O
Oliver Linton
Researcher at University of Cambridge
Publications - 447
Citations - 13008
Oliver Linton is an academic researcher from University of Cambridge. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 55, co-authored 425 publications receiving 12055 citations. Previous affiliations of Oliver Linton include University of Illinois at Urbana–Champaign & Yale University.
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Adaptive Estimation in ARCH Models
TL;DR: In this paper, the authors construct adaptive estimators of the identifiable parameters in a regression model when the errors follow a stationary parametric ARCH(P) process, but do not assume a functional form for the conditional density of the errors, but require that it be symmetric about zero.
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Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods
Oliver Linton,Enno Mammen +1 more
TL;DR: In this paper, the authors investigate a class of semiparametric ARCH(∞) models that includes as a special case the partially nonparametric (PNP) model introduced by Engle and Ng (1993) and which allows for both flexible dynamics and flexible function form with regard to the news impact function.
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Estimating quadratic variation consistently in the presence of endogenous and diurnal measurement error
Ilze Kalnina,Oliver Linton +1 more
TL;DR: In this article, an econometric model that captures the effects of market microstructure on a latent price process is proposed, which allows for correlation between the measurement error and the return process.
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Efficient semiparametric estimation of the fama-french model and extensions
TL;DR: In this article, a weighted additive nonparametric regression model was proposed to estimate the factor returns and the characteristic-beta functions of a factor model, with factor returns serving as time-varying weights and a set of univariate non-parametric functions relating security characteristic to the associated factor betas.
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Evaluating Value-at-Risk Models via Quantile Regression
TL;DR: This article proposed a new backtest that does not rely solely on binary variables, such as whether or not there was an exception, and showed that the new back test provides a sufficient condition to assess the finite sample performance of a quantile model whereas the existing ones do not.