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Downside Risk Evaluation with the R Package GAS

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Downside Risk Evaluation with the R Package GAS. / Ardia, David; Boudt, Kris; Catania, Leopoldo.
In: The R Journal, Vol. 10, No. 2, 2018, p. 410-421.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

Harvard

Ardia, D, Boudt, K & Catania, L 2018, 'Downside Risk Evaluation with the R Package GAS', The R Journal, vol. 10, no. 2, pp. 410-421. https://doi.org/10.32614/RJ-2018-064

APA

Ardia, D., Boudt, K., & Catania, L. (2018). Downside Risk Evaluation with the R Package GAS. The R Journal, 10(2), 410-421. https://doi.org/10.32614/RJ-2018-064

CBE

Ardia D, Boudt K, Catania L. 2018. Downside Risk Evaluation with the R Package GAS. The R Journal. 10(2):410-421. https://doi.org/10.32614/RJ-2018-064

MLA

Ardia, David, Kris Boudt, and Leopoldo Catania. "Downside Risk Evaluation with the R Package GAS". The R Journal. 2018, 10(2). 410-421. https://doi.org/10.32614/RJ-2018-064

Vancouver

Ardia D, Boudt K, Catania L. Downside Risk Evaluation with the R Package GAS. The R Journal. 2018;10(2):410-421. doi: 10.32614/RJ-2018-064

Author

Ardia, David ; Boudt, Kris ; Catania, Leopoldo. / Downside Risk Evaluation with the R Package GAS. In: The R Journal. 2018 ; Vol. 10, No. 2. pp. 410-421.

Bibtex

@article{662827216bcd436aa363f5d7c968a4aa,
title = "Downside Risk Evaluation with the R Package GAS",
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 = "David Ardia and Kris Boudt and Leopoldo Catania",
year = "2018",
doi = "10.32614/RJ-2018-064",
language = "English",
volume = "10",
pages = "410--421",
journal = "The R Journal",
issn = "2073-4859",
publisher = "The R Foundation for Statistical Computing",
number = "2",

}

RIS

TY - JOUR

T1 - Downside Risk Evaluation with the R Package GAS

AU - Ardia, David

AU - Boudt, Kris

AU - Catania, Leopoldo

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

U2 - 10.32614/RJ-2018-064

DO - 10.32614/RJ-2018-064

M3 - Journal article

VL - 10

SP - 410

EP - 421

JO - The R Journal

JF - The R Journal

SN - 2073-4859

IS - 2

ER -