Theoretical Analysis on Detection of Major Change in Financial Systems

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  • NAGATA Motoki
    東京大学大学院　情報理工学系研究科
  • AIHARA Kazuyuki
    東京大学生産技術研究所　情報・エレクトロニクス系部門

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Other Title
  • 金融システムにおける大変動の検出に関する理論解析

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Abstract

<p>Since crashes of financial bubbles cause damage to our society, it is important to predict the crashes and take necessary actions. The dynamical network marker can be applied to such real-time precursor detection in multivariate time series data of financial systems. If we do not know the mathematical model of the time series data, we have to choose the dominant group heuristically. We propose two methods to choose the dominant group. We compare the above method with the other methods based on the Koopman mode analysis （KMA）and we propose two methods that overcome the drawback of KMA. We test these methods in stock market data.</p>

Journal

  • SEISAN KENKYU

    SEISAN KENKYU 69 (3), 165-170, 2017

    Institute of Industrial Science The University of Tokyo

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