Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/13310
Title: Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk
Authors: Xu, Qifa
Liu, Xi
Jiang, Cuixia
Keywords: Expectiles;Quantile;Neural network;Nonparametric conditional autoregressive expectiles;Value at risk;Expected shortfall
Issue Date: 2016
Publisher: John Wiley & Sons, Ltd
Citation: Applied Stochastic Models in Business and Industry,(2016)
Abstract: The parametric conditional autoregressive expectiles (CARE) models have been developed by [1] to estimate expectiles that can be used to assess value at risk (VaR) and expected shortfall (ES). The challenge lies in parametric CARE modelling is speci cation of a parametric form. To avoid any model misspeci cation, we propose a nonparametric CARE model via neural network. The nonparametric CARE model can be estimated by a classical gradient based nonlinear optimization algorithm. We then apply the nonparametric CARE model to estimating VaR and ES of six stock indices. Empirical results for the new model is competitive with those classical models and parametric CARE models.
URI: http://bura.brunel.ac.uk/handle/2438/13310
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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