Package: ExtremeRisks 0.0.5

ExtremeRisks: Extreme Risk Measures

A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2022) <doi:10.3150/21-BEJ1375>, Davison et al. (2023) <doi:10.1080/07350015.2022.2078332>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>, Padoan and Rizzelli (2024) <doi:10.3150/23-BEJ1668>, Daouia et al. (2024) <doi:10.3150/23-BEJ1632>.

Authors:Simone Padoan [cre, aut], Gilles Stupfler [aut], Carlotta Pacifici [aut]

ExtremeRisks_0.0.5.tar.gz
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ExtremeRisks.pdf |ExtremeRisks.html✨
ExtremeRisks/json (API)

# Install 'ExtremeRisks' in R:
install.packages('ExtremeRisks', repos = c('https://simonepadoan.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • dowjones - Negative log-returns of DOW JONES.
  • sp500 - Negative log-returns of S&P 500.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.30 score 1 stars 6 scripts 466 downloads 34 exports 20 dependencies

Last updated from:4b85f16f9a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK150
source / vignettesOK163
linux-release-x86_64OK182
macos-release-arm64OK199
macos-oldrel-arm64OK175
windows-develOK131
windows-releaseOK125
windows-oldrelOK93
wasm-releaseOK101

Exports:Bqgpd_cBqgpd_dcpost_statEBTailIndexestExpectilesestExtLevelestMultiExpectilesestPOTexpectilesExpectMESextBQuantextBQuantxextMultiQuantileextQuantilefitdGPDHTailIndexHypoTestingMLTailIndexMomTailIndexMultiHTailIndexplotBayespredDenspredDensxpredExpectilespredMultiExpectilespredQuantquantFQuantMESrbtimeseriesrmdatartimeseriesscedastic.testschedastic.testtestTailHomo

Dependencies:ADGofTestclustercolorspacecopulaevdgmmgsllatticeMatrixmisc3dmvtnormnumDerivpcaPPplot3Dpracmapsplinesandwichstabledisttmvtnormzoo

Readme and manuals

Help Manual

Help pageTopics
Estimation of the scedasis functioncpost_stat
Negative log-returns of DOW JONES.dowjones
Expectile Based Tail Index EstimationEBTailIndex
High Expectile EstimationestExpectiles
Extreme Level EstimationestExtLevel
Multidimensional High Expectile EstimationestMultiExpectiles
Estimation of generalized Pareto distributionsestPOT
Expectile Computationexpectiles
Marginal Expected Shortfall Expectile Based EstimationExpectMES
Bayesian extreme quantileextBQuant
Conditional Bayesian extreme quantileextBQuantx
Multidimensional Value-at-Risk (VaR) or Extreme Quantile (EQ) EstimationextMultiQuantile
Value-at-Risk (VaR) or Extreme Quantile (EQ) EstimationextQuantile
Maximum likelihood estimation of the parameters of the discrete generalized Pareto distributionfitdGPD
Hill Tail Index EstimationHTailIndex
Wald-Type Hypothesis TestingHypoTesting
Maximum Likelihood Tail Index EstimationMLTailIndex
Moment based Tail Index EstimationMomTailIndex
Multidimensional Hill Tail Index EstimationMultiHTailIndex
Plot empirical Bayes inference results for continuous and discrete generalized Pareto distributionplotBayes
Predictive posterior density of peak-over-threshold modelspredDens
Conditional predictive posterior density of peaks-over-threshold modelspredDensx
Extreme Expectile EstimationpredExpectiles
Multidimensional Extreme Expectile EstimationpredMultiExpectiles
Predictive quantile based on the generalized Pareto modelpredQuant
Marginal Expected Shortfall Quantile Based EstimationQuantMES
Simulation of Two-Dimensional Temporally Dependent Observationsrbtimeseries
Simulation of d-Dimensional Temporally Independent Observationsrmdata
Simulation of One-Dimensional Temporally Dependent Observationsrtimeseries
Test on the effect of concomitant covariate on the extremes of the response variablescedastic.test
Negative log-returns of S&P 500.sp500
Test on tail homogeneitytestTailHomo