Nonparametric density estimation for positive time series

Taoufik Bouezmarni and Jeroen Rombouts

No 2006085, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: The Gaussian kernel density estimator is known to have substantial problems for bounded random variables with high density at the boundaries. For i.i.d. data several solutions have been put forward to solve this boundary problem. In this paper we propose the gamma kernel estimator as density estimator for positive data from a stationary -mixing process. We derive the mean integrated squared error, almost sure convergence and asymptotic normality. In a Monte Carlo study, where we generate data from an autoregressive conditional duration model and a stochastic volatility model, we find that the gamma kernel outperforms the local linear density estimator. An application to data from financial transaction durations, realized volatility and electricity price data is provided.

Keywords: gamma kernel; nonparametric density estimation; mixing process; transaction durations; realised volatility (search for similar items in EconPapers)
JEL-codes: C32 C41 (search for similar items in EconPapers)
Date: 2006-10
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Nonparametric density estimation for positive time series (2010)
Working Paper: Nonparametric Density Estimation for Positive Time Series (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2006085

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