A0697
Title: A varying coefficient model of expected shortfall and its application
Authors: Jiangtao Wang - Huazhong Normal University (China) [presenting]
Abstract: The purpose is to develop a nonparametric varying-coefficient approach for modeling the value-at-risk (VaR) and expected shortfall (ES) simultaneously since the ES is not elicitable but can be elicitable combined with VaR. Previous studies on conditional ES estimated only considered parametric model set-ups, which account for the stochastic dynamic of asset returns but ignore other exogenous economic variables and the investment situation. The approach overcomes this drawback and allows VaR and ES to be modeled directly using covariates that may be exogenous in a flexible way. A three-step procedure is developed based on the local linear smoothing technique for estimating the coefficient functions, and the consistency and asymptotic normality of the resultant estimator are established. To overcome the challenge associated with calculating the asymptotical variance, a random weight resampling approach is designed by perturbing the loss function directly to approximate the difficult-to-estimate asymptotic covariance. Simulation studies are presented to demonstrate the finite-sample performance of the proposed estimator. The favorable performance of the proposed method is further illustrated via an application for forecasting ES.
