{ "_id": "6a291903732311cd8759a632", "Package": "fEGarch", "Type": "Package", "Title": "SM/LM EGARCH & GARCH, VaR/ES Backtesting & Dual LM Extensions", "Version": "1.0.6", "Authors@R": "c(person(given = \"Dominik\",\nfamily = \"Schulz\",\nrole = c(\"aut\", \"cre\"),\nemail = \"dominik.schulz@uni-paderborn.de\",\ncomment = \"Paderborn University, Germany\"),\nperson(given = \"Yuanhua\",\nfamily = \"Feng\",\nrole = \"aut\",\nemail = \"yuanhua.feng@uni-paderborn.de\",\ncomment = \"Paderborn University, Germany\"),\nperson(given = \"Christian\",\nfamily = \"Peitz\",\nrole = c(\"aut\"),\ncomment = \"Financial Intelligence Unit (German Government)\"),\nperson(given = \"Oliver Kojo\",\nfamily = \"Ayensu\",\nrole = c(\"aut\"),\nemail = \"oliver.kojo.ayensu@uni-paderborn.de\",\ncomment = \"Paderborn University, Germany\"),\nperson(given = \"Thomas\", family = \"Gries\",\nrole = \"ctb\",\ncomment = \"Paderborn University, Germany\"),\nperson(given = \"Sikandar\", family = \"Siddiqui\",\nrole = \"ctb\",\ncomment = \"Deloitte Audit Analytics GmbH, Frankfurt, Germany\"),\nperson(given = \"Shujie\",\nfamily = \"Li\",\nrole = \"ctb\",\ncomment = \"Paderborn University, Germany\"))", "Maintainer": "Dominik Schulz ", "Description": "Implement and fit a variety of short-memory (SM) and\nlong-memory (LM) models from a very broad family of exponential\ngeneralized autoregressive conditional heteroskedasticity\n(EGARCH) models, such as a MEGARCH (modified EGARCH), FIEGARCH\n(fractionally integrated EGARCH), FIMLog-GARCH (fractionally\nintegrated modulus Log-GARCH), and more. The FIMLog-GARCH as\npart of the EGARCH family is discussed in Feng et al. (2023)\n. For\nconvenience and the purpose of comparison, a variety of other\npopular SM and LM GARCH-type models, like an APARCH model, a\nfractionally integrated APARCH (FIAPARCH) model, standard GARCH\nand fractionally integrated GARCH (FIGARCH) models, GJR-GARCH\nand FIGJR-GARCH models, TGARCH and FITGARCH models, are\nimplemented as well as dual models with simultaneous modelling\nof the mean, including dual long-memory models with a\nfractionally integrated autoregressive moving average (FARIMA)\nmodel in the mean and a long-memory model in the variance, and\nsemiparametric volatility model extensions. Parametric models\nand parametric model parts are fitted through\nquasi-maximum-likelihood estimation. Furthermore, common\nforecasting and backtesting functions for value-at-risk (VaR)\nand expected shortfall (ES) based on the package's models are\nprovided.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "Collate": "'AttachMessage.R' 'RcppExports.R' 'close_to_lreturn.R'\n'lin_filters.R' 'hessCalc.R' 'arma-farima-wrappers.R'\n'format_applier_ts.R' 'ts_split_train_and_test.R'\n'snorm-distribution-functions.R'\n'sstd-distribution-functions.R' 'sged-distribution-functions.R'\n'sald-distribution-functions.R' 'base_sim_functions.R'\n'density_selectors.R' 'generics.R'\n'input_checkers_egarch_spec.R' 'input_checkers_mean_spec.R'\n'input_checkers_nonpar_spec.R' 'class-mean_spec.R'\n'class-locpol_spec.R' 'class-fEGarch_fit.R'\n'class-egarch-spec.R' 'fitting-function.R' 'sim-functions.R'\n'nonparametric-step.R' 'setup-estim.R'\n'general_garch_fitting.R' 'aparchfit.R' 'gjrgarchfit.R'\n'tgarchfit.R' 'garchfit.R' 'fiaparchfit.R' 'figjrgarchfit.R'\n'fitgarchfit.R' 'figarchfit.R' 'garch_estim.R' 'varescalc.R'\n'datasets.R' 'fEGarch-package.R' 'rugarch-wrappers.R'\n'class-fEGarch_forecast.R' 'forecasting-functions.R'\n'fEGarch_fit-plot.R' 'class-fEGarch_risk.R'\n'ufRisk-functions.R' 'reexport-pipe.R' 'test-functions.R'\n'class-fEGarch_distr_est.R' 'distr_est.R' 'popular-methods.R'", "Config/testthat/edition": "3", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-06-10 07:41:17 UTC", "User": "root" }, "Author": "Dominik Schulz [aut, cre] (Paderborn University, Germany),\nYuanhua Feng [aut] (Paderborn University, Germany), Christian\nPeitz [aut] (Financial Intelligence Unit (German Government)),\nOliver Kojo Ayensu [aut] (Paderborn University, Germany),\nThomas Gries [ctb] (Paderborn University, Germany), Sikandar\nSiddiqui [ctb] (Deloitte Audit Analytics GmbH, Frankfurt,\nGermany), Shujie Li [ctb] (Paderborn University, Germany)", "Config/pak/sysreqs": "cmake", "Repository": 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"page": "measure_risk", "title": "VaR and ES Computation Following Fitted Models or Forecasts", "topics": [ "measure_risk", "measure_risk,fEGarch_distr_est-method", "measure_risk,fEGarch_fit-method", "measure_risk,fEGarch_forecast-method" ] }, { "page": "plot", "title": "S4 Plot Generic", "topics": [ "plot" ] }, { "page": "plot-fEGarch_fit-ANY-method", "title": "Plot Method for Showing Fitting Step Results", "topics": [ "plot,fEGarch_fit,ANY-method", "plot,fEGarch_fit-method" ] }, { "page": "plot-fEGarch_risk-ANY-method", "title": "Plotting of Risk Measure Results (Base R)", "topics": [ "plot,fEGarch_risk,ANY-method", "plot,fEGarch_risk-method" ] }, { "page": "nonpar-generics", "title": "Generics for Nonparametric Smoothing Setting Adjustments", "topics": [ "boundary_method", "boundary_method<-", "bwidth", "bwidth<-", "kernel_order", "kernel_order<-", "poly_order", "poly_order<-" ] }, { "page": "forecasting-generics", "title": "Generics for Forecasts", "topics": [ "predict_roll" ] }, { "page": "forecasting-methods", "title": "Multistep and Rolling Point Forecasts", "topics": [ "predict,fEGarch_fit-method", "predict_roll,fEGarch_fit-method" ] }, { "page": "residuals", "title": "Extract Standardized Residuals", "topics": [ "residuals,fEGarch_fit-method" ] }, { "page": "rugarch_wrappers", "title": "Wrapper Functions for Selected 'rugarch' GARCH Models", "topics": [ "aparch_ru", "egarch_ru", "gjrgarch_ru", "rugarch_wrappers" ] }, { "page": "show-methods", "title": "Show Method for Estimation Output", "topics": [ "show,fEGarch_distr_est-method", "show,fEgarch_distr_est-method", "show,fEGarch_fit_aparch-method", "show,fEgarch_fit_aparch-method", "show,fEGarch_fit_egarch-method", "show,fEGarch_fit_fiaparch-method", "show,fEgarch_fit_fiaparch-method", "show,fEGarch_fit_figarch-method", "show,fEgarch_fit_figarch-method", "show,fEGarch_fit_figjrgarch-method", "show,fEgarch_fit_figjrgarch-method", "show,fEGarch_fit_fitgarch-method", "show,fEgarch_fit_fitgarch-method", 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