Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall

By James W. Taylor. 2008

168 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/2b60a9b6873a3346d5f3b218d461b65cacd3095e

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Citations in the corpus

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.”
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.”
A joint quantile and expected shortfall regression framework (2017, unknown): “Taylor (2008b) proposes an implicit approach for forecasting ES using exponentially weighted quantile regression and Taylor (2008a) introduces a procedure based on expectile regression and a relationship between the ES and expectiles.”
Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions (2011, background): “As others have noted (e.g. Taylor 2008), direct inference on expected shortfall forecasts leads to few significant distinctions because of the small number of values in the lower 6A: General AEPD; B: AEPD with α = 0.5; C: SEPD; D: GED.”
Bayesian Tail Risk Interdependence Using Quantile Regression (2015, methodology): “An alternative solution is proposed by Yu and Moyeed (2001) and Tokdar and Kadane (2012) where they assume improper priors for the quantile regression and scale parameters.”
Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution (2019, methodology): “Taylor (2008) used exponentially weighted quantile regression for VaR estimation, and essentially replaced the summation in expression (3) with the resulting exponentially weighted summation to deliver a conditional ES estimate.”