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
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Journal Article
Estimation of an asymmetric model of asset prices
Andrew Harvey,Neil Shephard +1 more
TL;DR: In this article, a stochastic volatility model may be estimated by a quasi-maximum likelihood procedure by transforming to a linear state-space form, which is extended to handle correlation between the two disturbances in the model and applied to data on stock returns.
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Martingale unobserved component models
TL;DR: In this paper, a martingale component model is proposed to handle the local level model with a time-varying signal/noise ratio, which makes the rate of discounting of data local.
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Robust inference on parameters via particle filters and sandwich covariance matrices
Neil Shephard,Arnaud Doucet +1 more
TL;DR: A simulation strategy for computing sandwich covariance matrices which can be used for asymptotic likelihood based inference on state space models when the model is incorrect is developed.
Journal ArticleDOI
Econometric analysis of multivariate realised QML: estimation of the covariation of equity prices under asynchronous trading
Neil Shephard,Dacheng Xiu +1 more
TL;DR: In this paper, a multivariate realised quasi-likelihood (QML) approach is proposed to estimate the covariance between assets using high frequency data, which is challenging due to market microstructure effects and asynchronous trading.
Posted Content
Testing the assumptions behind the use of importance sampling
Siem Jan Koopman,Neil Shephard +1 more
TL;DR: In this paper, the authors use extreme value theory to empirically assess the appropriateness of the sampler's variance assumption in the context of a maximum simulated likelihood analysis of the stochastic volatility model.