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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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Filtering via Simulation: Auxiliary Particle Filters
Michael K. Pitt,Neil Shephard +1 more
TL;DR: This article analyses the recently suggested particle approach to filtering time series and suggests that the algorithm is not robust to outliers for two reasons: the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution.
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Econometric analysis of realized volatility and its use in estimating stochastic volatility models
TL;DR: In this paper, the moments and the asymptotic distribution of the realized volatility error were derived under the assumption of a rather general stochastic volatility model, and the difference between realized volatility and the discretized integrated volatility (which is called actual volatility) were estimated.
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Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics
TL;DR: The authors construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive Ornstein-Uhlenbeck (OU) processes, and study these models in relation to financial data and theory.
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Stochastic volatility : likelihood inference and comparison with arch models
TL;DR: In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models, and a highly effective method is developed that samples all the unobserved volatilities at once using an approximate offset mixture model, followed by an importance reweighting procedure.
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Power and Bipower Variation with Stochastic Volatility and Jumps
TL;DR: Barndorff-Nielsen and Shephard as mentioned in this paper showed that realized power variation and its extension, realized bipower variation, which they introduce here, are somewhat robust to rare jumps.