Estimating Value at Risk and Expected Shortfall Using Expectiles

By James W. Taylor. 2007

316 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/25c98ab3253243dcc005eb2b03649909abc407e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Zhijie Xiao
2. M. Nieto
3. Esther Ruiz
4. Roger W. Koenker
5. Thomas Kneib
6. Chengguo Weng
7. Jun Cai
8. Yu‐Chin Hsu

In Finance

1. M. Nieto
2. Zhijie Xiao
3. Roger W. Koenker
4. Esther Ruiz
5. Thomas Kneib
6. Yu‐Chin Hsu
7. Jin-Huei Yeh
8. C. Kuan

In Management Science and Operations Research

1. Zhijie Xiao
2. Chengguo Weng
3. M. Nieto
4. Jun Cai
5. Thomas Kneib
6. Esther Ruiz
7. Roger W. Koenker
8. Yu‐Chin Hsu

Citations in the corpus

• Quantile regression: A short story on how and why (2018, unknown): “The interpretation of those models is slightly harder than for quantile regression yet it is very useful in the analysis of financial risks, for example, the expected shortfall (Taylor, 2008).”
• Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models (2009, methodology): “In particular, based on a relation between expectile and quantile, Taylor (2008a) and Kuan, Yeh, and Hsu (2009) estimated conditional quantiles using asymmetric least squares methods.”
• Frontiers in VaR forecasting and backtesting (2016, methodology): “There is a one-to-one mapping from expectiles to quantiles and, according to Taylor (2008a), estimating expectiles is more attractive from a computational point of view.”
• Beyond mean regression (2013, background): “Another point in favour of expectiles is their relation to commonly applied risk measures in finance such as the expected shortfall, see Taylor (2008) or Kuan et al. (2008).”
• High-dimensional generalizations of asymmetric least squares regression and their applications (2016, background): “Applications of the ALS regression include estimation of the value at risk and expected shortfall [Kuan, Yeh and Hsu (2009), Taylor (2008)], medical baseline correction [Eilers and Boelens (2005)], and small area estimation [Chambers and Tzavidis (2006), Salvati et al. (2012)] among others.”
• A joint quantile and expected shortfall regression framework (2017, unknown): “23 from van der Vaart (1998) and show that the respective assumptions of this theorem hold.”
• Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall (2008, methodology): “prompts Taylor (2008) to present conditional autoregressive expectile models, which can be used as”
• Expectile and quantile regression—David and Goliath? (2015, methodology): “This connection becomes extremely useful for calculating the ES as it depends both on expectiles and their corresponding quantiles (as described by Taylor, 2008).”
• Assessing value at risk with CARE, the Conditional Autoregressive Expectile models (2009, methodology): “On the other hand, Taylor (2008) does not discuss the asymptotic properties of the ALS estimator and model specification test.”
• Risk management with expectiles (2017, unknown): “More sophisticated econometric modelling of expectiles has been pursued in Taylor (2008), Kuan, Yeh, and Hsu (2009) and De Rossi and Harvey (2009).”
• Optimal reinsurance with expectile (2016, background): “In particular, its applications in finance can been found in Taylor (2008), Kuan et al. (2009), and references therein.”