Peer-reviewed publications and R&R

​​

1. Banulescu-Radu, D., Hansen, P.R., Huang, Z., Matei, M. (2023). Volatility During the Financial Crisis Through the Lens of High Frequency Data: A Realized GARCH Approach. R&R Journal of Financial Econometrics 

2. Banulescu-Radu,D., Kougblenou, Y., (2023). Data science for insurance fraud detection: a review. Forthcoming in Handbook of Insurance, Springer

3. Banulescu-Radu, D., Yankol-Schalck, M. (2023). Practical guideline to efficiently detect insurance fraud in the era of machine learning: a household insurance case. Journal of Risk and Insurance (published online Nov 27, 2023)

4. Banulescu-Radu, D., Giuliano, F. (2022). Nouvelles technologies d’Intelligence Artificielle pour la détection de la fraude et du blanchiment d’argent : avantages, limites et opportunités. Revue du Grasco, no. 36 

5. Banulescu-Radu, D., Hurlin, C., Leymarie, J., Scaillet, O. (2021). Backtesting marginal expected shortfall and related systemic risk measures. Management Science, 67(9), 5301-5967

6. Banulescu-Radu, D., Dumitrescu, E. (2019). Do High-frequency-based Measures Improve Conditional Covariance Forecasts? in Financial Mathematics, Volatility and Covariance Modelling, Routledge Advances in Applied Financial Econometrics Series, Volume 2, 261–285

7. Banulescu, D., Colletaz, G., Hurlin, C., Tokpavi, S. (2016). Forecasting High-Frequency Risk Measures. Journal of Forecasting, 35(3), 224-249

8. Banulescu-Radu, D., Hurlin, C., Candelon, B., Laurent, S. (2016). Do we need high frequency data to forecast variances? Annals of Economics and Statistics (123/124), 135-174

9. Banulescu, G. D., Dumitrescu, E. I. (2015). Which are the SIFIs? A Component Expected Shortfall approach to systemic risk. Journal of Banking and Finance, 50, 575-588 

Book chapters

 

1. Banulescu-Radu, D. (2022). L'intelligence artificielle, le machine learning et le deep learning: les défis de leur utilisation dans la détection de la fraude, in Cerveau(x) et droit - Neurodroit, algorithmes, intelligence artificielle, objets connectés, centres de décision, Lgdj 

2. Banulescu-Radu, D., Ferrara, L., Marsilli, C. (2020). Prévoir la volatilité d'un actif financier à l'aide d'un modèle à mélange de fréquences, in Méthodes de prévision en finance, Economica

Working papers

​

1. Banulescu-Radu, D., Benoit, S., Hurlin, C., Evaluating the Social Security Contribution Fraud

2. Baesens, B., Banulescu-Radu, D., Hurlin, C., Kougblenou, Y., Verdonck, T., Benchmarking state-of-the-art resampling techniques for classification models 

3. Banulescu-Radu, D., Dumitrescu, E., de Truchis, G., Long memory and power law coherency between realized volatility and trading volume 

4. Banulescu-Radu,D., Bouchegnies, L., D., Giuliano, F., Artificial intelligence and banking-as-a-service: two challenges in the fight against financial crime   

5. Banulescu-Radu, D., Hurlin, C., Kougblenou, Y., Machine Learning and cost sensitive learning for insurance fraud detection 

​

Miscellaneous

​

1. ​Banulescu-Radu, D. (2022).  Data Science et Détection de Fraude en Assurance, in Lettre du LEO 

2. Banulescu-Radu, D., Lacroix de Sousa, S. (2021). La lutte contre le blanchiment d’argent à l’ére du COVID-19, in Lettre du LEO 

​

​​​

​