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.
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Continuous time analysis of fleeting discrete price moves
Neil Shephard,Justin Yang +1 more
TL;DR: In this paper, the authors proposed a model of financial prices where prices are discrete and prices change in continuous time, and a high proportion of price changes are reversed in a fraction of a second.
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Autoregressive conditional root model
Anders Rahbek,Neil Shephard +1 more
TL;DR: The authors developed a time series model which allows long-term disequilibria to have epochs of nonstationarity, giving the impression that long term relationships between economic variables have temporarily broken down, before they endogenously collapse back towards their long term relationship.
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Analysis of High Dimensional Multivariate Stochastic Volatility Models
Neil Shephard,Siddhartha Chib +1 more
TL;DR: In this article, the authors consider the fitting and comparison of high-dimensional multivariate time series models with time varying correlations, and propose an estimation, filtering and model choice algorithm.
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Measuring and forecasting financial variability using realised variance with and without a model
TL;DR: In this paper, the authors use high frequency financial data to proxy, via the realised variance, each day's financial variability via filtering, smoothing and forecasting ideas can be used to improve their estimates of variability by exploiting the time series structure of the realised variances.
Journal ArticleDOI
Computationally intensive econometrics using a distributed matrix-programming language.
TL;DR: In this article, the authors argue that the profession is being held back by the lack of easy-to-use generic software which is able to exploit the availability of cheap clusters of distributed computers.