A Deep Learning Approach for Dynamic Balance Sheet Stress Testing

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Tác giả: Sotirios Chatzis, Konstantinos P Panousis, Loukas Papadoulas, Anastasios Petropoulos, Vassilis Siakoulis

Ngôn ngữ: eng

Ký hiệu phân loại: 537.5 Electronics

Thông tin xuất bản: 2020

Mô tả vật lý:

Bộ sưu tập: Metadata

ID: 165246

 Comment: 3rd ACM International Conference on AI in FinanceIn 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.
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