermudez
Additional contact information
Kamil Feridun Turkman: Faculdade de Ciências Universidade de Lisboa, Departmento de Estatística e Investigação Operacional
Manuel González Scotto: Universidade de Aveiro, Departamento de Matemática
Patrícia de Zea Bermudez: Faculdade de Ciências Universidade de Lisboa, Departmento de Estatística e Investigação Operacional

Chapter Chapter 3 in Non-Linear Time Series, 2014, pp 91-120 from Springer

Abstract: Abstract We have seen in Sect. 2.1.4 that nonlinear processes, due to their dependence on initial conditions, often magnify error causing unstable behavior. Even when stationary solutions exist, this noise magnification and dependence on initial conditions reflects on the tails of the stationary distribution, as well as on how large values cluster.

Keywords: Nonlinear Process; Heavy Tail; Tail Dependence; Tail Index; Gumbel Distribution (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-07028-5_3

Ordering information: This item can be ordered from
http://www.springer.com/9783319070285

DOI: 10.1007/978-3-319-07028-5_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().