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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.
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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 realized quasi-likelihood (QML) approach is proposed to estimate the covariance between assets using high frequency data, where the observations arise from an asynchronously observed vector scaled Brownian model observed with error.
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Detecting shocks: Outliers and breaks in time series
TL;DR: In this article, the authors examined the effect on the estimated parameters of moving various kinds of intervention along the series, such as seasonal adjustment and detrending of series, and provided insights into the fragility of inferences to specific shocks.
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Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice
TL;DR: This work proposes a composite realized kernel to estimate the ex-post covariation of asset prices, and shows that the estimator is able to outperform its competitors, while the associated trading costs are competitive.
Posted Content
Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation
TL;DR: In this article, the authors provide an asymptotic distribution theory for some nonparametric tests of the hypothesis that asset prices have continuous sample paths and apply the tests to exchange rate data and show that the null of a continuous sample path is frequently rejected.
Posted Content
Estimating quadratic variation using realised volatility
TL;DR: In this paper, it was shown that even with the large values of M and RV is sometimes a quite noisy estimator of integrated volatility, even with strong regularity assumptions, it is difficult to give any measure of uncertainty of the RV in this context.