atRisk: At-Risk
The at-Risk (aR) approach is based on a two-step parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, quantreg, sn, dfoptim, ggplot2, ggridges |
| Published: |
2023-08-08 |
| Author: |
Quentin Lajaunie [aut, cre],
Guillaume Flament [aut, ctb],
Christophe Hurlin [aut],
Souzan Kazemi [rev] |
| Maintainer: |
Quentin Lajaunie <quentin_lajaunie at hotmail.fr> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| In views: |
ActuarialScience |
| CRAN checks: |
atRisk results |
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