  In the aftermath of the financial crisis, supervisory authorities have
considerably altered the mode of operation of financial stress testing. Despite
these efforts, significant concerns and extensive criticism have been raised by
market participants regarding the considered unrealistic methodological
assumptions and simplifications. Current stress testing methodologies attempt
to simulate the risks underlying a financial institution's balance sheet by
using several satellite models. This renders their integration a really
challenging task, leading to significant estimation errors. Moreover, advanced
statistical techniques that could potentially capture the non-linear nature of
adverse shocks are still ignored. This work aims to address these criticisms
and shortcomings by proposing a novel approach based on recent advances in Deep
Learning towards a principled method for Dynamic Balance Sheet Stress Testing.
Experimental results on a newly collected financial/supervisory dataset,
provide strong empirical evidence that our paradigm significantly outperforms
traditional approaches; thus, it is capable of more accurately and efficiently
simulating real world scenarios.
