Financial risk managers routinely use non–linear time series models to predict the downside risk of the capital under management. They also need to evaluate the adequacy of their model using so–called backtesting procedures. The latter involve hypothesis testing and evaluation of loss functions. This paper shows how the R package GAS can be used for both the dynamic prediction and the evaluation of downside risk. Emphasis is given to the two key financial downside risk measures: Value-at-Risk (VaR) and Expected Shortfall (ES). High-level functions for: (i) prediction, (ii) backtesting, and (iii) model comparison are discussed, and code examples are provided. An illustration using the series of log–returns of the Dow Jones Industrial Average constituents is reported.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-064.zip
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For attribution, please cite this work as
Ardia, et al., "Downside Risk Evaluation with the R Package GAS", The R Journal, 2018
BibTeX citation
@article{RJ-2018-064,
author = {Ardia, David and Boudt, Kris and Catania, Leopoldo},
title = {Downside Risk Evaluation with the R Package GAS},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-064},
doi = {10.32614/RJ-2018-064},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {410-421}
}