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System-wide risk and systemic importance: I ncomplete review of metrics and data

System-wide risk and systemic importance: I ncomplete review of metrics and data. Nikola Tarashev, Bank for International Settlements Cambridge, 25 September 2014. This presentation does not necessarily reflect the views of the BIS, the BCBS or the BCBS Secretariat. Roadmap.

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System-wide risk and systemic importance: I ncomplete review of metrics and data

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  1. System-wide risk and systemic importance:Incomplete review of metrics and data Nikola Tarashev, Bank for International Settlements Cambridge, 25 September 2014

  2. This presentation does not necessarily reflect the views of the BIS, the BCBS or the BCBS Secretariat.

  3. Roadmap • Two competing metrics of aggregate risk: VaR and ES • have different statistical properties • are suited for different purposes • Systemic importance: what if data were not an issue? • different measures for different purposes • common misunderstandings • Data: an important driver of analyses of system-wide risk and systemic importance

  4. Choice of metric: rarely motivated by objectives • Two types of metrics: for portfolio risk & system-wide risk. • Quantile-based: e.g. Value-at-Risk (VaR) • robust estimation • elicitable, if data sample is known • Tail expectations: e.g. Expected Shortfall (ES) • coherent: well-defined capital optimization problems • limits arbitrage • But these metrics serve very different purposes • VaR: attain an acceptably low probability of distress • ES: prepare for the fallout of distress

  5. Limit the probability of failure: VaR Tail of loss distribution (a bank’s assets) VaR = default point Losses absorbed by capital

  6. Prepare for / insure against costs of failure: ES Tail of loss distribution Losses absorbed by capital ES ES  DI premium

  7. Regulatory arbitrage • VaR: incentives for banks to hide behind a quantile Tail of loss distribution VaR = default point

  8. From system-wide risk to systemic importance • Shapley value: an allocation methodology & disciplining device • Satisfies appealing criteria. • Captures how the interaction of players creates risk Tarashev, Borio and Tsatsaronis (2010) • A popular alternative is a special case • Aumann-Shapley value, applied to ES or VaR • = marginal ES, popularised by Acharya et al (2009) • Another popular alternative falls in a different category: • CoVaR, popularised by Adrian and Brunnermeier (2008)

  9. Contribution approach (CA) vs. participation approach (PA)

  10. Participation in tail events vs. contribution to systemic risk • Participation approach charge premia for insuring against losses in tail events • Contributionapproachpenalise banks for raising the risk in system • Does it really matter which one you choose ?

  11. Numerical setup • Bank-level losses: • data on non-equity liabilities • Correlated defaults • data on marginal PDs & asset correlations • Derive distribution of system-wide losses  VaR or ES

  12. Participation vs. contribution Similar message with ES

  13. Need to remain mindful of causality • A measure of system-wide impact (CoVaR idea): • quite useful from a policy perspective • in practice: E(systemic distress|individual distress) • Think of the stylized banking system from above. To fix ideas: • different PDs; • identical exposures to common risk factor, etc. • Which bank is designated as most systemically important? • The bank with lowest PD • Intuition: if the safest bank is in trouble because of common risk factor, other banks must also be in trouble. • Spurious causality  misleading message

  14. Data availability: a factor in the metric design • Data on interlinkages in the system: • Interbank network is a key driver of system-wide risk. • Different approaches to measuring systemic importance treat interbank borrowers and lenders differently. Drehmann and Tarashev (2013)

  15. IB lender vs. IB borrower: the approach matters

  16. Price data • Rely on markets to convey information about interconnectedness, in reduced form. • Data are rich: • despite few direct observations in the tail of interest • … EVT techniques possible Tarashev and Zhou (2013)

  17. Empirical setup • Sample of 50 largest banks with CDS data • Data: • Balance sheet data  banks’ size • CDS spreads  LGD, tendency to default with others • Moody’s KMV EDFs  PDs • Systemic event ≡ when losses exceed 15% of system size

  18. Humble even with price data

  19. Bank characteristics and systemic importance

  20. Cited papers • Tarashev, Borio and Tsatsaronis (2010): “Attributing systemic risk to individual institutions”, BIS Working Paper 308. • Drehmann and Tarashev (2013): “Measuring the systemic importance of interconnected banks”, Journal of Financial Intermediation, v. 22, iss 4. • Tarashev and Zhou (2013), “Looking at the tail: price-based measures of systemic importance”, BIS Quarterly Review, June

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