A non-linear Lasso and explainable LSTM approach for estimating tail risk interconnectedness

De, Tuhin Subhra; Karthikeya, Madeti; Bhattacharya, Sujoy

Abstract

Tail risk inter-connectivity is a significant aspect and a risk indicator that should be focused on. Many of the previous works have shown potential non-linearity in tail risk contagion. With the recent advancements in deep learning, Long-Short Term Memory (LSTM) networks have played an important role in sequential data prediction. We experiment with LASSO-based neural networks and interpretative LSTM model along with other machine learning approaches for investigating tail risk interconnectedness among the public banks of Japan. We also investigate the risk reception from large overseas banks in United States finding that medium-sized banks are more likely to receive international risks. Our studies show that LSTM-based model is an excellent fit for the scenario and total connectedness goes up during an economic crisis. The banks having larger market capitalization are more prone to emission and reception of tail risks. This is accompanied by exhibiting the impact of some major economic distresses on Japanese banking system. These results provide important information to regulators and policy makers.

Citation

De, T. S., Karthikeya, M., & Bhattacharya, S. (online). A non-linear Lasso and explainable LSTM approach for estimating tail risk interconnectedness. Applied Economics, https://doi.org/10.1080/00036846.2024.2385747

• Journal Article Type: Article
• Online Publication Date: Aug 13, 2024
• Deposit Date: Sep 16, 2024
• Publicly Available Date: Sep 16, 2024
• Journal: Applied Economics
• Print ISSN: 0003-6846
• Electronic ISSN: 1466-4283
• Publisher: Routledge
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1080/00036846.2024.2385747
• Keywords: Tail risk, interconnectedness, machine learning, neural networks, LSTM