Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk. (10th November 2016)

• Record Type: Journal Article
• Authors: Xu, Qifa; Liu, Xi; Jiang, Cuixia; Yu, Keming
• Is Part Of: Applied stochastic models in business and industry. Volume 32:Number 6(2016:Nov./Dec.)
• Page Start: 882
• Page End: 908
• Publication Date: 2016-11-10
• Subjects: expectiles -- quantile -- neural network -- nonparametric conditional autoregressive expectiles -- value at risk -- expected shortfall; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2212
• Languages: English
• ISSNs: 1524-1904

Abstract

Abstract : The parametric conditional autoregressive expectiles (CARE) models have been developed to estimate expectiles, which can be used to assess value at risk and expected shortfall. The challenge lies in parametric CARE modeling is the specification of a parametric form. To avoid any model misspecification, we propose a nonparametric CARE model via neural network. The nonparametric CARE model can be estimated by a classical gradient based nonlinear optimization algorithm, and the consistency of nonparametric conditional expectile estimators is established. We then apply the nonparametric CARE model to estimating value at risk and expected shortfall of six stock indices. Empirical results for the new model is competitive with those classical models and parametric CARE models. Copyright © 2016 John Wiley & Sons, Ltd.