A note on asymptotic normality of kernel deconvolution density estimator with logarithmic Chi-square noise: with application in volatility density estimation

Zu, Y. (2015). A note on asymptotic normality of kernel deconvolution density estimator with logarithmic Chi-square noise: with application in volatility density estimation. Econometrics, 3(3), pp. 561-576. doi: 10.3390/econometrics3030561

Abstract

This paper studies the asymptotic normality for kernel deconvolution estimator when the noise distribution is logarithmic Chi-square, both identical and independently distributed observations and strong mixing observations are considered. The dependent case of the result is applied to obtaining the pointwise asymptotic distribution of the deconvolution volatility density estimator in a discrete-time stochastic volatility models.

Publication Type:	Article
Publisher Keywords:	kernel deconvolution estimator, asymptotic normality, volatility density estimation
Subjects:	H Social Sciences > HB Economic Theory
Departments:	School of Policy & Global Affairs > Economics