What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Probability distribution
Neil Shephard mainly investigates Econometrics, Stochastic volatility, Volatility, Realized variance and Quadratic variation.
Neil Shephard is interested in Autoregressive conditional heteroskedasticity, which is a branch of Econometrics.
His research integrates issues of Mathematical economics and Stochastic modelling in his study of Stochastic volatility.
His research in Volatility intersects with topics in Probability theory and Autoregressive model.
His studies in Realized variance integrate themes in fields like Analysis of covariance, Estimator, Asymptotic distribution and Sample.
Neil Shephard combines subjects such as Market liquidity and Semimartingale with his study of Quadratic variation.
His most cited work include:
- Filtering via Simulation: Auxiliary Particle Filters (1937 citations)
- STOCHASTIC VOLATILITY : LIKELIHOOD INFERENCE AND COMPARISON WITH ARCH MODELS (1686 citations)
- Econometric analysis of realized volatility and its use in estimating stochastic volatility models (1669 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Econometrics, Stochastic volatility, Volatility, Applied mathematics and Estimator.
The Realized variance research Neil Shephard does as part of his general Econometrics study is frequently linked to other disciplines of science, such as Financial econometrics, therefore creating a link between diverse domains of science.
His Stochastic volatility research incorporates themes from Implied volatility, Leverage and Markov chain Monte Carlo.
His work on Autoregressive conditional heteroskedasticity as part of his general Volatility study is frequently connected to Context, thereby bridging the divide between different branches of science.
His Applied mathematics research incorporates elements of Kalman filter and Expectation–maximization algorithm.
His Asymptotic distribution, Delta method and Consistent estimator study in the realm of Estimator interacts with subjects such as Rate of convergence.
He most often published in these fields:
- Econometrics (57.43%)
- Stochastic volatility (38.15%)
- Volatility (26.10%)
What were the highlights of his more recent work (between 2011-2020)?
- Econometrics (57.43%)
- Inference (12.85%)
- Nonparametric statistics (5.22%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Econometrics, Inference, Nonparametric statistics, Covariance and Applied mathematics.
Neil Shephard specializes in Econometrics, namely Stochastic volatility.
His Stochastic volatility study combines topics from a wide range of disciplines, such as Kalman filter and Expectation–maximization algorithm.
His research in Inference intersects with topics in Heteroscedasticity, Linear regression, Quantile regression, Bayesian probability and Algorithm.
In Covariance, Neil Shephard works on issues like Asset allocation, which are connected to Quasi-maximum likelihood, Key, Risk management and Sharpe ratio.
His work on Semimartingale is typically connected to Orders of magnitude as part of general Applied mathematics study, connecting several disciplines of science.
Between 2011 and 2020, his most popular works were:
- Multivariate high‐frequency‐based volatility (HEAVY) models (136 citations)
- How English domiciled graduate earnings vary with gender, institution attended, subject and socio-economic background (60 citations)
- Integer-valued Lévy processes and low latency financial econometrics (51 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Finance
Econometrics, Covariance, Volatility, Stochastic volatility and Estimator are his primary areas of study.
His study in Econometrics is interdisciplinary in nature, drawing from both Outcome and Futures contract.
His Covariance study also includes fields such as
- Quasi-likelihood that intertwine with fields like Market microstructure, Mathematical optimization and Estimation of covariance matrices,
- Asset allocation which connect with Bivariate analysis, Realized variance, Univariate and Moving average.
His studies deal with areas such as Inference, Multivariate statistics, Semimartingale and Quadratic variation as well as Volatility.
He has researched Quadratic variation in several fields, including Implied volatility, Forward volatility and Variance.
His Stochastic volatility study combines topics in areas such as Kalman filter and Expectation–maximization algorithm.
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