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Semiparametric estimation of volatility: some models and complexity choice in the adaptive functional-coefficient class

Ziegelmann, Flavio (2010) Semiparametric estimation of volatility: some models and complexity choice in the adaptive functional-coefficient class. Journal of Statistical Computation and Simulation, 81 (6). pp. 707-728. ISSN 0094-9655

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Identification Number: 10.1080/00949650903468193

Abstract

In this paper, semiparametric methods are applied to estimate multivariate volatility functions, using a residual approach as in [J. Fan and Q. Yao, Efficient estimation of conditional variance functions in stochastic regression, Biometrika 85 (1998), pp. 645-660; F.A. Ziegelmann, Nonparametric estimation of volatility functions: The local exponential estimator, Econometric Theory 18 (2002), pp. 985-991; F.A. Ziegel-mann, A local linear least-absolute-deviations estimator of volatility, Comm. Statist. Simulation Comput. 37 (2008), pp. 1543-1564], among others. Our main goal here is two-fold: (1) describe and implement a number of semiparametric models, such as additive, single-index ae class of adaptive functional-coefficient models, choosing simultaneously the bandwidth, the number of covariates in the model and also the single-index smoothing variable. The modified cross-validation algorithm is able to tackle the computational burden caused by the model complexity, providing an important tool in semiparametric volatility estimation. We briefly discuss model identifiability when estimating volatility as well as nonnegativity of the resulting estimators. Furthermore, Monte Carlo simulations for several underlying generating models are implemented and applications to real data are provided.

Item Type: Article
Official URL: http://www.informaworld.com/smpp/content~db=all?co...
Additional Information: © 2011 Taylor & Francis
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C15 - Statistical Simulation Methods; Monte Carlo Methods; Bootstrap Methods
Sets: Departments > Statistics
Collections > Economists Online
Date Deposited: 27 Jun 2011 10:28
Last Modified: 04 Apr 2012 17:48
URI: http://eprints.lse.ac.uk/id/eprint/36922

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