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Risk and Financial Management Article Systemic Risk Indicators Based on Nonlinear PolyModel

Author

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  • Xingxing Ye

    (SUNY - State University of New York)

  • Raphaël Douady

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, SUNY - State University of New York)

Abstract

The global financial market has become extremely interconnected as it demonstrates strong nonlinear contagion in times of crisis. As a result, it is necessary to measure financial systemic risk in a comprehensive and nonlinear approach. By establishing a large set of risk factors as the main bones of the financial market network and applying nonlinear factor analysis in the form of so-called PolyModel, this paper proposes two systemic risk indicators that can prognosticate the advent and trace the development of financial crises. Through financial network analysis, theoretical simulation, empirical data analysis and final validation, we argue that the indicators suggested in this paper are proved to be very effective in forecasting and tracing the financial crises from 1998 to 2017. The economic benefit of the indicator is evidenced by the enhancement of a protective put/covered call strategy on major stock markets.

Suggested Citation

  • Xingxing Ye & Raphaël Douady, 2019. "Risk and Financial Management Article Systemic Risk Indicators Based on Nonlinear PolyModel," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02488592, HAL.
    • Handle: RePEc:hal:cesptp:hal-02488592
    DOI: 10.3390/jrfm12010002
    Note: View the original document on HAL open archive server: https://hal.science/hal-02488592
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    Cited by:

    1. Raphaël Douady, 2019. "Managing the Downside of Active and Passive Strategies: Convexity and Fragilities," Post-Print hal-02488589, HAL.

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    Keywords

    Poly-Model; Systemic Risk; Nonlinear; Indicators; systemic risk crisis; financial indicator; network; nonlinear regression; PolyModel; validation;
    All these keywords.

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