Publications and communications of Julien Hambuckers

Hambuckers, J., & Ulm, M. (December 2023). On the role of interest rate differentials in the dynamic asymmetry of exchange rates. Economic Modelling, 129.

Crucil, R., Hambuckers, J., & Maxand Simone. (08 May 2023). Do monetary policy shocks affect financial uncertainty? A non-Gaussian proxy SVAR approach [Paper presentation]. International Francqui Chair: Causal Inference in Macroeconomics.

Hambuckers, J., & Kneib, T. (2023). Smooth-transition regression models for non-stationary extremes. Journal of Financial Econometrics, 21 (2), 445-484.

Wiemann, P., Kneib, T., & Hambuckers, J. (2023). Using the softplus function to construct alternative link functions in generalized linear models and beyond. Statistical Papers. doi:10.1007/s00362-023-01509-x

Mhalla, L., Hambuckers, J., & Lambert, M. (2022). Extremal connectedness of hedge funds. Journal of Applied Econometrics, 37 (5), 988-1009. doi:10.1002/jae.2900

Ulm, M., & Hambuckers, J. (2022). Do interest rate differentials drive the volatility of exchange rates? Evidence from an extended stochastic volatility model. Journal of Empirical Finance, 65, 125-148. doi:10.1016/j.jempfin.2021.12.004

Bee, M., Hambuckers, J., & Trapin, L. (2021). Estimating large losses in insurance analytics and operational risk using the g-and-h distribution. Quantitative Finance, 21 (7), 1207-1221. doi:10.1080/14697688.2020.1849778

Lurkin, V., Hambuckers, J., & Van Woensel, T. (2021). Urban Low Emissions Zones: A Behavioral Operations Management Perspective. Transportation Research. Part A, Policy and Practice, 144, 222-240. doi:10.1016/j.tra.2020.11.015

Hambuckers, J., & Ulm, M. (2020). Interest rate differentials and the dynamic asymmetry of exchange rates. ORBi-University of Liège.

Groll, A., Hambuckers, J., Kneib, T., & Umlauf, N. (December 2019). LASSO-Type Penalization in the Framework of Generalized Additive Models for Location, Scale and Shape. Computational Statistics and Data Analysis, 140, 59-74. doi:10.1016/j.csda.2019.06.005

Bee, M., Hambuckers, J., & Trapin. (2019). Estimating Value-at-Risk for the g-and-h distribution: an indirect inference approach. Quantitative Finance. doi:10.1080/14697688.2019.1580762

Hambuckers, J., & Kneib, T. (2019). Operational risk, uncertainty, and the economy: a smooth transition extreme value approach. ORBi-University of Liège.

Heymann, E. W., Culot, L., Knogge, C., Smith, A. C., Tirado Herrera, E. R., Stojan-Dolar, M., Ferrer, Y. L., Kubisch, P., Kupsch, D., Slana, D., Koopmann, M. L., Ziegenhagen, B., Bialozyt, R., Mengel, C., Hambuckers, J., & Heer, K. (2019). Small Neotropical primates promote the natural regeneration of anthropogenically disturbed areas. Scientific Reports, 9. doi:10.1038/s41598-019-46683-x

Peckre, L., Fabre, A.-C., Hambuckers, J., Wall, C., Socias-Martinez, L., & Pouydebat, E. (2019). Food properties influence grasping strategies in strepsirrhines. Biological Journal of the Linnean Society. doi:10.1093/biolinnean/bly215

Hambuckers, J., Groll, A., & Kneib, T. (2018). Understanding the Economic Determinants of the Severity of Operational Losses: A regularized Generalized Pareto Regression Approach. Journal of Applied Econometrics, 33 (6), 898-935. doi:10.1002/jae.2638

Hambuckers, J., Kneib, T., Langrock, R., & Silbersdorff, A. (2018). A Markov-switching Generalized additive model for compound Poisson processes, with applications to operational losses models. Quantitative Finance, 18 (10), 1679-1698. doi:10.1080/14697688.2017.1417625

Hambuckers, J., & Heuchenne, C. (2017). A robust statistical approach to select adequate error distributions for financial returns. Journal of Applied Statistics, 44 (1), 137-161. doi:10.1080/02664763.2016.1165803

Hambuckers, J., & Heuchenne, C. (July 2016). Estimating the out-of-sample predictive ability of trading rules: a robust bootstrap approach. Journal of Forecasting, 35 (4), 347-372. doi:10.1002/for.2380

Hambuckers, J., & Heuchenne, C. (2014). A new methodological approach for error distributions selection in Finance. (Submitted version 30/04/2014). ORBi-University of Liège.

Hambuckers, J. (2011). Modélisation d'évènements rares à l'aide de distributions non normales : application en finance avec la fonction sinh-arcsinh [Master’s dissertation, ULiège - Université de Liège]. ORBi-University of Liège.