Quantitative Statistical Robustness for Tail-Dependent Law Invariant Risk Measures

no code implementations • 28 Jun 2020 • Wei Wang, Huifu Xu, Tiejun Ma

When estimating the risk of a financial position with empirical data or Monte Carlo simulations via a tail-dependent law invariant risk measure such as the Conditional Value-at-Risk (CVaR), it is important to ensure the robustness of the statistical estimator particularly when the data contain noise.

Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting

no code implementations • 14 Dec 2018 • Yaodong Yang, Alisa Kolesnikova, Stefan Lessmann, Tiejun Ma, Ming-Chien Sung, Johnnie E. V. Johnson

The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over machine learning and rule-based benchmarks.

BIG-bench Machine Learning Feature Engineering +1