Use this identifier to quote or link this document: http://hdl.handle.net/2072/202969

Nonparametric estimation of Value-at-Risk
Alemany Leira, Ramon; Bolancé Losilla, Catalina; Guillén, Montserrat
Xarxa de Referència en Economia Aplicada (XREAP)
A method to estimate an extreme quantile that requires no distributional assumptions is presented. The approach is based on transformed kernel estimation of the cumulative distribution function (cdf). The proposed method consists of a double transformation kernel estimation. We derive optimal bandwidth selection methods that have a direct expression for the smoothing parameter. The bandwidth can accommodate to the given quantile level. The procedure is useful for large data sets and improves quantile estimation compared to other methods in heavy tailed distributions. Implementation is straightforward and R programs are available.
2012-10-16
33 - Economia
Teoria de l'estimació
Risc (Economia)
Estadística no paramétrica
Estimation theory
Risk
Nonparametric statistics
L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by/3.0/es/
40 p.
Working Paper
Xarxa de Referència en Economia Aplicada (XREAP)
XREAP;2012-19
         

Full text files in this document

Files Size Format
XREAP2012-19.pdf 356.1 KB PDF

Show full item record

Related documents

Other documents of the same author

Bolancé Losilla, Catalina; Alemany Leira, Ramon; Guillén, Montserrat
Alemany Leira, Ramon; Bolancé Losilla, Catalina; Guillén, Montserrat
Bolancé Losilla, Catalina; Alemany Leira, Ramon; Guillén Estany, Montserrat
 

Coordination

 

Supporters