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Monitoring systemic risk: facing the data constraints. Isaac NEWTON Institute Cambridge 25 September 2014. Laurent CLERC Banque de France Financial Stability Directorate. Financial Indicators on Risk and Stability (FIRST), by Avesani (2005)
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Monitoring systemic risk: facing the data constraints Isaac NEWTON Institute Cambridge 25 September 2014 Laurent CLERC Banque de France Financial Stability Directorate
Financial Indicators on Risk and Stability (FIRST), by Avesani (2005) • Expected number of defaults (END), by Chan-Lau and Gravelle (2005) • Conditional Value-at-Risk (CoVaR), by Adrian and Brunnermeier (2008) • Conditional probability of failure of a large fraction of financial institutions, by Giesecke and Kim (2009) • Banking Stability Measures (in particular BSI and JPoD), by Segoviano and Goodhart (2009) • Distress insurance premium (DIP) of the banking system and an institution’s marginal contribution to it, by Huang et al. (2009) and Huang et al. (2010) • Marginal expected shortfall (MES), by Acharya et al. (2010) and disentangled into volatility, correlation and tail risk by Brownlees and Engle (2010) • Measure institutions’ systemic importance using Shapley value methodology, by Tarashev et al. (2010) and Drehmann and Tsatsaronis (2011) • Information contained in central bank communication to help measure stress in financial markets (specific focus on the euro area), by Grimaldi (2010) • Composite Indicator of Systemic Stress (CISS) (specific focus on the euro area), by Hollo et al. (2010) • Systemic risk diagnostics (simultaneous failure of a large number of bank and non-bank intermediaries), by Schwaab et al. (2010) • Bank Vulnerability (AV), by Greenwood et al. (2011) • Contigent Claim Analysis (CCA), by Gray and Jobst (2011) • Non-Coreliabilities ratio (Hahm et al., 2011) • Contagion Index, Cont et al. (2012) • Granger causality on principal components, by Billio et al. (2012) • A Survey of Systemic Risk Analytics (31 measures), by Bisias et al. (2012) • SRISK (Brownlees and Engle, 2011 ; Acharya et al. AER, 2012) • Multi-CoVaR and Shapley value, by Cao (2013) • Domino Effects when Banks Hoard Liquidity, Fourel et al. (2013) • A Network View on Money Market Freezes, Abassi et al. (2013) • Evaluating Early-Warning indicators of banking crises, Drehmann and Juselius (2013) • Macro-financialvulnerabilities and future financial stress, Lo Duca and Peltonen (2013) • An amazing amount of research on systemic risk measures… • but mostly based on market price data
Market price and exposures data: Does it make any difference? Source: Clerc, Gabrieli, Kern & el Omari (2014): Monitoring the European CDS Market through Networks: Implications for Contagion Risks, Banque de France Working Paper, n° 477, March.
Before the crisis: data driven process Since 2009: policy driven process: e.g. the G20 Data Gap initiative • Regulatory efforts to overcome the data issue and better monitor systemic risk
Development of measures of system-wide, macro-prudential risk • Development of a common data template for GSIFIs • Enhancement of BIS consolidated banking statistics • Development of a standardized template covering the international exposure of large non bank financial institutions • 10 countries participating in the Hub (located at the BIS) • The access of Central banks to the Hub is recent (10 days ago): 10 members ; BIS provide reports • The data Gap initiative
Multilateral surveillance vs. nationally oriented supervision • No supervisory authority has the global picture • But efforts to develop resolution regimes for GSIFIs • Limited information: so far, no information on collateral (the collection of information has just started this year); difficult also to get consistent exposures across various classes of derivatives • Current challenges: is the information sufficient for systemic risk monitoring?
Post Crisis regulatory environment • Ex. EMIR in Europe • 6 Trade repositories registered by the ESMA • Billions of daily observations • Still limited information on collateral • Issue of the consistency of the information gathered by the TR, the extent to which data can be aggregated… • Cost of storage: ex. DTCC invested USD100 millions to develop its information system • New Challenges: the Data Tsunami