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Found 4 papers, 0 papers with code
Multivariate Systemic Risk Measures and Computation by Deep Learning Algorithms
In this work we propose deep learning-based algorithms for the computation of systemic shortfall risk measures defined via multivariate utility functions.
Deep Learning for Systemic Risk Measures
The aim of this paper is to study a new methodological framework for systemic risk measures by applying deep learning method as a tool to compute the optimal strategy of capital allocations.
Systemic Risk Models for Disjoint and Overlapping Groups with Equilibrium Strategies
We introduce the concept of Nash equilibrium for these new models, and analyze the optimal solution under Gaussian distribution of the risk factor.
Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner.
