Working Paper
1903.01637.pdf slides20200624.pdf
Hoyle, Edward, and Neil Shephard. Working Paper. “Volatility scaling's impact on the Sharpe ratio”. Abstract
We study the econometric properties of dynamic risk parity, which volatility scales to equalise risk through time using the precision process, the inverse of the time-varying volatility. A particular focus is on the impact of the Sharpe ratio. We give necessary and suffcient conditions that volatility scaling improves the Sharpe ratio of an investment. We approximate the Sharpe improvement using the sum of two terms: one determined by the convexity of the precision and the other the covariance of the precision and conditional mean. We show that empirically this approximation is very accurate and we document the
relative importance of the two terms.
Engle, Robert F, Cavit Pakel, Kevin K. Shephard, and Neil Shephard. Forthcoming. “Fitting Vast Dimensional Time-Varying Covariance Models.” Journal of Business and Economic Statistics. Abstract
Estimation of time-varying covariances is a key input in risk management and asset allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of such models is computationally costly and parameter estimates are meaningfully biased when applied to a moderately large number of assets. Here we propose a novel estimation approach that suffers from neither of these issues, even when the number of assets is in the hundreds. The theory of this new method is developed in some detail. The performance of the proposed method is investigated using extensive simulation studies and empirical examples.
vastarch-revised.pdf supplementaryappendix-revised.pdf
Britton, Jack, Laura van der Erve, Neil Shephard, and Chris Belfield. 2019. Where is the money going? Estimating government spending on different university degrees. London: Institute of Fiscal Studies. Publisher's Version
Britton, Jack, Lorraine Deardon, Neil Shephard, and Anna Vignoles. 2019. “Is improving access to university enough? Socio economic gaps in the earnings of English graduates.” Oxford Bulletin of Economics and Statistics 81: 328-368. Abstract
Much research and policy attention has been on socio economic gaps in participation at university, but little attention has been paid to gaps in earnings. This paper addresses this shortfall using tax and student loan administrative data to investigate the earnings of English graduates up to their mid thirties by socio economic background. We find that  graduates from higher income families (from the top fth of the income distribution of those enrolled in university) have average earnings which are 20% higher than those from lower income families. Once we condition on institution and subject choices, this premium roughly halves, to around 10%. The premium grows with age and is larger for men, in particular for men at the most selective universities. We follow Chetty et al. (2017) and estimate English mobility scorecards by university and subject, highlighting the good performance of medicine, economics, law, business, engineering, technology, math, computer science and architecture courses as well as the prominent London-based universities.
Bojinov, Iavor, and Neil Shephard. 2019. “Time series experiments and causal estimands: exact randomization tests and trading.” Journal of the American Statistical Association 114: 1665-82. Abstract
We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data.  Our approach allows the estimation of a broad class of these estimands and exact randomization based p-values for testing causal effects, without imposing stringent assumptions.  We further derive a general central limit theorem that can be used to conduct conservative tests and build confidence intervals for causal effects.  Finally, we provide three methods for generalizing our approach to multiple units that are receiving the same class of treatment, over time.  We test our methodology on simulated "potential autoregressions,"which have a causal interpretation.  Our methodology is partially inspired by data from a large number of experiments carried out by a financial company who compared the impact of two different ways of trading equity futures contracts.  We use our methodology to make causal statements about their trading methods.
Britton, Jack, Neil Shephard, and Anna Vignoles. 2019. “A comparison of sample survey measures of earnings of English graduates with administrative data.” Journal of the Royal Statistical Society, Series A 182: 719-754.
Bornn, Luke, Neil Shephard, and Reza Solgi. 2019. “Moment conditions and Bayesian nonparametrics.” Journal of the Royal Statistical Society, Series B 81: 5-43.
Shephard, Neil, and Shaoyang Ning. 2018. “A Nonparametric Bayesian Approach to Copula Estimation.” Journal of Statistical Computation and Simulation 201: 1081-1105. Abstract
We propose a novel Dirichlet-based Pólya tree (D-P tree) prior on the copula and based on the D-P tree prior, a nonparametric Bayesian inference procedure. Through theoretical analysis and simulations, we are able to show that the flexibility of the D-P tree prior ensures its consistency in copula estimation, thus able to detect more subtle and complex copula structures than earlier nonparametric Bayesian models, such as a Gaussian copula mixture. Furthermore, the continuity of the imposed D-P tree prior leads to a more favourable smoothing effect in copula estimation over classic frequentist methods, especially with small sets of observations. We also apply our method to the copula prediction between the S&P 500 index and the IBM stock prices during the 2007–08 financial crisis, finding that D-P tree-based methods enjoy strong robustness and flexibility over classic methods under such irregular market behaviours.
Shephard, Neil, and Justin J Yang. 2017. “Continuous time analysis of fleeting discrete price moves.” Journal of the American Statistical Association 112: 1090-1106.
Lunde, Asger, Kevin Sheppard, and Neil Shephard. 2015. “Econometric analysis of vast covariance matrices using composite realized kernels and their application to portfolio choice.” Journal of Business and Economic Statistics 34: 504-518.
Shephard, Neil, and Justin Yang. 2015. “Likelihood Inference for Exponential-Trawl Processes.” The Fascination of Probability, Statistics and their Applications, edited by Mark Podolskij, Robert Stelzer, and S Thorbjornsen, 251-281. Springer. Publisher's Version
Shephard, Neil. 2015. “Martingale unobserved component models.” Unobserved Components and Time Series Econometrics, edited by Siem Jan Koopman and Neil Shephard, 218-249. Oxford: Oxford University Press.
Koopman, Siem Jan, and Neil Shephard, ed. 2015. Unobserved Components and Time Series Econometrics. Oxford: Oxford University Press, 370. Publisher's Version
Noureldin, Diaa, Kevin Sheppard, and Neil Shephard. 2014. “Multivariate Rotated ARCH models.” Journal of Econometrics 179: 16-30.
Barndorff-Nielsen, Ole E., Asger Lunde, Neil Shephard, and Almut Veraart. 2014. “Integer value trawl processes: a class of stationary infinitely divisible processes.” Scandanavian Journal of Statistics 41: 693-724.
Barndorff-Nielsen, Ole E., David G. Pollard, and Neil Shephard. 2012. “Integer-valued Levy processes and low latency financial econometrics.” Quantitative Finance 12: 587-605.
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Shephard, Neil, Diaa Noureldin, and Kevin K Sheppard. 2012. “Multivariate high-frequency-based volatility (HEAVY) models.” Journal of Applied Econometrics 27: 907-933.
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Barndorff-Nielsen, Ole E., Peter R Hansen, Asger Lunde, and Neil Shephard. 2011. “Subsampling realised kernels.” Journal of Econometrics 160: 204-219.
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Pakel, Cavit, Neil Shephard, and Kevin K Sheppard. 2011. “Nuisance parameters, composite likelihoods and a panel of GARCH models.” Statistica Sinica 21: 307-329.
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