Estimation of extreme value-at-risk: An EVT approach for quantile GARCH model
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DOI: 10.1016/j.econlet.2014.06.028
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References listed on IDEAS
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Cited by:
- H. Kaibuchi & Y. Kawasaki & G. Stupfler, 2022.
"GARCH-UGH: a bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series,"
Quantitative Finance, Taylor & Francis Journals, vol. 22(7), pages 1277-1294, July.
- Hibiki Kaibuchi & Yoshinori Kawasaki & Gilles Stupfler, 2021. "GARCH-UGH: A bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series," Papers 2104.09879, arXiv.org.
- Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
- Marco Bee & Luca Trapin, 2018. "Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review," Risks, MDPI, vol. 6(2), pages 1-16, April.
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More about this item
Keywords
Extreme value theory; GARCH; Quantile regression; Semiparametric; Value at risk;All these keywords.
JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
Statistics
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