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ARIMA-Modell (Autoregressive Integrated Moving Average)×Extremwerttheorie (EVT)×
FachgebietÖkonometrieFinanzwirtschaft
FamilieRegression modelRegression model
Entstehungsjahr20152001
UrheberBox & Jenkins (Box-Jenkins methodology)Coles (textbook treatment); McNeil, Frey & Embrechts
TypUnivariate time-series modelTail / extreme-event model
Wegweisende QuelleBox, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598
AliasnamenBox-Jenkins model, ARIMA(p,d,q), ARIMA ModeliEVT, generalized extreme value, generalized Pareto distribution, peaks over threshold
Verwandt55
ZusammenfassungARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold.
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ScholarGateMethoden vergleichen: ARIMA · Extreme Value Theory. Abgerufen am 2026-06-19 von https://scholargate.app/de/compare