Details about Julien Hambuckers

Access statistics for papers by Julien Hambuckers.

Last updated 2025-09-09. Update your information in the RePEc Author Service.

Short-id: pha1318

Working Papers

2024

1. Nonstandard Errors
   Post-Print, HAL View citations (2)
   Also in Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers), HAL (2024)
   Working Papers, Faculty of Economics and Statistics, Universität Innsbruck (2021) View citations (6)
   Working Papers, Lund University, Department of Economics (2021)
   LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library (2024) View citations (2)
   Post-Print, HAL (2024)
   See also Journal Article Nonstandard Errors, Journal of Finance, American Finance Association (2024) View citations (2) (2024)

2023

2. EFFICIENT ESTIMATION IN EXTREME VALUE REGRESSION MODELS OF HEDGE FUND TAIL RISKS
   Working Papers, HAL
   Also in Papers, arXiv.org (2023)
3. Measuring tail risk at high-frequency: An $L_1$-regularized extreme value regression approach with unit-root predictors
   Papers, arXiv.org

2019

4. 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

5. 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)
6. 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 (11) (2019)

2017

7. 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

8. 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

9. 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

2024

1. Nonstandard Errors
   Journal of Finance, 2024, 79, (3), 2339-2390 View citations (2)
   See also Working Paper Nonstandard Errors, Post-Print (2024) View citations (2) (2024)
2. Using the softplus function to construct alternative link functions in generalized linear models and beyond
   Statistical Papers, 2024, 65, (5), 3155-3180 View citations (1)

2023

3. On the role of interest rate differentials in the dynamic asymmetry of exchange rates
   Economic Modelling, 2023, 129, (C) View citations (1)
4. Smooth-Transition Regression Models for Non-Stationary Extremes
   Journal of Financial Econometrics, 2023, 21, (2), 445-484

2022

5. Do interest rate differentials drive the volatility of exchange rates? Evidence from an extended stochastic volatility model
   Journal of Empirical Finance, 2022, 65, (C), 125-148 View citations (4)
6. Extremal connectedness of hedge funds
   Journal of Applied Econometrics, 2022, 37, (5), 988-1009 View citations (3)

2021

7. Estimating large losses in insurance analytics and operational risk using the g-and-h distribution
   Quantitative Finance, 2021, 21, (7), 1207-1221 View citations (5)
8. Testing a parameter restriction on the boundary for the g-and-h distribution: a simulated approach
   Computational Statistics, 2021, 36, (3), 2177-2200
9. Urban low emissions zones: A behavioral operations management perspective
   Transportation Research Part A: Policy and Practice, 2021, 144, (C), 222-240 View citations (10)

2019

10. 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)
11. 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 (11)
    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

12. 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)
13. 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

14. 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

15. 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)

Undated

16. Modeling multivariate operational losses via copula-based distributions with g-and-h marginals
    Journal of Operational Risk