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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The Shape of the Risk Premium: Evidence from a Semiparametric GARCH Model
Benoit Perron,Oliver Linton +1 more
TL;DR: In this paper, the relationship between the risk premium on the S&P500 index total return and its conditional variance is examined, where the conditional variance process is parametric, while the conditional mean is an arbitrary function of the conditional variable.
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Multivariate density estimation using dimension reducing information and tail flattening transformations
TL;DR: In this paper, a nonparametric multiplicative bias corrected transformation estimator is proposed for heavy tailed data, which has a dimension reducing effect at the same time as the original dimension of the estimation problem is retained.
Economic impact assessments on MiFID II policy measures related to computer trading in financial markets
TL;DR: In this article, the authors present interim findings of the Foresight project on computer trading and consider the costs, risks and benefits of 6 possible regulatory measures which are being considered within the EU's Markets in Financial Instruments Directive 2 (MiFID II).
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Multiscale clustering of nonparametric regression curves
Michael Vogt,Oliver Linton +1 more
TL;DR: A bandwidth-free clustering method to estimate the unknown group structure from the data is developed and multiscale estimators of the unknown groups and their unknown number are constructed which are free of classical bandwidth or smoothing parameters.
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Bootstrap tests of stochastic dominance with asymptotic similarity on the boundary
TL;DR: The authors proposed a new method of testing stochastic dominance which improves on existing tests based on bootstrap or subsampling, which requires estimation of the contact sets between the marginal distributions.