N
Neil Shephard
Researcher at Harvard University
Publications - 219
Citations - 32524
Neil Shephard is an academic researcher from Harvard University. The author has contributed to research in topics: Stochastic volatility & Volatility (finance). The author has an hindex of 68, co-authored 219 publications receiving 30586 citations. Previous affiliations of Neil Shephard include University of Oxford & London School of Economics and Political Science.
Papers
More filters
Posted Content
Realised Power Variation and Stochastic Volatility Models
TL;DR: In this article, limit distribution results on realised power variation are derived for certain types of semimartingale with continuous local martingale component, in particular for a class of flexible stochastic volatility models.
Posted Content
Nuisance parameters, composite likelihoods and a panel of GARCH models
TL;DR: In this article, the authors investigated the properties of the composite likelihood (CL) method for (T × N_T ) GARCH panels with time series length T and showed that in reasonably large T CL performs well, but due to the estimation error introduced through nuisance parameter estimation, CL is subject to the "incidental parameter" problem for small T.
Posted Content
Econometric analysis of multivariate realised QML: efficient positive semi-definite estimators of the covariation of equity prices
Neil Shephard,Dacheng Xiu +1 more
TL;DR: In this article, the authors extend Xiu's univariate QML approach to the multivariate case, carrying out inference as if the observations arise from an asynchronously observed vector scaled Brownian model observed with error.
Posted Content
Incorporation of a Leverage Effect in a Stochastic Volatility Model
TL;DR: In this article, the authors show how the stochastic volatility model of Barndorff-Nielsen and Shephard (1998a) can be generalised to allow for the leverage effect.
Journal ArticleDOI
Moment conditions and Bayesian non‐parametrics
TL;DR: This paper used Hausdorff measures to analyse moment conditions on real and simulated data, which can be applied widely, including providing Bayesian analysis of quasi-likelihoods, linear and nonlinear regression, missing data and hierarchical models.