Assessing value at risk with CARE, the Conditional Autoregressive Expectile models

By C. Kuan, Jin-Huei Yeh, Yu‐Chin Hsu. 2009

225 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f498526aa74c9cffe078a39e87a8ae5657f2a4b0

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

In Finance

Citations in the corpus

Generalized Quantiles as Risk Measures (2014, background): “In the statistical literature several generalized quantiles xα have been introduced, by considering more general loss functions: Newey and Powell (1987) introduced the expectiles as the minimizers of a piecewise quadratic loss function, Chen (1996) considered power loss functions (Lquantiles), Breck”
Extremiles: A New Perspective on Asymmetric Least Squares (2018, unknown): “For a fixed τ in (0, 1), the τ th quantile of Y can be obtained by minimizing asymmetrically weighted mean absolute deviations (Koenker and Bassett 1978): qτ ∈ argminθE {|τ − 1I(Y ≤ θ )| · |Y − θ | − |τ − 1I(Y ≤ 0)| · |Y”
A Confidence Corridor for Expectile Functions (2011, unknown): “Kuan et al. (2009) considered the conditional autoregressive expectile (CARE) model to calculate the VaR, and expectiles are used to calculate the expected shortfall in Taylor (2008).”
Penalized expectile regression: an alternative to penalized quantile regression (2018, unknown): “Table 1 (Kuan et al. 2009) contains the corresponding quantile α to the expectile τ = 0.01, 0.03, 0.05, 0.1, 0.25, respectively, for the Uniform(-a, a), N (0, 1), t (30), t (10), t (5) and t (3) distributions where t (d) indicates the distribution with d degrees of freedom, illustrating the…”
COHERENCE AND ELICITABILITY (2013, background): “If we restrict our attention to law-invariant coherent risk measures as introduced by Kusuoka (2001) we can view them as functionals on some set P of probability distributions on R.”
Risk management with expectiles (2017, unknown): “To be consistent with these sign conventions and to facilitate comparisons, we define, following Kuan, Yeh, and Hsu (2009), the expectile-VaR (EVaRq) as follows: EVaRq(X ) = −eq(X ).”
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.”
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.”
Expectile and quantile regression—David and Goliath? (2015, unknown): “For accurate predictions, one should aim to combine conditional autoregressive expectiles (CARE, Kuan et al., 2009) that are able to account for the autocorrelation in the data with the methods introduced in this article.”
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).”