Details about Julien Hambuckers

Access statistics for papers by Julien Hambuckers.

Last updated 2021-04-19. Update your information in the RePEc Author Service.

Short-id: pha1318

Working Papers

2019

1. An improved approach for estimating large losses in insurance analytics and operational risk using the g-and-h distribution
   DEM Working Papers, Department of Economics and Management

2018

2. Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach
   DEM Working Papers, Department of Economics and Management View citations (1)
   See also Journal Article Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach, Quantitative Finance, Taylor & Francis Journals (2019) View citations (2) (2019)
3. LASSO-Type Penalization in the Framework of Generalized Additive Models for Location, Scale and Shape
   Working Papers, Faculty of Economics and Statistics, Universität Innsbruck View citations (1)
   See also Journal Article LASSO-type penalization in the framework of generalized additive models for location, scale and shape, Computational Statistics & Data Analysis, Elsevier (2019) View citations (9) (2019)

2017

4. A robust statistical approach to select adequate error distributions for financial returns
   LIDAM Reprints ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) View citations (1)
   See also Journal Article A robust statistical approach to select adequate error distributions for financial returns, Journal of Applied Statistics, Taylor & Francis Journals (2017) View citations (1) (2017)

2016

5. Estimating the out-of-sample predictive ability of trading rules: a robust bootstrap approach
   LIDAM Reprints ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)
   See also Journal Article Estimating the Out‐of‐Sample Predictive Ability of Trading Rules: A Robust Bootstrap Approach, Journal of Forecasting, John Wiley & Sons, Ltd. (2016) (2016)

2014

6. A new methodological approach for error distributions selection in Finance
   LIDAM Discussion Papers ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Journal Articles

2021

1. Urban low emissions zones: A behavioral operations management perspective
   Transportation Research Part A: Policy and Practice, 2021, 144, (C), 222-240 View citations (9)

2019

2. Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach
   Quantitative Finance, 2019, 19, (8), 1255-1266 View citations (2)
   See also Working Paper Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach, DEM Working Papers (2018) View citations (1) (2018)
3. LASSO-type penalization in the framework of generalized additive models for location, scale and shape
   Computational Statistics & Data Analysis, 2019, 140, (C), 59-73 View citations (9)
   See also Working Paper LASSO-Type Penalization in the Framework of Generalized Additive Models for Location, Scale and Shape, Working Papers (2018) View citations (1) (2018)

2018

4. A Markov-switching generalized additive model for compound Poisson processes, with applications to operational loss models
   Quantitative Finance, 2018, 18, (10), 1679-1698 View citations (5)
5. Understanding the economic determinants of the severity of operational losses: A regularized generalized Pareto regression approach
   Journal of Applied Econometrics, 2018, 33, (6), 898-935 View citations (13)

2017

6. A robust statistical approach to select adequate error distributions for financial returns
   Journal of Applied Statistics, 2017, 44, (1), 137-161 View citations (1)
   See also Working Paper A robust statistical approach to select adequate error distributions for financial returns, LIDAM Reprints ISBA (2017) View citations (1) (2017)

2016

7. Estimating the Out‐of‐Sample Predictive Ability of Trading Rules: A Robust Bootstrap Approach
   Journal of Forecasting, 2016, 35, (4), 347-372
   See also Working Paper Estimating the out-of-sample predictive ability of trading rules: a robust bootstrap approach, LIDAM Reprints ISBA (2016) (2016)