Tilted Nonparametric Estimation of Volatility Functions

Peter C. B. Phillips () and Ke-Li Xu ()

No 1612, Cowles Foundation Discussion Papers from Cowles Foundation, Yale University

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.

Keywords: Conditional variance function, Empirical likelihood, Heteroskedasticity, Local linear estimator, Nadaraya-Watson estimator, Nonlinear time series; Nonparametric regression, Volatility (search for similar items in EconPapers)
JEL-codes: C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 2007-06
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Persistent link: http://EconPapers.repec.org/RePEc:cwl:cwldpp:1612

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