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Learning Financial Networks using Quantile Granger Causality

Published: 15 June 2018 Publication History

Abstract

In the post-crisis era, financial regulators and policymakers require data-driven tools to quantify systemic risk and to identify systemically important firms. We propose a statistical method that measures connectivity in the financial sector using time series of firms' stock returns. Our method is based on system-wide lower-tail analysis, whereby we estimate linkages between firms that occur when those firms are distressed and that exist conditional on the financial information of all other firms in the sample. This is achieved using Lasso-penalized quantile vector autoregression. By considering centrality measures of the estimated networks, we can assess the build-up of systemic risk and identify risk propagation channels. We apply our method to monthly returns of large U.S. firms, demonstrating that we are able to detect many of the most recent systemic events, in addition to identifying key players in the 2007-2009 U.S. financial crisis. Importantly, these players are not identified using standard Granger causality, which estimates connectivity by averaging across good, bad, and normal days of the market.

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S. Basu, S. Das, G. Michailidis, and A. K. Purnanandam. A system-wide approach to measure connectivity in the financial sector. Available at SSRN 2816137.
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M. Billio, M. Getmansky, A. W. Lo, and L. Pelizzon. Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economies, 104(3):535--559, 2012.
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S. R. Das. Matrix metrics: Network-based systemic risk scoring. The Journal of Alternative Investments, 18(4):33--51, 2016.
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  • (2021)Mixed effect modelling and variable selection for quantile regressionStatistical Modelling10.1177/1471082X21103349023:1(53-80)Online publication date: 23-Aug-2021
  1. Learning Financial Networks using Quantile Granger Causality

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    cover image ACM Conferences
    DSMM'18: Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets
    June 2018
    66 pages
    ISBN:9781450358835
    DOI:10.1145/3220547
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 15 June 2018

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    Author Tags

    1. Granger causality
    2. Lasso
    3. quantile regression
    4. systemic risk
    5. vector autoregression

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    DSMM'18 Paper Acceptance Rate 14 of 17 submissions, 82%;
    Overall Acceptance Rate 32 of 64 submissions, 50%

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    • (2021)Mixed effect modelling and variable selection for quantile regressionStatistical Modelling10.1177/1471082X21103349023:1(53-80)Online publication date: 23-Aug-2021

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