An Extensive Comparison of Some Well‐Established Value at Risk Methods-Reference-Cited by-同舟云学术

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An Extensive Comparison of Some Well‐Established Value at Risk Methods

Published:2020-07-23 Issue:1 Volume:89 Page:148-166
ISSN:0306-7734
Container-title:International Statistical Review
language:en
Short-container-title:International Statistical Review

Author:

Calmon Wilson¹^ORCID,Ferioli Eduardo¹²,Lettieri Davi³,Soares Johann⁴⁵,Pizzinga Adrian¹

Affiliation:

1. Institute of Mathematics and Statistics Fluminense Federal University Niteroi Brazil

2. Department of Statistics London School of Economics and Political Science London UK

3. Department of Mechanical Engineering Fluminense Federal University Niteroi Brazil

4. FGV EPGE Brazilian School of Economics and Finance Rio de Janeiro Brazil

5. Faculty of Economics Fluminense Federal University Niteroi Brazil

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/insr.12393

Reference69 articles.

1. A detailed comparison of value at risk estimates

2. A comprehensive review of Value at Risk methodologies

3. Bayesian Estimation of a Skew-Student-t Stochastic Volatility Model

4. The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR

5. Evaluating predictive performance of value-at-risk models in emerging markets: a reality check

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1. A spectral approach to evaluating VaR forecasts: stock market evidence from the subprime mortgage crisis, through COVID-19, to the Russo–Ukrainian war;Quality & Quantity;2024-04-15

2. Forecasting VaRs via hybrid EVT with normal and non-normal filters: A comparative analysis from the Chinese stock market;Pacific-Basin Finance Journal;2024-02

3. Patch Track Software for Measuring Kinematic Phenotypes of Arabidopsis Roots Demonstrated on Auxin Transport Mutants;International Journal of Molecular Sciences;2023-11-18

4. Sparse estimation within Pearson's system, with an application to financial market risk;Canadian Journal of Statistics;2023-01-06

5. A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies;Journal of Forecasting;2022-11-23

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