Quantiles, expectiles and splines

By G. Rossi, Andrew S. Harvey. 2009

108 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/0b7261e2956f8903de3c2a06379f826eec9bcaf0

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In Analytical Chemistry

In Control and Systems Engineering

Citations in the corpus

Generalized Quantiles as Risk Measures (2014, background): “Section 3 is devoted to the study of expectiles, that are becoming increasingly popular also in the econometric literature (see for instance Kuan et al. (2009), De Rossi and Harvey (2009) and Embrechts and Hofert (2013) and the references therein.)”
Estimation of tail risk based on extreme expectiles (2016, unknown): “1 Asymmetric least squares estimation On the basis of the limit (19) and then of the heavy-tailedness assumption on X, we have for τ ă τ 1 ă 1 that, as τ Ñ 1, XMESpτ 1q « UXp1{F Y pξY,τ 1qq UXp1{F Y pξY,τ qq XMESpτq « ˆ F Y pξY,τ q F Y pξY,τ 1q γX XMESpτq.”
Bayesian Tail Risk Interdependence Using Quantile Regression (2015, methodology): “In particular, based on the ideas of De Rossi and Harvey (2009), we propose a dynamic model to capture the evolution of VaR and CoVaR. In order to provide a flexible solution for quantile modelisation whilst retaining a parsimonious representation, the time evolution of the process should be careful”
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). The first question that has to be addressed is the choice of q. In the case of VaRα , it is customary to choose α = 0.01. In the case of ESα , the lates”
Kernel density estimation for time series data (2012, methodology): “De Rossi & Harvey (2009) adopt a different method, based on ideas from signal extraction and using only indicator variables.”
Expectile and quantile regression—David and Goliath? (2015, unknown): “5 + 2G(q˛) + (1 − 2˛)q˛ (1.3) with G(q) = ∫ q−∞ y dF(y) as the partial moment function and F(y) as cumulative distribution of y (see also De Rossi and Harvey, 2009).”
Conditional Quantile Estimation for Generalized Autoregressive Conditional Heteroscedasticity Models (2009, methodology): “Rossi and Harvey (2009) recently proposed an iterative Kalman filter method to calculate dynamic conditional quantiles that can be applied to calculate the CAViaR model.”