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Frances Shaw

21 September 2022
One important source of systemic risk can arise from asset commonality among financial institutions. This indirect interconnection may occur when financial institutions invest in similar or correlated assets and is also described as overlapping portfolios. In this work, we propose a methodology to quantify systemic risk derived from asset commonality and we apply it to assess the degree of indirect interconnection of banks due to their financial holdings. Based on granular information of asset holdings of European significant banks, we compute the sensitivity based ∆ CoVaR which captures the potential sources of systemic risk originating from asset commonality. The novel indicator proves to be consistent with other indicators of systemic importance, yet it has a more transparent foundation in terms of the source of systemic risk, which can contribute to effective macroprudential supervision.
JEL Code
C58 : Mathematical and Quantitative Methods→Econometric Modeling→Financial Econometrics
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
G01 : Financial Economics→General→Financial Crises
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
G18 : Financial Economics→General Financial Markets→Government Policy and Regulation
G20 : Financial Economics→Financial Institutions and Services→General
G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill
12 February 2021
Net trading income is an important but volatile source of revenue for many euro area banks deemed to be highly sensitive to changes in financial market conditions. We propose a two-step econometric approach to quantify the downside risk of financial shocks on the banks’ trading revenues. First, we estimate the parameters of a fixed-effects quantile autoregressive model conditional on exogenous macro-financial shocks and bank characteristics. In the second step, we approximate the entire empirical conditional distribution of net trading income across all banks and time horizons by interpolating between the estimated quantiles. Based on the estimated distribution function, we derive two key metrics that summarize conditional left tail risks: i) conditional shortfall, ii) material loss probability. These measures are relevant in a stress test exercise whose aim to gauge CET-1 capital depletion under an adverse macro-financial scenario. We apply our methodology on supervisory data for a representative sample of European banks over the period spanning from the first quarter of 2015 to the last quarter of 2020. We find that the lower quantiles of net trading revenue distribution are significantly impacted by deteriorating financial conditions, whereas the upper quantiles seem to be stable over time.
JEL Code
C21 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions
C23 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Panel Data Models, Spatio-temporal Models
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation