Tilted Nonparametric Estimation of Volatility Functions With Empirical Applications

By Ke-Li Xu, Peter C. B. Phillips. 2010

53 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/56a5d1f2def46bb9c7cd0a48a97b62b4a0bc3803

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Yukitoshi Matsushita
2. Taisuke Otsu
3. Ke-Li Xu
4. Yunwen Yang
5. Xuming He
6. Yixiao Sun
7. David M. Kaplan
8. Huixia Judy Wang

In Finance

1. Taisuke Otsu
2. Ke-Li Xu
3. Xuming He
4. Yunwen Yang
5. Huixia Judy Wang
6. Zhongyi Zhu
7. David M. Kaplan
8. Yixiao Sun

Citations in the corpus

• Estimation and Inference of Discontinuity in Density (2013, background): “Second, the local linear estimator based on a nonnegative sample may produce negative estimates at some design points (Xu and Phillips 2011).”
• Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints (2012, unknown): “Yatchew and Härdle (2006) employ a residual-based test to check for monotonicity and convexity simultaneously in a regression setting but their result is for univariate functions and requires that the constraints be strictly satisfied.”