TY - BOOK ID - 78437583 TI - Variance Decomposition Networks : Potential Pitfalls and a Simple Solution PY - 2017 SN - 1475598688 9781475598681 1475598629 PB - Washington, D.C. : International Monetary Fund, DB - UniCat KW - Decomposition method. KW - Decomposition method KW - Method, Decomposition KW - Operations research KW - Programming (Mathematics) KW - System analysis KW - Data processing. KW - Banks and Banking KW - Econometrics KW - Finance: General KW - Insurance KW - Industries: Financial Services KW - Time-Series Models KW - Dynamic Quantile Regressions KW - Dynamic Treatment Effect Models KW - Diffusion Processes KW - State Space Models KW - Multiple or Simultaneous Equation Models: Other KW - Banks KW - Depository Institutions KW - Micro Finance Institutions KW - Mortgages KW - Pension Funds KW - Non-bank Financial Institutions KW - Financial Instruments KW - Institutional Investors KW - Insurance Companies KW - Actuarial Studies KW - General Financial Markets: Government Policy and Regulation KW - Finance KW - Banking KW - Insurance & actuarial studies KW - Econometrics & economic statistics KW - Insurance companies KW - Systemic risk KW - Vector autoregression KW - Financial institutions KW - Financial sector policy and analysis KW - Econometric analysis KW - Commercial banks KW - Banks and banking KW - Financial risk management KW - United States UR - http://www.unicat.be/uniCat?func=search&query=sysid:78437583 AB - Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. The standard choice is the order invariant generalized forecast error variance decomposition of Pesaran and Shin (1998). The shares of the forecast error variation, however, do not add to unity, making difficult to compare risk ratings and risks contributions at two different points in time. As a solution, this paper suggests using the Lanne-Nyberg (2016) decomposition, which shares the order invariance property. To illustrate the differences between both decomposition methods, I analyzed the global financial system during 2001 – 2016. The analysis shows that different decomposition methods yield substantially different systemic risk and vulnerability rankings. This suggests caution is warranted when using rankings and risk contributions for guiding financial regulation and economic policy. ER -