Time series of extremes

Brian J. Reich, Benjamin Adam Shaby

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Data collected for the purpose of an extreme value analysis very often exhibit temporal dependence. Examples include daily time series of stock prices, temperature, or water levels. In this chapter, we address two fundamental inferential objectives: estimating extreme marginal quantiles while accounting for serial dependence, and estimating the strength of serial dependence in extreme values. We review the literature for methods aimed at each of these objectives, including a survey of computational methods used for each approach. We illustrate the methods using an analysis of hourly wind gust speeds. R code to implement the methods is available online.

Original languageEnglish (US)
Title of host publicationExtreme Value Modeling and Risk Analysis
Subtitle of host publicationMethods and Applications
PublisherCRC Press
Pages131-152
Number of pages22
ISBN (Electronic)9781498701310
ISBN (Print)9781498701297
StatePublished - Jan 6 2016

Fingerprint

Serial Dependence
Extremes
Time series
Extreme Values
Stock Prices
Wind Speed
Quantile
Computational Methods
Water
Review

All Science Journal Classification (ASJC) codes

  • Mathematics(all)

Cite this

Reich, B. J., & Shaby, B. A. (2016). Time series of extremes. In Extreme Value Modeling and Risk Analysis: Methods and Applications (pp. 131-152). CRC Press.
Reich, Brian J. ; Shaby, Benjamin Adam. / Time series of extremes. Extreme Value Modeling and Risk Analysis: Methods and Applications. CRC Press, 2016. pp. 131-152
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Reich, BJ & Shaby, BA 2016, Time series of extremes. in Extreme Value Modeling and Risk Analysis: Methods and Applications. CRC Press, pp. 131-152.

Time series of extremes. / Reich, Brian J.; Shaby, Benjamin Adam.

Extreme Value Modeling and Risk Analysis: Methods and Applications. CRC Press, 2016. p. 131-152.

Research output: Chapter in Book/Report/Conference proceedingChapter

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Reich BJ, Shaby BA. Time series of extremes. In Extreme Value Modeling and Risk Analysis: Methods and Applications. CRC Press. 2016. p. 131-152