Distributionally robust risk evaluation with a causality constraint and
structural information
- URL: http://arxiv.org/abs/2203.10571v3
- Date: Mon, 10 Apr 2023 01:46:30 GMT
- Title: Distributionally robust risk evaluation with a causality constraint and
structural information
- Authors: Bingyan Han
- Abstract summary: A set of alternative measures is characterized by the causal optimal transport.
We approximate test functions by neural networks and prove the sample complexity with Rademacher complexity.
Empirical analysis of realized volatility and stock indices demonstrates that our framework offers an attractive alternative to the classic optimal transport formulation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work studies distributionally robust evaluation of expected function
values over temporal data. A set of alternative measures is characterized by
the causal optimal transport. We prove the strong duality and recast the
causality constraint as minimization over an infinite-dimensional test function
space. We approximate test functions by neural networks and prove the sample
complexity with Rademacher complexity. Moreover, when structural information is
available to further restrict the ambiguity set, we prove the dual formulation
and provide efficient optimization methods. Empirical analysis of realized
volatility and stock indices demonstrates that our framework offers an
attractive alternative to the classic optimal transport formulation.
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