A Three-Step Nonparametric Estimation of Conditional Value-At-Risk Admitting a Location-Scale Model

Abstract

Financial institutions owners and regulators are concerned majorly about risk analysis, Value-at-Risk (VaR) is one of the most popular and common measures of risk used in finance, measures the down-side risk and is determined for a given probability level. In this paper, we consider the problem of estimating conditional Value-at-Risk via the nonparametric method and have proposed a three-step nonparametric estimator for conditional Value-at-Risk. The returns are assumed to have a location-scale model where the function of the error innovations is assumed unknown. The asymptotic properties of the proposed estimator were established, a simulation study was also conducted to confirm the properties. Application to real data was carried out, TOTAL stocks quoted on the Nigerian Stock Exchange using daily closing prices for covering the period between January 02, 2008 to December 29, 2017 trading days was used to illustrate the applicability of the estimator.