| dc.description.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. | en_US |
