| Opening the black box–Quantile neural networks for loss given default prediction R Kellner, M Nagl, D Rösch Journal of Banking & Finance 134, 106334, 2022 | 39 | 2022 |
| Introducing an interpretable deep learning approach to domain-specific dictionary creation: A use case for conflict prediction S Häffner, M Hofer, M Nagl, J Walterskirchen Political Analysis 31 (4), 481-499, 2023 | 12 | 2023 |
| Parameter estimation, bias correction and uncertainty quantification in the Vasicek credit portfolio model M Pfeuffer, M Nagl, M Fischer, D Rösch Journal of Risk 22 (4), 1-29, 2020 | 12 | 2020 |
| Deep calibration of financial models: turning theory into practice P Büchel, M Kratochwil, M Nagl, D Rösch Review of Derivatives Research 25 (2), 109-136, 2022 | 7 | 2022 |
| Quantifying uncertainty of machine learning methods for loss given default M Nagl, M Nagl, D Rösch Frontiers in Applied Mathematics and Statistics 8, 1076083, 2022 | 4 | 2022 |
| Does non-linearity in risk premiums vary over time? M Nagl Available at SSRN 4638168, 2023 | 3 | 2023 |
| Time Varying Dependences Between Real Estate Crypto, Real Estate and Crypto Returns C Nagl, M Nagl, D Rösch, W Schäfers, J Freybote Journal of Real Estate Research, 1-29, 2023 | 2 | 2023 |
| Credit line exposure at default modelling using Bayesian mixed effect quantile regression J Betz, M Nagl, D Rösch Journal of the Royal Statistical Society Series A: Statistics in Society 185 …, 2022 | 2 | 2022 |
| Intricacy of cryptocurrency returns M Nagl Economics Letters 239, 111746, 2024 | | 2024 |
| Dynamics of REIT Returns and Volatility: Analyzing Time-Varying Drivers Using an Explainable Machine Learning Approach H Jenett, M Nagl, C Nagl, MK Price, W Schäfers ERES, 2024 | | 2024 |
| Virtual Land as a New Asset Class: Examining the Relationship with Physical Real Estate H Leonhard, M Nagl, W Schäfers Available at SSRN 4567859, 2023 | | 2023 |
| Determinants of US REIT Bond Risk Premia with Explainable Machine Learning J Kozak, M Nagl, C Nagl, E Beracha, W Schäfers ERES, 2023 | | 2023 |
| Statistical and machine learning for credit and market risk management M Nagl | | 2022 |
| Estimating Asset Correlations from Default Data M Nagl, Y Havrylenko, M Pfeuffer, K Jakob, M Fischer, D Roesch | | 2018 |
| Package ‘AssetCorr’ M Nagl, Y Havrylenko, MM Nagl | | 2018 |
| Non-linearity and the distribution of market based loss rates M Nagl, M Nagl, D Rösch | | |
