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Downside risk evaluation with the R package GAS

David Ardia, Kris Boudt (UGent) and Leopoldo Catania
(2018) R JOURNAL. 10(2). p.410-421
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Abstract
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.
Keywords
Statistics, Probability and Uncertainty, Statistics and Probability, Numerical Analysis, VALUE-AT-RISK, SCORE MODELS

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Citation

Please use this url to cite or link to this publication:

MLA
Ardia, David, et al. “Downside Risk Evaluation with the R Package GAS.” R JOURNAL, vol. 10, no. 2, 2018, pp. 410–21, doi:10.32614/rj-2018-064.
APA
Ardia, D., Boudt, K., & Catania, L. (2018). Downside risk evaluation with the R package GAS. R JOURNAL, 10(2), 410–421. https://doi.org/10.32614/rj-2018-064
Chicago author-date
Ardia, David, Kris Boudt, and Leopoldo Catania. 2018. “Downside Risk Evaluation with the R Package GAS.” R JOURNAL 10 (2): 410–21. https://doi.org/10.32614/rj-2018-064.
Chicago author-date (all authors)
Ardia, David, Kris Boudt, and Leopoldo Catania. 2018. “Downside Risk Evaluation with the R Package GAS.” R JOURNAL 10 (2): 410–421. doi:10.32614/rj-2018-064.
Vancouver
1.
Ardia D, Boudt K, Catania L. Downside risk evaluation with the R package GAS. R JOURNAL. 2018;10(2):410–21.
IEEE
[1]
D. Ardia, K. Boudt, and L. Catania, “Downside risk evaluation with the R package GAS,” R JOURNAL, vol. 10, no. 2, pp. 410–421, 2018.
@article{8601075,
  abstract     = {{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.}},
  author       = {{Ardia, David and Boudt, Kris and Catania, Leopoldo}},
  issn         = {{2073-4859}},
  journal      = {{R JOURNAL}},
  keywords     = {{Statistics,Probability and Uncertainty,Statistics and Probability,Numerical Analysis,VALUE-AT-RISK,SCORE MODELS}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{410--421}},
  title        = {{Downside risk evaluation with the R package GAS}},
  url          = {{http://doi.org/10.32614/rj-2018-064}},
  volume       = {{10}},
  year         = {{2018}},
}
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