Package: qrmtools 0.0-19

qrmtools: Tools for Quantitative Risk Management

Functions and data sets for reproducing selected results from the book "Quantitative Risk Management: Concepts, Techniques and Tools". Furthermore, new developments and auxiliary functions for Quantitative Risk Management practice.

Authors:Marius Hofert [aut, cre], Kurt Hornik [aut], Alexander J. McNeil [aut]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
qrmtools/json (API)

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

On CRAN:

Conda:

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

4.28 score 1 stars 316 scripts 572 downloads 80 exports 40 dependencies

Last updated from:6f62184eb6. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK181
linux-devel-x86_64OK175
source / vignettesOK250
linux-release-arm64OK199
linux-release-x86_64OK223
macos-release-arm64OK138
macos-release-x86_64OK296
macos-oldrel-arm64OK111
macos-oldrel-x86_64OK288
windows-develOK130
windows-releaseOK128
windows-oldrelOK157
wasm-releaseOK187

Exports:ABRAalloc_ellipalloc_npARABlack_ScholesBlack_Scholes_Greeksblock_rearrangecatchconditioningcrude_VaR_boundsdeBrowningdGEVdGPDdGPDtaildPardual_boundedf_ploteqf_plotES_GPDES_GPDtailES_npES_ParES_tES_t01fit_ARMA_GARCHfit_GARCH_11fit_GEV_MLEfit_GEV_PWMfit_GEV_quantilefit_GPD_MLEfit_GPD_MOMfit_GPD_PWMget_dataGEV_shape_plotgEXGPD_shape_plotgVaRhierarchical_matrixHill_estimatorHill_plotlogLik_GEVlogLik_GPDmaha2_testmardia_testmatrix_density_plotmatrix_plotmean_excess_GPDmean_excess_npmean_excess_plotNA_plotpGEVpGPDpGPDtailpp_plotpParqGEVqGPDqGPDtailqParqq_plotRArBrownianrearrangereturnsreturns_qrmtoolsrGEVrGPDrGPDtailrParRVaR_npstep_plottail_index_GARCH_11tail_plotVaR_bounds_homVaR_GPDVaR_GPDtailVaR_npVaR_ParVaR_tVaR_t01

Dependencies:ADGofTestchroncodetoolscurldigestDistributionUtilsFNNfracdifffuturefuture.applyGeneralizedHyperbolicglobalsjsonlitekernlabKernSmoothkslatticelistenvMASSMatrixmclustmgcvmulticoolmvtnormnlmenloptrnumDerivparallellypracmaquantmodRcppRcppArmadilloRsolnprugarchSkewHyperbolicspdtruncnormTTRxtszoo

Fitting and Predicting VaR based on an ARMA-GARCH Process
1 Simulate (-log-return) data $(X_t)$ from an ARMA-GARCH process | 2 Fit an ARMA-GARCH model to the (simulated) data | 3 Calculate the VaR time series | 4 Backtest VaR estimates | 5 Predict VaR based on fitted model | 6 Simulate future trajectories of $(X_t)$ and compute corresponding VaRs | 7 Plot
Rendered fromARMA_GARCH_VaR.Rmdusingknitr::rmarkdown

Last update: 2022-05-31
Started: 2015-11-10

Geometric Risk Measures
1 Geometric VaR and expectile for two sets of confidence levels | 2 Bootstrapped geometric expectiles | 3 Comparison of geometric VaR and expectile for a given direction
Rendered fromgeometric_risk_measures.Rmdusingknitr::rmarkdown

Last update: 2020-01-13
Started: 2017-06-16

Worst Value-at-Risk under Known Margins
1 Homogeneous case | 1.1 Checks for method = "dual" | 1.2 Checks for method = "Wang"/"Wang.Par" | 1.2.1 Check of auxiliary functions with numerical integration (for $\theta = 2$) | 1.2.2 Check of $h(c)$ without numerical integration (for a range of $\theta$) | 1.3 Compute best/worst $\mathrm{VaR}_\alpha$ (via "Wang.Par") | 1.4 Comparison between various methods for computing worst value-at-risk | 2 Inhomogeneous case | 2.1 A motivation for (column) rearrangements | 2.2 Run-time comparison (straightforward vs efficient implementation) | 2.3 How rearrange() acts on specific matrices | 2.4 Convergence | 2.5 A real data application | 2.6 Worst VaR copula samples
Rendered fromVaR_bounds.Rmdusingknitr::rmarkdown

Last update: 2020-01-13
Started: 2015-11-10

Readme and manuals

Help Manual

Help pageTopics
Computing allocationsalloc_ellip alloc_np conditioning
Black-Scholes formula and the GreeksBlack_Scholes Black_Scholes_Greeks
Brownian and Related MotionsdeBrowning rBrownian
Catching Results, Warnings and Errors Simultaneouslycatch
Fitting ARMA-GARCH Processesfit_ARMA_GARCH
Fast(er) and Numerically More Robust Fitting of GARCH(1,1) Processesfit_GARCH_11 tail_index_GARCH_11
Parameter Estimators of the Generalized Extreme Value Distributionfit_GEV_MLE fit_GEV_PWM fit_GEV_quantile logLik_GEV
Parameter Estimators of the Generalized Pareto Distributionfit_GPD_MLE fit_GPD_MOM fit_GPD_PWM logLik_GPD
Tools for Getting and Working with Dataget_data
Generalized Extreme Value DistributiondGEV pGEV qGEV rGEV
Fitted GEV Shape as a Function of the ThresholdGEV_shape_plot
(Generalized) Pareto DistributiondGPD dPar pGPD pPar qGPD qPar rGPD rPar
Fitted GPD Shape as a Function of the ThresholdGPD_shape_plot
GPD-Based Tail Distribution (POT method)dGPDtail pGPDtail qGPDtail rGPDtail
Construction of Hierarchical Matriceshierarchical_matrix
Hill Estimator and PlotHill_estimator Hill_plot
Density Plot of the Values from a Lower Triangular Matrixmatrix_density_plot
Graphical Tool for Visualizing Matricesmatrix_plot
Mean Excessmean_excess_GPD mean_excess_np mean_excess_plot
Graphical Tool for Visualizing NAs in a Data SetNA_plot
P-P and Q-Q Plotspp_plot qq_plot
Computing Returns and Inverse Transformationreturns returns_qrmtools
Risk MeasuresES_GPD ES_GPDtail ES_np ES_Par ES_t ES_t01 gEX gVaR RVaR_np VaR_GPD VaR_GPDtail VaR_np VaR_Par VaR_t VaR_t01
Plot of Step Functions, Empirical Distribution and Quantile Functionsedf_plot eqf_plot step_plot
Plot of an Empirical Surival Function with Smith Estimatortail_plot
Formal Tests of Multivariate Normalitymaha2_test mardia_test
``Analytical'' Best and Worst Value-at-Risk for Given Marginalscrude_VaR_bounds dual_bound VaR_bounds_hom
Worst and Best Value-at-Risk and Best Expected Shortfall for Given Marginals via RearrangementsABRA ARA block_rearrange RA rearrange