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Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity

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  • Etesami, Jalal
  • Habibnia, Ali
  • Kiyavash, Negar

Abstract

Financial instability and its destructive effects on the economy can lead to financial crises due to its contagion or spillover effects to other parts of the economy. Having an accurate measure of systemic risk gives central banks and policy makers the ability to take proper policies in order to stabilize financial markets. Much work is currently being undertaken on the feasibility of identifying and measuring systemic risk. In principle, there are two main schemes to measure interlinkages between financial institutions. One might wish to construct a mathematical model of financial market participant relations as a network/graph by using a combination of information extracted from financial statements like the market value of liabilities of counterparties, or an econometric model to estimate those relations based on financial series. In this paper, we develop a data-driven econometric framework that promotes an understanding of the relationship between financial institutions using a nonlinearly modified Granger-causality network. Unlike existing literature, it is not focused on a linear pairwise estimation. The method allows for nonlinearity and has predictive power over future economic activity through a time-varying network of relationships. Moreover, it can quantify the interlinkages between financial institutions. We also show how the model improve the measurement of systemic risk and explain the link between Granger-causality network and generalized variance decompositions network. We apply the method to the monthly returns of U.S. financial Institutions including banks, broker and insurance companies to identify the level of systemic risk in the financial sector and the contribution of each financial institution.

Suggested Citation

  • Etesami, Jalal & Habibnia, Ali & Kiyavash, Negar, 2017. "Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity," LSE Research Online Documents on Economics 70769, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:70769
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    File URL: http://eprints.lse.ac.uk/70769/
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    References listed on IDEAS

    as
    1. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    2. repec:fip:fedhpr:y:2010:i:may:p:65-71 is not listed on IDEAS
    3. Viral V. Acharya & Lasse H. Pedersen & Thomas Philippon & Matthew Richardson, 2017. "Measuring Systemic Risk," Review of Financial Studies, Society for Financial Studies, vol. 30(1), pages 2-47.
    4. repec:taf:jnlbes:v:30:y:2012:i:2:p:212-228 is not listed on IDEAS
    5. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    6. Monica Billio & Silvio Di Sanzo, 2015. "Granger-causality in Markov switching models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 956-966, May.
    7. Franklin Allen & Ana Babus & Elena Carletti, 2010. "Financial Connections and Systemic Risk," NBER Chapters,in: Market Institutions and Financial Market Risk National Bureau of Economic Research, Inc.
    8. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Short and long run causality measures: Theory and inference," Journal of Econometrics, Elsevier, vol. 154(1), pages 42-58, January.
    9. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    10. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    11. Viral V. Acharya, 2010. "Measuring systemic risk," Proceedings 1140, Federal Reserve Bank of Chicago.
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    More about this item

    Keywords

    Systemic risk; Risk Measurement; Financial Linkages and Contagion; Nonlinear Granger Causality; Directed Information Graphs;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • G1 - Financial Economics - - General Financial Markets
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies

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