Nonparametric Inference of Value-at-Risk for Dependent Financial Returns

The article considers nonparametric estimation of value-at-risk (VaR) and associated standard error estimation for dependent financial returns. Theoretical properties of the kernel VaR estimator are investigated in the context of dependence. The presence of dependence affects the variance of the VaR estimates and has to be taken into consideration in order to obtain adequate assessment of their variation. An estimation procedure of the standard errors is proposed based on kernel estimation of the spectral density of a derived series. The performance of the VaR estimators and the proposed standard error estimation procedure are evaluated by theoretical investigation, simulation of commonly used models for financial returns, and empirical studies on real financial return series. Copyright 2005, Oxford University Press.