Calmon, Wilson, Ferioli, Eduardo, Lettieri, Davi, Soares, Johann and Pizzinga, Adrian (2021) An Extensive Comparison of Some Well-Established Value at Risk Methods. International Statistical Review, 89 (1). pp. 148-166. ISSN 0306-7734
Full text not available from this repository.Abstract
In the last two decades, several methods for estimating Value at Risk have been proposed in the literature. Four of the most successful approaches are conditional autoregressive Value at Risk, extreme value theory, filtered historical simulation and time-varying higher order conditional moments. In this paper, we compare their performances under both an empirical investigation using 80 assets and a large Monte Carlo simulation. From our analysis, we conclude that most of the methods seem not to imply huge numerical difficulties and, according to usual backtests and performance measurements, extreme value theory presents the best results most of the times, followed by filtered historical simulation.
| Item Type: | Article |
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| Additional Information: | Funding Information: We the authors acknowledge the Editor Nalini Ravishanker and the Referees because their comments, suggestions and requests have greatly contributed to improve the paper. Davi Lettieri acknowledges the financial support given by FAPERJ (Davi was Scientific Initiation Fellow of FAPERJ ‐ process number ‐ E‐26/ 201.731/2017). Johann Soares acknowledges the financial support given by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior ‐ Brasil (CAPES) ‐ Finance code 001. Funding Information: We the authors acknowledge the Editor Nalini Ravishanker and the Referees because their comments, suggestions and requests have greatly contributed to improve the paper. Davi Lettieri acknowledges the financial support given by FAPERJ (Davi was Scientific Initiation Fellow of FAPERJ - process number - E-26/ 201.731/2017). Johann Soares acknowledges the financial support given by the Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - Brasil (CAPES) - Finance code 001. Publisher Copyright: © 2020 International Statistical Institute |
| Divisions: | Statistics |
| Date Deposited: | 03 May 2024 10:30 |
| Last Modified: | 16 Nov 2024 07:42 |
| URI: | http://eprints.lse.ac.uk/id/eprint/122887 |
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