Alternatives and similar repositories for rtadfr:

Users that are interested in rtadfr are comparing it to the libraries listed below

• PedroBSB / Caviar
  Conditional Autoregressive Value-at-Risk: all flavors of CAViaR.
  ☆10Updated 7 years ago
• lucabarbaglia / t-VAR
  Large t-Vector AutoRegressive models with volatility spillovers and networks. Code of the paper Barbaglia, Croux, Wilms (2020) "Volatilit…
  ☆19Updated 4 years ago
• GustavoAlovisi / cvar_portfolio_optim
  ARMA-GARCH Mixture Copula Mean-CVaR portfolio optimization project.
  ☆29Updated 4 years ago
• Tinkat / CoVaR
  CoVaR estimation via quantile regression
  ☆26Updated 7 years ago
• cran / rumidas
   This is a read-only mirror of the CRAN R package repository. rumidas — Univariate GARCH-MIDAS, Double-Asymmetric GARCH-MIDAS and MEM-MI…
  ☆11Updated last month
• EmanuelSommer / portvine
  Portfolio level (un)conditional risk measure estimation for backtesting using Vine Copula and ARMA-GARCH models.
  ☆22Updated last year
• lcallot / lassovar
  Estimation and forecasting of VAR model with the Lasso
  ☆28Updated last year
• jhlinplus / high_dim_favar_estimation
  Official Code Repo for Paper "Regularized estimation of high-dimensional FAVAR models" in JMLR, 2020
  ☆8Updated last year
• sardormirzaev / -measuring-volatility-spillovers
  Diebold & Yilmaz method, DCC-Garch method on composite indicies. 2009-2019
  ☆20Updated 4 years ago
• franzmohr / bgvars
  Bayesian Inference for Global Vector Autoregressive (GVAR) and Global Vector Error Correction (GVEC) Models
  ☆17Updated last year
• blake-marsh / GARCH_replication
  Replication of key GARCH model papers
  ☆35Updated 9 years ago
• JellalYu / Multivariate-DCC-GARCH-model
  Multivariate DCC-GARCH model
  ☆15Updated 6 years ago
• Karansamlal / HAR-RV-model
  Heterogeneous Autoregressive model of Realized Volatility (HAR-RV), introduced by F Corsi (‎2009).
  ☆12Updated 4 years ago
• chenyang45 / BoostedHP
  packages for Peter Phillips and Zhentao Shi (2018): "Boosting the Hodrick-Prescott Filter"
  ☆12Updated 2 years ago
• zhentaoshi / Boosted_HP_filter
  Functions and replication files for Peter Phillips and Zhentao Shi (2021): "Boosting: Why You Can Use the HP Filter"
  ☆28Updated 2 years ago
• QuantLet / GrangerCausalityTestInQuantile
  ☆11Updated 3 months ago
• emilsjoerup / HARModel
  An implementation of the Heterogeneous AutoRegressive model from Corsi(2009)
  ☆18Updated 2 years ago
• Marga8 / HDGCvar
  Granger causality testing in High Dimensional Vector Autoregressive Models
  ☆15Updated 11 months ago
• dppalomar / covFactorModel
  Covariance Matrix Estimation via Factor Models
  ☆33Updated 6 years ago
• 18939739359 / copula-dcc-garch
  dynamic copula dcc garch estimate bank systematic risk
  ☆19Updated 3 years ago
• anhdanggit / volatility-garch-VaR
  Simulate and estimate volatility by GARCH with/without leverage, riskmetriks. Compute Value-at-Risk and Test on VaR Violation
  ☆21Updated 6 years ago
• yools56 / Neural-Network-based-HAR-models
  R code and Realized Volatility (RV) series set for fitting NN-based-HAR models to multinational RV series.
  ☆13Updated 6 years ago
• hannes101 / TimeVaryingCointegration
  R Implementation of the Time Varying Cointegration by Bierens and Martins 2010
  ☆10Updated 9 years ago
• Reckziegel / Large-TVP-VAR
  Trying to get "Large Time-Varying Parameter VAR" of Koop & Kurubillis (2013) done in R.
  ☆22Updated 7 years ago
• ArdiaD / RiskPortfolios
  Functions for the construction of risk-based portfolios
  ☆51Updated 3 years ago
• rhameysayed / Spillovers-and-Connectedness
  ☆18Updated 2 years ago
• nickpoison / Stochastic-Volatility-Models
  R Code to accompany "A Note on Efficient Fitting of Stochastic Volatility Models"
  ☆13Updated 2 years ago
• dhopp1 / nowcastDFM
  Dynamic factor models (DFM) in R. Easy estimation and new data contributions to changes in prediction.
  ☆27Updated last year
• onnokleen / mfGARCH
  An R package for using mixed-frequency GARCH models
  ☆70Updated 2 years ago
• oa36 / Systemic-Risk
  Systemic Risk - CoVaR
  ☆12Updated 4 years ago