ROMBOUTS Jeroen

Ph.D. In Econometrics, CORE, Universite Catholique de Louvain. Currently CORE research associate and Elected Member of the International Statistical Institute. Joined ESSEC in 2013 and formerly professor at HEC Montreal (2004-2012).

Professor Jeroen VK Rombouts joined ESSEC Business School in January 2013. He combines rich data sets with statistical tools to answer financial questions. His research outcomes are published in international peer reviewed journals. He teaches courses of Basic Statistics and Econometrics in the Master and PhD programs. He has been Visiting Scholar at several universities (University of Pittsburg, Tilburg University, Erasmus University Rotterdam, Aarhus University (CREATES) and CORE as a research associate among others). He is an expert consultant in financial econometrics and macro-economic forecasting. Prior to joining ESSEC Business School, Jeroen was Associate Professor at HEC Montreal (2004-2012)

• 2004 : Ph.D. en Econométrie (Université Catholique de Louvain, Belgique)
• 2001 : Master en Statistiques (Université Catholique de Louvain, Belgique)
• 2000 : Master en Econométrie (Université Catholique de Louvain, Belgique)
• 1999 : Master en Economie (Katholieke Universiteit Leuven, Belgique)

Positions académiques principales

• 2014 - présent : Professeur (ESSEC Business School, France)
• 2013 - 2014 : Professeur associé (ESSEC Business School, France)
• 2009 - 2012 : Professeur associé (HEC Montréal, Canada)
• 2004 - 2009 : Professeur assistant (HEC Montréal, Canada)

Autres positions académiques

• 2017 - 2023 : Professeur titulaire de la chaire Accenture Strategic Business Analytics (ESSEC Business School, France)
• 2017 - 2020 : Responsable du département Systèmes d’Information, Sciences de la Décision et Statistiques (ESSEC Business School, France)
• 2014 - présent : Chercheur au Finance and Insurance Lab (Centre de recherche en économie et statistique (CREST), France)
• 2015 - 2019 : Professeur visitant (Katholieke Universiteit Leuven, Belgique)
• 2015 : Professeur visitant (University of Melbourne, Australie)
• 2014 : Professeur visitant (CORE, Belgique)
• 2014 : Professeur visitant (CREATES, Aarhus University, Danemark)
• 2010 - 2011 : Professeur visitant (CORE, Belgique)
• 2010 : Professeur visitant (CREATES, Aarhus University, Danemark)
• 2010 : Professeur visitant (University of Melbourne, Australie)
• 2006 : Professeur visitant (Université de Pittsburgh, États-Unis)
• 2005 : Professeur visitant (University of California, San Diego, États-Unis)
• 2000 - 2004 : Assistant pédagogique, Département Economie (Université Catholique de Louvain, Belgique)
• 2003 : Professeur visitant (Erasmus Universiteit Rotterdam, Pays-Bas)
• 2003 : Professeur visitant (Tilburg University, School of Economics and Management, Pays-Bas)
• 1999 - 2000 : Assistant pédagogique, Institut de statistiques (Université Catholique de Louvain, Belgique)

• WILMS, I., ROMBOUTS, J. and CROUX, C. (2021). Multivariate volatility forecasts for stock market indices. International Journal of Forecasting, 37(2), pp. 484-499.
• DUFAYS, A., LI, Z., ROMBOUTS, J. and SONG, Y. (2021). Sparse change‐point VAR models. Journal of Applied Econometrics, 36(6), pp. 703-727.
• ROMBOUTS, J., VIOLANTE, F. and STENTOFT, L. (2020). Dynamics of variance risk premia: A new model for disentangling the price of risk. Journal of Econometrics, 217(2), pp. 312-334.
• DUFAYS, A. and ROMBOUTS, J. (2020). Relevant parameter changes in structural break models. Journal of Econometrics, 217(1), pp. 46-78.
• ROMBOUTS, J., VIOLANTE, F. and STENTOFT, L. (2020). Pricing Individual Stock Options Using both Stock and Market Index Information. Journal of Banking and Finance, 111.
• ROMBOUTS, J., STENTOFT, L. and VIOLANTE, F. (2020). Variance swap payoffs, risk premia and extreme market conditions. Econometrics and Statistics, 13, pp. 106-124.
• DUFAYS, A. and ROMBOUTS, J. (2019). Sparse Change-point HAR Models for Realized Variance. Econometric Reviews, 38.
• DELAIGLE, A., MEISTER, A. and ROMBOUTS, J. (2016). Root-T Consistent Density Estimation in GARCH Models. Journal of Econometrics, 192(1), pp. 55-63.
• ROMBOUTS, J. and STANTOFT, L. (2015). Option Pricing with Asymmetric Heteroskedastic Normal Mixture Models. International Journal of Forecasting, 31(3), pp. 635-650.
• BAUWENS, L., KOOP, G., KOROBILIS, D. and ROMBOUTS, J. (2015). The Contribution of Structural Break Models to Forecasting Macroeconomic Series. Journal of Applied Econometrics, 30(4), pp. 596-620.
• ROMBOUTS, J. and STENTOFT, L. (2014). Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models. Computational Statistics and Data Analysis, 76, pp. 588-605.
• BAUWENS, L., DUFAYS, A. and ROMBOUTS, J. (2014). Marginal Likelihood for Markov-switching and Change-Point GARCH Models. Journal of Econometrics, 178(3), pp. 508-522.
• ROMBOUTS, J., STENTOFT, L. and VIOLANTE, F. (2014). The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options. International Journal of Forecasting, 30(1), pp. 78-98.
• LAURENT, G., ROMBOUTS, J. and VIOLANTE, F. (2013). On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models. Journal of Econometrics, 173(1), pp. 1-10.
• BOUEZMARNI, T., ROMBOUTS, J. and TAAMOUTI, A. (2012). Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality. Journal of Business and Economic Statistics, 30(2), pp. 275-287.
• BAUWENS, L. and ROMBOUTS, J. (2012). On Marginal Likelihood Computation in Change-Point Models. Computational Statistics and Data Analysis, 56(11), pp. 3415-3429.
• LAURENT, S., ROMBOUTS, J. and VIOLANTE, F. (2012). On the forecasting accuracy of multivariate GARCH models. Journal of Applied Econometrics, 27(6), pp. 934-955.
• ROMBOUTS, J. and STENTOFT, L. (2011). Multivariate Option Pricing with Time Varying Volatility and Correlations. Journal of Banking and Finance, 35(9), pp. 2267-2281.
• BOUEZMARNI, T., ROMBOUTS, J. and TAAMOUTI, A. (2010). Asymptotic properties of the Bernstein density copula estimator for α-mixing data. Journal of Multivariate Analysis, 101(1), pp. 1-10.
• BOUEZMARNI, T. and ROMBOUTS, J. (2010). Nonparametric Density Estimation for Multivariate Bounded Data. Journal of Statistical Planning and Inference, 140(1), pp. 139-152.
• BOUEZMARNI, T. and ROMBOUTS, J. (2010). Nonparametric Density Estimation for Positive Time Series. Computational Statistics and Data Analysis, 54(2), pp. 245-261.
• BAUWENS, L., PREMINGER, A. and ROMBOUTS, J. (2010). Theory and Inference for a Markov Switching GARCH Model. Econometrics Journal, 13(2), pp. 218-244.
• VERBEEK, M. and ROMBOUTS, J. (2009). Evaluating portfolio Value-at-Risk using semi-parametric GARCH models. Quantitative Finance, 9(6), pp. 737-745.
• BOUADDI, M. and ROMBOUTS, J. (2009). Mixed Exponential Power Asymmetric Conditional Heteroskedasticity. Studies in Nonlinear Dynamics and Econometrics, 13(3), pp. 1-30.
• BOUEZMARNI, T. and ROMBOUTS, J. (2009). Semiparametric Multivariate Density Estimation for Positive Data Using Copulas. Computational Statistics and Data Analysis, 53(6), pp. 2040-2054.
• BOUEZMARNI, T. and ROMBOUTS, J. (2008). Density and hazard rate estimation for censored and α-mixing data using gamma kernels. Journal of Nonparametric Statistics, 20(7), pp. 627-643.
• BAUWENS, L. and ROMBOUTS, J. (2007). Bayesian Clustering of Many GARCH Models. Econometric Reviews, 26(2), pp. 365-386.
• BAUWENS, L. and ROMBOUTS, J. (2007). Bayesian Inference for the Mixed Conditional Heteroskedasticity Model. Econometrics Journal, 10(2), pp. 408-425.
• HAFNER, C. and ROMBOUTS, J. (2007). Estimation of Temporally Aggregated Multivariate GARCH Models. Journal of Statistical Computation and Simulation, 77(8), pp. 629-650.
• BAUWENS, L., HAFNER, C. and ROMBOUTS, J. (2007). Multivariate mixed normal conditional heteroskedasticity. Computational Statistics and Data Analysis, 51(7), pp. 3551-3566.
• HAFNER, C. and ROMBOUTS, J. (2007). Semiparametric Multivariate Volatility Models. Econometric Theory, 23(2), pp. 251-280.
• LAURENT, S., BAUWENS, L. and ROMBOUTS, J. (2006). Multivariate GARCH Models: A Survey. Journal of Applied Econometrics, 21(1), pp. 79-109.
• MOUCHART, M. and ROMBOUTS, J. (2005). Clustered Panel data models: An Efficient Approach for Nowcasting from Poor Data. International Journal of Forecasting, 21, pp. 577-594.

• BAUWENS, L. and ROMBOUTS, J. (2004). Econometrics. In: Handbook of Computational Statistics. 1st ed. Springer, pp. 951-980.

• ROMBOUTS, J., SCAILLET, O., VEREDAS, D. and ZAKOIAN, J.M. (2019). Nonlinear financial econometrics - JOE Special Issue. Journal of Econometrics.

• ROMBOUTS, J., CROUX, C. and WILMS, I. (2019). Multivariate lasso-based Forecast Combinations for stock market Volatility. In: 2019 3rd International Conference on Econometrics and Statistics.
• ROMBOUTS, J. (2018). Relevant Parameter Changes in Structural Break Models. In: 2018 Econometric Theory and Time Series Analysis (ETTSA) Workshop.
• ROMBOUTS, J. (2013). Fast Density Estimation in Graph Models. In: CIREQ Econometrics Conference: Time Series and Financial Econometrics.
• ROMBOUTS, J. and STENTOFT, L. (2013). Mixtures Models, Jumps and Option Pricing. In: 33rd International Symposium on Forecasting.
• ROMBOUTS, J. (2013). The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options. In: 30th International Conference of the French Finance Association.

• ROMBOUTS, J. and AMICHIA, R. (2021). Why Are AI Use Cases Not Going Live? MLOPS Bring an Answer. ESSEC Knowledge.
• ROMBOUTS, J. (2021). The Experience Game-Changer. ESSEC Knowledge.