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The Journal of Financial Data Science

The Journal of Financial Data Science

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Dynamic Systemic Risk: Networks in Data Science

Sanjiv R. Das, Seoyoung Kim and Daniel N. Ostrov
The Journal of Financial Data Science Winter 2019, 1 (1) 141-158; DOI: https://doi.org/10.3905/jfds.2019.1.1.141
Sanjiv R. Das
is the William and Janice Terry professor of finance and data science at Santa Clara University in Santa Clara, CA
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Seoyoung Kim
is an associate professor of finance at Santa Clara University in Santa Clara, CA
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Daniel N. Ostrov
is a professor of mathematics at Santa Clara University in Santa Clara, CA
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Abstract

In this article, the authors propose a theory-driven framework for monitoring system-wide risk by extending data science methods widely deployed in social networks. Their approach extends the one-firm Merton credit risk model to a generalized stochastic network-based framework across all financial institutions, comprising a novel approach to measuring systemic risk over time. The authors identify four desired properties for any systemic risk measure. They also develop measures for the risks created by each individual institution and a measure for risk created by each pairwise connection between institutions. Four specific implementation models are then explored, and brief empirical examples illustrate the ease of implementation of these four models and show general consistency among their results.

TOPICS: Big data/machine learning, financial crises and financial market history, other

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The Journal of Financial Data Science
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Winter 2019
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Dynamic Systemic Risk: Networks in Data Science
Sanjiv R. Das, Seoyoung Kim, Daniel N. Ostrov
The Journal of Financial Data Science Jan 2019, 1 (1) 141-158; DOI: 10.3905/jfds.2019.1.1.141

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Dynamic Systemic Risk: Networks in Data Science
Sanjiv R. Das, Seoyoung Kim, Daniel N. Ostrov
The Journal of Financial Data Science Jan 2019, 1 (1) 141-158; DOI: 10.3905/jfds.2019.1.1.141
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  • Article
    • Abstract
    • CONTRAST WITH EXTANT APPROACHES
    • STOCHASTIC DYNAMICS IN A NETWORK MODEL
    • PRACTICAL VALUE OF THE MODEL
    • A GENERAL FRAMEWORK FOR SYSTEMIC RISK
    • THE INSTITUTION RISK MEASURE, CONNECTEDNESS, AND THE CONNECTEDNESS RISK MEASURE
    • FOUR FINANCIAL PROPERTIES
    • SYSTEMIC RISK NETWORK MODELS THAT ARE HOMOGENOUS IN DEFAULT RISKS
    • A SYSTEMIC RISK NETWORK MODEL THAT IS NOT HOMOGENOUS IN DEFAULT RISKS
    • DATA SOURCES AND DESCRIPTION OF VARIABLES
    • EMPIRICAL ILLUSTRATIONS
    • CONCLUDING COMMENTS
    • ACKNOWLEDGMENTS
    • APPENDIX
    • ENDNOTES
    • REFERENCES
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  • PDF (Subscribers Only)

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