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.
Original language | English |
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Pages (from-to) | 410-421 |
Number of pages | 12 |
Journal | The R Journal |
Volume | 10 |
Issue number | 2 |
Early online date | 8 Dec 2018 |
DOIs | |
Publication status | Published - Dec 2018 |
Funding
The authors thank the Editor-in-Chief, John Verzani, two Executive Editors, Roger Bivand and Norman Matloff, the anonymous reviewer, Alexios Ghalanos, participants at the R/Finance conference 2017 in Chicago, and participants at the session "Financial econometrics with R" at the 11th International Conference on Computational and Financial Econometrics 2017 in London. The authors acknowledge Google for financial support via the Google Summer of Code 2016 and 2017 project "GAS"