Package: fExtremes 4032.84

Paul J. Northrop

fExtremes: Rmetrics - Modelling Extreme Events in Finance

Provides functions for analysing and modelling extreme events in financial time Series. The topics include: (i) data pre-processing, (ii) explorative data analysis, (iii) peak over threshold modelling, (iv) block maxima modelling, (v) estimation of VaR and CVaR, and (vi) the computation of the extreme index.

Authors:Diethelm Wuertz [aut], Tobias Setz [aut], Yohan Chalabi [aut], Paul J. Northrop [cre, ctb]

fExtremes_4032.84.tar.gz
fExtremes_4032.84.zip(r-4.5)fExtremes_4032.84.zip(r-4.4)fExtremes_4032.84.zip(r-4.3)
fExtremes_4032.84.tgz(r-4.4-any)fExtremes_4032.84.tgz(r-4.3-any)
fExtremes_4032.84.tar.gz(r-4.5-noble)fExtremes_4032.84.tar.gz(r-4.4-noble)
fExtremes_4032.84.tgz(r-4.4-emscripten)fExtremes_4032.84.tgz(r-4.3-emscripten)
fExtremes.pdf |fExtremes.html
fExtremes/json (API)
NEWS

# Install 'fExtremes' in R:
install.packages('fExtremes', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://r-forge.r-project.org/projects/rmetrics

Datasets:

On CRAN:

7.38 score 1 stars 4 packages 118 scripts 1.1k downloads 1 mentions 67 exports 15 dependencies

Last updated 28 days agofrom:d12c4f133f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 24 2024
R-4.5-winOKOct 24 2024
R-4.5-linuxOKOct 24 2024
R-4.4-winOKOct 24 2024
R-4.4-macOKOct 24 2024
R-4.3-winOKOct 24 2024
R-4.3-macOKOct 24 2024

Exports:.gevmleFit.gevpwmFit.gevrlevelLLH.gummleFit.gumpwmFitblockMaximablockThetaclusterThetaCVaRdeClusterdgevdgpdemdPlotexindexesPlotexindexPlotferrosegersThetafindThresholdgevFitgevMomentsgevrlevelPlotgevSimgevSliderghMeanExcessFitghtMeanExcessFitgpdFitgpdMomentsgpdQPlotgpdQuantPlotgpdRiskMeasuresgpdSfallPlotgpdShapePlotgpdSimgpdSlidergpdTailPlotgumbelFitgumbelSimhillPlothypMeanExcessFitlilPlotmePlotmrlPlotmsratioPlotmxfPlotnigMeanExcessFitnormMeanExcessFitpgevpgpdpointProcessqgevqgpdqqparetoPlotrecordsPlotrgevrgpdrunThetashaparmDEHaanshaparmHillshaparmPickandsshaparmPlotsllnPlotssrecordsPlottailPlottailRisktailSliderthetaSimVaRxacfPlot

Dependencies:cvarfastICAfBasicsfGarchgbutilsgsslatticeMASSMatrixrbibutilsRdpackspatialstabledisttimeDatetimeSeries

Readme and manuals

Help Manual

Help pageTopics
Modelling Extreme Events in FinancefExtremes-package fExtremes
Extremes Data PreprocessingblockMaxima DataPreprocessing deCluster findThreshold pointProcess
Extremal Index EstimationblockTheta clusterTheta exindexesPlot exindexPlot ExtremeIndex ferrosegersTheta fTHETA fTHETA-class runTheta show,fTHETA-method thetaSim
Explorative Data AnalysisemdPlot ExtremesData ghMeanExcessFit ghtMeanExcessFit hypMeanExcessFit lilPlot mePlot mrlPlot msratioPlot mxfPlot nigMeanExcessFit normMeanExcessFit qqparetoPlot recordsPlot sllnPlot ssrecordsPlot xacfPlot
Generalized Extreme Value Distributiondgev GevDistribution gevMoments gevSlider pgev qgev rgev
Generalized Extreme Value ModellingGevMdaEstimation hillPlot shaparmDEHaan shaparmHill shaparmPickands shaparmPlot
Generalized Extreme Value ModellingfGEVFIT fGEVFIT-class gevFit GevModelling gevSim gumbelFit gumbelSim plot.fGEVFIT show,fGEVFIT-method summary.fGEVFIT
Generalized Extreme Value ModellingGevRisk gevrlevelPlot
Generalized Pareto Distributiondgpd GpdDistribution gpdMoments gpdSlider pgpd qgpd rgpd
GPD Distributions for Extreme Value TheoryfGPDFIT fGPDFIT-class gpdFit GpdModelling gpdSim plot.fGPDFIT show,fGPDFIT-method summary.fGPDFIT
GPD Distributions for Extreme Value TheorygpdQPlot gpdQuantPlot gpdRisk gpdRiskMeasures gpdSfallPlot gpdShapePlot gpdTailPlot tailPlot tailRisk tailSlider
Time Series Data SetsbmwRet danishClaims TimeSeriesData
Value-at-RiskCVaR ValueAtRisk VaR