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D-Index & Metrics

Economics and Finance

D-Index
73
Citations
42561
World Ranking
383
National Ranking
262

Research.com Recognitions

  • 2004 - Fellows of the Econometric Society

Overview

Neil Shephard is affiliated with Harvard University in the United States and has contributed extensively to the fields of Mathematics and Economics, Econometrics, and Finance. Their research primarily spans topics related to Financial Risk and Volatility Modeling, Statistical Methods and Inference, and Advanced Causal Inference Techniques.

The scientist has published research in several key areas, including:

  • Financial Risk and Volatility Modeling
  • Statistical Methods and Inference
  • Advanced Causal Inference Techniques
  • School Choice and Performance
  • Forecasting Techniques and Applications
  • Bayesian Methods and Mixture Models
  • Economics of Agriculture and Food Markets

Frequent co-authors collaborating with Neil Shephard include:

  • Iavor Bojinov
  • Ashesh Rambachan
  • Mikkel Bennedsen
  • Asger Lunde
  • Almut E. D. Veraart

Key venues where their work has appeared are:

  • arXiv (Cornell University)
  • Journal of Business and Economic Statistics
  • Quantitative Economics
  • Journal of Econometrics
  • Harvard Data Science Review

Recent papers by Neil Shephard include:

  • "Fitting Vast Dimensional Time-Varying Covariance Models," 2020, Journal of Business and Economic Statistics
  • "Panel experiments and dynamic causal effects: A finite population perspective," 2021, Quantitative Economics
  • "Inference and forecasting for continuous-time integer-valued trawl processes," 2023, Journal of Econometrics
  • "An estimator for predictive regression: reliable inference for financial economics," 2020, arXiv (Cornell University)
  • "Inference and forecasting for continuous-time integer-valued trawl processes," 2021, arXiv (Cornell University)

Neil Shephard was recognized as a Fellow of the Econometric Society in 2004.

Best Publications

  • Filtering via Simulation: Auxiliary Particle Filters

    Michael K. Pitt;Neil Shephard

  • STOCHASTIC VOLATILITY : LIKELIHOOD INFERENCE AND COMPARISON WITH ARCH MODELS

    Sangjoon Kim;Neil Shephard;Siddhartha Chib

  • Econometric analysis of realized volatility and its use in estimating stochastic volatility models

    Ole E. Barndorff-Nielsen;Neil Shephard

  • Power and Bipower Variation with Stochastic Volatility and Jumps

    Ole E. Barndorff-Nielsen;Neil Shephard

  • Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics

    Ole E. Barndorff-Nielsen;Neil Shephard

  • Multivariate stochastic variance models

    Andrew Harvey;Esther Ruiz;Neil Shephard

  • Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation

    Ole Eiler Barndorff-Nielsen;Neil Shephard

  • Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise ∗

    Ole E. Barndorff-Nielsen;Peter Reinhard Hansen;Asger Lunde;Neil Shephard

  • Statistical aspects of ARCH and stochastic volatility

    Neil Shephard

  • Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics

    Ole Eiler Barndorff-Nielsen;N. Shephard

  • STAMP 6.0 Structural Time Series Analyser, Modeller and Predictor

    S.J. Koopman;A.C. Harvey;J.A. Doornik;N. Shephard

  • Realized kernels in practice: trades and quotes

    O. E. Barndorff‐Nielsen;P. Reinhard Hansen;A. Lunde;N. Shephard

  • Markov chain Monte Carlo methods for stochastic volatility models

    Siddhartha Chib;Federico Nardari;Neil Shephard

  • Estimating quadratic variation using realized variance

    Ole E. Barndorff-Nielsen;Neil Shephard

  • Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading

    Ole E. Barndorff-Nielsen;Peter Reinhard Hansen;Asger Lunde;Neil Shephard

  • The simulation smoother for time series models

    Piet De Jong;Neil Shephard

  • Statistical algorithms for models in state space using SsfPack 2.2

    Siem Jan Koopman;Neil Shephard;Jurgen A. Doornik

  • Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns

    Andrew C. Harvey;Neil Shephard

  • LIKELIHOOD INFERENCE FOR DISCRETELY OBSERVED NONLINEAR DIFFUSIONS

    Ola Elerian;Siddhartha Chib;Neil Shephard

  • Stochastic volatility with leverage: Fast and efficient likelihood inference

    Yashuhiro Omori;Siddhartha Chib;Neil Shephard;Jouchi Nakajima

  • Stochastic volatility : selected readings

    Neil Shephard

  • Realising the future: forecasting with high‐frequency‐based volatility (HEAVY) models

    Neil Shephard;Kevin Sheppard

Frequent Co-Authors

Siem Jan Koopman
Siem Jan Koopman Vrije Universiteit Amsterdam
Andrew Harvey
Andrew Harvey University of Cambridge
Asger Lunde
Asger Lunde Copenhagen Economics
Peter Reinhard Hansen
Peter Reinhard Hansen University of North Carolina at Chapel Hill
Anna Vignoles
Anna Vignoles Leverhulme Trust
David F. Hendry
David F. Hendry University of Oxford
Lorraine Dearden
Lorraine Dearden University College London
Robert F. Engle
Robert F. Engle New York University
Per A. Mykland
Per A. Mykland University of Chicago
Arnaud Doucet
Arnaud Doucet University of Oxford

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