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Systemic Risk Modeling with Expectile Regression Neural Network and Modified LASSO

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  • Wisnowan Hendy Saputra

    (Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
    Department of Computer Science, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia)

  • Dedy Dwi Prastyo

    (Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia)

  • Kartika Fithriasari

    (Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia)

Abstract

Traditional risk models often fail to capture extreme losses in interconnected global stock markets. This study introduces a novel approach, Expectile Regression Neural Network with Modified LASSO regularization (ERNN-mLASSO), to model nonlinear systemic risk. By analyzing five major stock indices (JKSE, GSPC, GDAXI, FTSE, N225), we identify distinct market roles: developed markets, such as the GSPC, act as risk spreaders, while emerging markets, like the JKSE, act as risk takers. Our network systemic risk index, SNRI, accurately captures systemic shocks during the COVID-19 crisis. More importantly, the model projects increasing global financial fragility through 2025, providing an early warning signal for policymakers and risk managers of potential future instability.

Suggested Citation

  • Wisnowan Hendy Saputra & Dedy Dwi Prastyo & Kartika Fithriasari, 2025. "Systemic Risk Modeling with Expectile Regression Neural Network and Modified LASSO," JRFM, MDPI, vol. 18(11), pages 1-28, October.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:11:p:593-:d:1776861
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