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Tilted Nonparametric Estimation of Volatility Functions

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Author Info
Peter C.B. Phillips () (Cowles Foundation, Yale University)
Ke-Li Xu (Dept. of Mathematics, Yale University)

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Abstract

This paper proposes a novel positive nonparametric estimator of the conditional variance function without relying on a logarithmic transformation. The basic idea is to apply the re-weighted Nadaraya-Watson regression estimator of Hall and Presnell (1999, Journal of the Royal Statistical Society B, 61, 143--158) to squared residuals. The new conditional variance estimator is asymptotically equivalent to the local linear estimator and is restricted to be positive in finite samples. A small simulation is performed to compare the new methodology with Ziegelmann's (2002) local exponential and Yu and Jones's (2004) local likelihood-based estimators of the conditional variance.

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File URL: http://cowles.econ.yale.edu/P/cd/d16a/d1612.pdf
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Publisher Info
Paper provided by Cowles Foundation, Yale University in its series Cowles Foundation Discussion Papers with number 1612.

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Length: 22 pages
Date of creation: Jun 2007
Date of revision:
Handle: RePEc:cwl:cwldpp:1612

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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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Related research
Keywords: Conditional variance function; Empirical likelihood; Heteroskedasticity; Local linear estimator; Nadaraya-Watson estimator; Nonlinear time series; Nonparametric regression; Volatility;

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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  1. Ke-Li Xu & Peter C.B. Phillips, 2006. "Adaptive Estimation of Autoregressive Models with Time-Varying Variances," Cowles Foundation Discussion Papers 1585R, Cowles Foundation, Yale University, revised Nov 2006. [Downloadable!]
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