What is he best known for?
The fields of study he is best known for:
- Statistics
- Econometrics
- Finance
His main research concerns Econometrics, Autoregressive conditional heteroskedasticity, Volatility, Multivariate statistics and Autoregressive conditional duration.
His Econometrics study combines topics in areas such as Market microstructure, Statistical inference and Bayesian inference.
His Bayesian inference study incorporates themes from Bayes estimator, Monte Carlo method, Applied mathematics and Gibbs sampling.
His Autoregressive conditional heteroskedasticity research includes elements of Univariate, Valuation of options, Importance sampling, Degrees of freedom and Regression analysis.
His research in Volatility intersects with topics in Foreign exchange market and Liberian dollar.
He combines subjects such as Heteroscedasticity, Multivariate garch, Multivariate garch model, Inference and Skewness with his study of Multivariate statistics.
His most cited work include:
- Multivariate GARCH models: a survey (1367 citations)
- Bayesian Inference in Dynamic Econometric Models (397 citations)
- The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks (268 citations)
What are the main themes of his work throughout his whole career to date?
Luc Bauwens focuses on Econometrics, Bayesian inference, Volatility, Autoregressive conditional heteroskedasticity and Bayesian probability.
In his study, Skewness is strongly linked to Multivariate statistics, which falls under the umbrella field of Econometrics.
The Bayesian inference study combines topics in areas such as Marginal likelihood, Valuation of options, Markov chain Monte Carlo, Applied mathematics and Conditional expectation.
His work on Implied volatility as part of general Volatility research is frequently linked to Multiplicative function, bridging the gap between disciplines.
His work carried out in the field of Autoregressive conditional heteroskedasticity brings together such families of science as Value at risk and Univariate.
His Bayesian probability research includes themes of Econometric model, Regression analysis and Inference.
He most often published in these fields:
- Econometrics (60.00%)
- Bayesian inference (19.47%)
- Volatility (18.95%)
What were the highlights of his more recent work (between 2012-2020)?
- Econometrics (60.00%)
- Applied mathematics (13.16%)
- Covariance (7.37%)
In recent papers he was focusing on the following fields of study:
Luc Bauwens mainly focuses on Econometrics, Applied mathematics, Covariance, Volatility and Multiplicative function.
His research integrates issues of Hidden Markov model and Bayesian inference in his study of Econometrics.
His Applied mathematics study integrates concerns from other disciplines, such as Smoothing, Dynamic factor and Laplace's method.
His research on Covariance also deals with topics like
- Wishart distribution which connect with Estimator,
- Algorithm that connect with fields like Inference and Monte Carlo method,
- Covariance matrix that connect with fields like Conditional variance and Sample.
He has researched Volatility in several fields, including Minimum variance portfolio and Markov chain.
His Autoregressive conditional heteroskedasticity research is multidisciplinary, incorporating elements of Marginal likelihood, Path dependence, Computation and Multivariate statistics.
Between 2012 and 2020, his most popular works were:
- Marginal likelihood for Markov-switching and change-point GARCH models (53 citations)
- The Contribution of Structural Break Models to Forecasting Macroeconomic Series (43 citations)
- MULTIVARIATE VOLATILITY MODELING OF ELECTRICITY FUTURES (41 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Econometrics
- Finance
His primary scientific interests are in Econometrics, Volatility, Multiplicative function, Bayesian inference and Covariance.
His research integrates issues of Futures contract and Hidden Markov model in his study of Econometrics.
As part of one scientific family, Luc Bauwens deals mainly with the area of Hidden Markov model, narrowing it down to issues related to the Dirichlet process, and often Applied mathematics.
His work in the fields of Autoregressive conditional heteroskedasticity overlaps with other areas such as Empirical evidence.
The study incorporates disciplines such as Marginal likelihood, Path dependence, Computation and Markov chain in addition to Autoregressive conditional heteroskedasticity.
His Covariance research incorporates themes from Mathematical optimization and Minimum-variance unbiased estimator.
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