Tilted nonparametric estimation of volatility functions with empirical applications
This article proposes a novel positive nonparametric estimator of the conditional variance function without reliance on logarithmic or other transformations. The estimator is based on an empirical likelihood modification of conventional local-level nonparametric regression applied to squared residua...
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sg-smu-ink.soe_research-29752017-11-30T08:30:08Z Tilted nonparametric estimation of volatility functions with empirical applications XU, Ke-Li PHILLIPS, Peter C. B. This article proposes a novel positive nonparametric estimator of the conditional variance function without reliance on logarithmic or other transformations. The estimator is based on an empirical likelihood modification of conventional local-level nonparametric regression applied to squared residuals of the mean regression. The estimator is shown to be asymptotically equivalent to the local linear estimator in the case of unbounded support but, unlike that estimator, is restricted to be nonnegative in finite samples. It is fully adaptive to the unknown conditional mean function. Simulations are conducted to evaluate the finite-sample performance of the estimator. Two empirical applications are reported. One uses cross-sectional data and studies the relationship between occupational prestige and income, and the other uses time series data on Treasury bill rates to fit the total volatility function in a continuous-time jump diffusion model. 2011-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1976 info:doi/10.1198/jbes.2011.09012 https://ink.library.smu.edu.sg/context/soe_research/article/2975/viewcontent/TitledNonparametricEstVolatilityFunctions_2010_pp.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Conditional heteroscedasticity Conditional variance function Empirical likelihood Heteroscedastic nonparametric regression Jump diffusion Local linear estimator Econometrics |
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Conditional heteroscedasticity Conditional variance function Empirical likelihood Heteroscedastic nonparametric regression Jump diffusion Local linear estimator Econometrics XU, Ke-Li PHILLIPS, Peter C. B. Tilted nonparametric estimation of volatility functions with empirical applications |
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This article proposes a novel positive nonparametric estimator of the conditional variance function without reliance on logarithmic or other transformations. The estimator is based on an empirical likelihood modification of conventional local-level nonparametric regression applied to squared residuals of the mean regression. The estimator is shown to be asymptotically equivalent to the local linear estimator in the case of unbounded support but, unlike that estimator, is restricted to be nonnegative in finite samples. It is fully adaptive to the unknown conditional mean function. Simulations are conducted to evaluate the finite-sample performance of the estimator. Two empirical applications are reported. One uses cross-sectional data and studies the relationship between occupational prestige and income, and the other uses time series data on Treasury bill rates to fit the total volatility function in a continuous-time jump diffusion model. |
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text |
author |
XU, Ke-Li PHILLIPS, Peter C. B. |
author_facet |
XU, Ke-Li PHILLIPS, Peter C. B. |
author_sort |
XU, Ke-Li |
title |
Tilted nonparametric estimation of volatility functions with empirical applications |
title_short |
Tilted nonparametric estimation of volatility functions with empirical applications |
title_full |
Tilted nonparametric estimation of volatility functions with empirical applications |
title_fullStr |
Tilted nonparametric estimation of volatility functions with empirical applications |
title_full_unstemmed |
Tilted nonparametric estimation of volatility functions with empirical applications |
title_sort |
tilted nonparametric estimation of volatility functions with empirical applications |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2011 |
url |
https://ink.library.smu.edu.sg/soe_research/1976 https://ink.library.smu.edu.sg/context/soe_research/article/2975/viewcontent/TitledNonparametricEstVolatilityFunctions_2010_pp.pdf |
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1770573487325315072 |