## 9. Interconnectedness: Spillover Risk for U.S. Financial Institutions in 2007

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---

### I. Motivation and definition
- Systemic risk defined by IMF/FSB/BIS as disruption to the flow of financial services that (i) is caused by an impairment of all or parts of the financial system and (ii) has the potential to have serious negative consequences for the real economy.
- Tail event identified with systemic event; tail events are by definition rare.
- Need for concrete, operational metrics of systemic/tail events to inform monitoring and contingency policy despite poor timing predictability of early warning systems.
- Market-based indicators emphasized as "near-coincident" indicators that can give policymakers weeks or months to deploy pre-mapped crisis responses.

### II. Near-coincident and coincident indicators: construction and key definitions
- Sample sizes:
  - United States: 17 financial institutions.
  - Euro area: 19 financial institutions.
- Systemic Financial Stress (SFS):
  - SFS = fraction of financial institutions experiencing large negative abnormal equity returns.
  - "Large negative abnormal returns" defined as abnormal returns lower than the 5th percentile (left) tail of the joint distribution on a given day, and cumulatively negative abnormal returns for the two weeks following that day.
  - Example: an SFS of 0.10 = 10 percent of U.S. financial institutions experiencing large and persistent stresses.
  - Extreme SFS = binary variable equal to 1 if SFS ≥ 0.25, and 0 otherwise.
  - Thresholds for abnormal return cutoff (based on joint distribution): U.S. banks = -6.7 percent; euro area banks = -5.9 percent.
- Eleven near-coincident indicators classified by data source:
  - Equity market based: VIX (US), VSTOXX (euro area), Credit Suisse Fear Barometer.
  - Debt market based: JPoD, Diebold-Yilmaz (based on CDS spreads in this paper).
  - Balance sheet + market hybrids: Distance-to-Default (DD), Systemic Contingent Claims Analysis (SCCA), CoVaR variants.
  - Macro/cross-market: yield curve slope, LIBOR-OIS spread, Systemic Liquidity Risk Indicator (SLRI).

### III. Data and frequency
- Daily (5-day week) data from January 2003 to April 2011.
- Indicators constructed at daily frequency; for testing, daily series transformed to weekly series using transformations that a priori give the highest risk signal (e.g., weekly maximum for JPoD, weekly minimum for DD).
- Data sources include Bloomberg (equity prices, 5-year CDS premia depending on coverage), Datastream (where applicable), Moody’s KMV (implied asset values), and country-specific market rates and volatility indices (VIX, VSTOXX).

### IV. Testing framework (three tests)
- Indicators evaluated by average score across three tests.

- Test 1 — Granger Causality (forecasting systemic stress):
  - Four Granger-causality tests using weekly data at lag lengths: 52 weeks, 26 weeks, 4 weeks, 1 week.
  - Two component scores: (a) Granger-Causality p-value score (weights only lags with p-value < 0.01 and no reverse causality), (b) lag-length score (p-values of t-tests on coefficients of lags in OLS regressions). Total Test 1 score = average of the two component scores.

- Test 2 — Predicting extreme events:
  - Extreme events proxied by extreme SFS (binary: SFS ≥ 0.25).
  - Logit regressions using lags of SFS and lags of the near-coincident indicator for horizons of 6 weeks, 4 weeks, and 1 week (equations 3.1–3.3).
  - Two component scores: (a) (1 minus) lag-weighted average p-values of Wald tests that all lags are jointly significant, (b) lag-weighted McFadden R-squares. Total Test 2 score = average of these two components.

- Test 3 — Early turning point:
  - AR(4) regressions and Quandt-Andrews breakpoint (QABP) tests for breaks in level and persistence.
  - Turning points scored by how far ahead of a reference failure they occurred (U.S. reference: failure of Bear Stearns; euro area reference: end-March 2009 peak in bank CDS).
  - If QABP p-value ≤ 0.05, turning point assigned score based on timeliness.

### V. Main results — United States (summary of performance)
- Top overall indicators (Table 6 overall average scores):
  - Diebold-Yilmaz: 0.62
  - Time-varying CoVaR: 0.56
  - DD banks (Distance-to-Default): 0.55
  - Yield curve: 0.52
  - VIX: 0.51
  - LIBOR-OIS spread: 0.44
  - JPoD: 0.34
  - Systemic Liquidity Risk Index: 0.30
  - Credit Suisse Fear Barometer: 0.25
  - Rolling CoVaR: 0.24
  - Systemic CCA: 0.21
- Notable sub-test highlights:
  - Diebold-Yilmaz, DD, and T-CoVaR each ranked in the top three in at least two sub-tests.
  - Yield curve slope performed particularly well in predicting systemic stress (Test 1).
  - LIBOR-OIS spread strong in predicting extreme events and had relatively early turning point detection.
  - JPoD best performer for predicting extreme events (Test 2) but weak on other tests.
- Turning points (selected U.S. dates and values):
  - Diebold-Yilmaz: persistence break 16-Jul-2007 (p-Value 0.000), level break 16-Jul-2007 (p-Value 0.000).
  - Time-varying CoVaR: 6-Aug-2007 (p-Value 0.000).
  - DD banks: level break 9-Jul-2007 (p-Value 0.031), persistence break 9-Jul-2007 (p-Value 0.000).
  - VIX: level break 9-Jul-2007 (p-Value 0.008), persistence break 23-Jul-2007 (p-Value 0.000).
- Example threshold interpretations (U.S.):
  - Extreme SFS threshold: SFS ≥ 0.25.
  - Time-varying CoVaR level at turning point (6-Aug-2007) = 7.17 (risk indicator in percentage points).
  - DD banks level at turning point (9-Jul-2007) = 8.9 (risk indicator in number standard deviations).
  - LIBOR-OIS spread turning point 30-Jul-2007 indicator value = 18.5 (basis points); alternate persistence break 6-Aug-2007 indicator value = 48.30.
  - VIX turning point values and ranges: level break 9-Jul-2007 value = 17.57; persistence break 23-Jul-2007 value = 24.17; threshold range noted 18 - 24.

### VI. Main results — Euro area (summary of performance)
- Top overall indicators (Table 7 overall average scores):
  - LIBOR-OIS spread: 0.65
  - Time-varying CoVaR: 0.55
  - Diebold-Yilmaz: 0.54
  - JPoD: 0.53
  - Systemic Liquidity Risk Index: 0.45
  - VIX (VSTOXX): 0.44
  - Credit Suisse Fear Barometer: 0.41
  - DD banks: 0.39
  - Yield curve: 0.28
  - Systemic CCA: 0.20
- Notable differences vs. U.S.:
  - LIBOR-OIS spread is the best overall near-coincident indicator for the euro area.
  - Distance-to-default performs relatively poorly in the euro area driven by an insignificant turning point in persistence (QABP p-value 0.32).
  - SLRI performs better in the euro area than in the United States.
- Selected euro area turning points and thresholds:
  - LIBOR-OIS spread: turning point 6-Aug-2007 indicator value = 14.92 (basis points); persistence break 26-Aug-2007 same value 14.92.
  - VIX (EURO STOXX 50 implied volatility): turning point 2-Jun-2008 indicator value = 23.41; persistence break 30-Jun-2008 value = 26.79; threshold range noted 23 - 27.
  - Time-varying CoVaR: turning point 14-Apr-2008 indicator value = 1.8 (risk indicator in percentage points); persistence break 16-Aug-2007 indicator value = 0.9.

### VII. Robustness checks
- Robustness perturbations included:
  - Recomputing JPoD sample composition for the U.S. (including Bear Stearns, including Lehman Brothers, including only non-failed institutions).
  - Varying extreme SFS threshold to 33 percent and 20 percent.
  - Using probit and extreme value regressions instead of logit for Test 2.
- Findings:
  - Ordering of some indicators (e.g., VIX, CSFB in U.S.; JPoD, yield curve in euro area) sensitive to test perturbations.
  - Top two indicators stable for both U.S. and euro area across robustness tests.

### VIII. Interconnectedness and spillover measures
- Spillover measures derived from market data used as proxies for perceived interconnectedness when balance-sheet exposure data are unavailable.
- Four spillover measures plotted against total assets (2007) to assess contribution to systemic spillovers: Diebold-Yilmaz, Chan-Lau/Mitra/Ong, Segoviano-Goodhart ("JPoD" PCE), and Adrian-Brunnermeier ("CoVaR").
- Key empirical finding:
  - Striking disassociation between firm size and perceived spillover potential.
  - Based on data up to June 2007, Diebold-Yilmaz and Chan-Lau/Mitra/Ong indicators identify disproportionately high spillover risk for Bear Stearns in June 2007; other indicators (Segoviano-Goodhart JPoD, Adrian-Brunnermeier CoVaR) show Bear Stearns with similar spillover potential as Bank of America or Citigroup as of June 2007.
- Implication: size alone is a poor proxy for systemic spillover potential; urgent need for better data on actual inter-institution exposures (on- and off-balance-sheet).

### IX. Policy implications and recommendations
- Choice of indicator:
  - Authorities should monitor a wide range of indicators rather than rely on a single measure because indicators serve different purposes.
  - Time-varying CoVaR and Diebold-Yilmaz performed well across samples; simple indicators (yield curve slope, LIBOR-OIS spread) also performed strongly and can be useful near-term coincident indicators.
- Indicator thresholds and policy triggers:
  - Policy tools (e.g., drawdown of countercyclical capital buffers) could be tied to pre-specified indicator thresholds derived from historical turning points.
  - Examples of threshold interpretations:
    - Extreme SFS: SFS ≥ 0.25.
    - VIX U.S. turning point range: 18 - 24; euro area: 23 - 27.
    - LIBOR-OIS spread U.S. level at turning point: 18.50 (with persistence up to 48.30).
    - Time-varying CoVaR U.S. level at turning point: 7.17.
  - For some indicators (yield curve slope; Credit Suisse Fear Barometer), the change in the indicator around the turning point may be more informative than the level itself.
- Other uses of near-coincident indicators:
  - CoVaR and Diebold-Yilmaz can be used as cross-sectional spillover measures; JPoD suited as a coincident measure (strong for extreme events); SLRI more suited as coincident measure of systemic liquidity risk; SCCA useful for stress tests estimating expected shortfall and capital shortfalls during crises.
- Data gaps:
  - Better, granular data on inter-institution exposures would improve measurement of interconnectedness and early-warning capacities; addressing G-20 Data Gaps Initiative recommendations 8 and 9 is critical.

### X. Conclusions
- The paper develops the Systemic Financial Stress (SFS) metric: continuous SFS (percent of institutions in 5th percentile left-tail abnormal returns) and extreme SFS (binary: SFS ≥ 0.25).
- SFS is continuous, standardizable across institutions/countries/time, and easily updated with market data.
- In-sample tests over January 2003–April 2011 (and turning-point subsamples) identify Diebold-Yilmaz, time-varying CoVaR, and simple macro indicators (yield curve, LIBOR-OIS) among top near-coincident indicators; performance differs across U.S. and euro area samples.
- Lack of out-of-sample evaluation noted as a caveat given limited crisis episodes and data availability; nevertheless SFS and extreme SFS could meet G-20 recommendation to develop standard measures of tail risk.

_Source: _wp13115 - 9. Interconnectedness: Spillover Risk for U.S. Financial Institutions in 2007 (PDF chapter content provided)._

### References. ............................................................................................................

### _wp13115 - References. ...............................................................................................................................30

### Tables
- 1. Financial Institutions. ...........................................................................................................16
- 2. Near-Coicident Indicators of Financial Stress. ....................................................................17
- 3. Test 1—Granger Causality of Systemic Risk Measures to the Event Indicator 
  for the U.S. Sample. ............................................................................................................18
- 4. Test 2—Forecastability of Extreme Events for U.S. Sample: Logit 
  Regressions. .........................................................................................................................18
- 5. Test 3—Turning Points for the U.S. Sample: Quandt-Andrews Breakpoint 
  Tests on Persistence and Level ............................................................................................19
- 6. Overall Results—Performance of Near-Coincidental Risk Indicators  
     for the United States. ...........................................................................................................19
- 7. Performance of Near-Coincident Risk Indicators for the Euro Area ...................................20
- 8. Thresholds for Near-Coincidental Risk Indicators in the United States. .............................20
- 9. Thresholds for Near-Coincidental Risk Indicators in the Euro Area. .....................................21

### Figures
- 1. Data Requirements for Near-Coincident Indicators ............................................................22
- 2. Systemic Financial Stress in the United States ....................................................................22
- 3. Systemic Financial Stress in the Euro Area .........................................................................23
- 4. Near Coincident Risk Indicators for the United States ........................................................24
- 5. Performance of Near-Coincident Risk Indicators for the United States ..............................26
- 6. Performance of Near-Coincident Risk Indicators for the Euro Area ...................................27
- 7. Variation in Overall Score of U.S. Near-Coincident Risk Indicators Across 
  Robustness Tests .................................................................................................................28
- 8. Variation in Overall Score of Euro Area Near-Coincident Risk Indicators 
  Across Robustness Tests .....................................................................................................28

*Source: _wp13115 - References. ...............................................................................................................................30*

### 9. Interconnectedness: Spillover Risk for U.S. Financial Institutions in 2007 ........................29

### 9. Interconnectedness: Spillover Risk for U.S. Financial Institutions in 2007

### I. Motivation and definition
- Systemic risk defined by IMF/FSB/BIS as disruption to the flow of financial services that (i) is caused by an impairment of all or parts of the financial system and (ii) has the potential to have serious negative consequences for the real economy.
- Tail event identified with systemic event; tail events are by definition rare.
- Need for concrete, operational metrics of systemic/tail events to inform monitoring and contingency policy despite poor timing predictability of early warning systems.
- Market-based indicators are emphasized as "near-coincident" indicators that can give policymakers weeks or months to deploy pre-mapped crisis responses.

### II. Near-coincident and coincident indicators: construction and key definitions
- Sample sizes:
  - United States: 17 financial institutions.
  - Euro area: 19 financial institutions.
- Systemic Financial Stress (SFS):
  - SFS = fraction of financial institutions experiencing large negative abnormal equity returns.
  - "Large negative abnormal returns" defined as abnormal returns lower than the 5th percentile (left) tail of the joint distribution on a given day, and cumulatively negative abnormal returns for the two weeks following that day.
  - Example: an SFS of 0.10 = 10 percent of U.S. financial institutions experiencing large and persistent stresses.
  - Extreme SFS = binary variable equal to 1 if SFS ≥ 0.25, and 0 otherwise.
  - Thresholds for abnormal return cutoff (based on joint distribution): U.S. banks = -6.7 percent; euro area banks = -5.9 percent.
- Eleven near-coincident indicators classified by data source:
  - Equity market based: VIX (US), VSTOXX (euro area), Credit Suisse Fear Barometer.
  - Debt market based: JPoD, Diebold-Yilmaz (based on CDS spreads in this paper).
  - Balance sheet + market hybrids: Distance-to-Default (DD), Systemic Contingent Claims Analysis (SCCA), CoVaR variants.
  - Macro/cross-market: yield curve slope, LIBOR-OIS spread, Systemic Liquidity Risk Indicator (SLRI).

### III. Data and frequency
- Daily (5-day week) data from January 2003 to April 2011.
- Indicators are constructed at daily frequency; for testing, daily series transformed to weekly series using transformations that a priori give the highest risk signal (e.g., weekly maximum for JPoD, weekly minimum for DD).
- Data sources include Bloomberg (equity prices, 5-year CDS premia depending on coverage), Datastream (where applicable), Moody’s KMV (implied asset values), and country-specific market rates and volatility indices (VIX, VSTOXX).

### IV. Testing framework (three tests)
- Indicators evaluated by average score across three tests.

- Test 1 — Granger Causality (forecasting systemic stress):
  - Four Granger-causality tests using weekly data at lag lengths: 52 weeks, 26 weeks, 4 weeks, 1 week.
  - Two component scores: (a) Granger-Causality p-value score (weights only lags with p-value < 0.01 and no reverse causality), (b) lag-length score (p-values of t-tests on coefficients of lags in OLS regressions). Total Test 1 score = average of the two component scores.

- Test 2 — Predicting extreme events:
  - Extreme events proxied by extreme SFS (binary: SFS ≥ 0.25).
  - Logit regressions using lags of SFS and lags of the near-coincident indicator for horizons of 6 weeks, 4 weeks, and 1 week (equations 3.1–3.3).
  - Two component scores: (a) (1 minus) lag-weighted average p-values of Wald tests that all lags are jointly significant, (b) lag-weighted McFadden R-squares. Total Test 2 score = average of these two components.

- Test 3 — Early turning point:
  - AR(4) regressions and Quandt-Andrews breakpoint (QABP) tests for breaks in level and persistence.
  - Turning points scored by how far ahead of a reference failure they occurred (U.S. reference: failure of Bear Stearns; euro area reference: end-March 2009 peak in bank CDS).
  - If QABP p-value ≤ 0.05, turning point assigned score based on timeliness.

### V. Main results — United States (summary of performance)
- Top overall indicators (Table 6 overall average scores):
  - Diebold-Yilmaz: 0.62
  - Time-varying CoVaR: 0.56
  - DD banks (Distance-to-Default): 0.55
  - Yield curve: 0.52
  - VIX: 0.51
  - LIBOR-OIS spread: 0.44
  - JPoD: 0.34
  - Systemic Liquidity Risk Index: 0.30
  - Credit Suisse Fear Barometer: 0.25
  - Rolling CoVaR: 0.24
  - Systemic CCA: 0.21
- Notable sub-test highlights:
  - Diebold-Yilmaz, DD, and T-CoVaR each ranked in the top three in at least two sub-tests.
  - Yield curve slope performed particularly well in predicting systemic stress (Test 1).
  - LIBOR-OIS spread strong in predicting extreme events and had relatively early turning point detection.
  - JPoD best performer for predicting extreme events (Test 2) but weak on other tests.
- Turning points (selected U.S. dates and values from Table 5 / Table 8):
  - Diebold-Yilmaz: persistence break 16-Jul-2007 (p-Value 0.000), level break 16-Jul-2007 (p-Value 0.000).
  - Time-varying CoVaR: 6-Aug-2007 (p-Value 0.000).
  - DD banks: level break 9-Jul-2007 (p-Value 0.031), persistence break 9-Jul-2007 (p-Value 0.000).
  - VIX: level break 9-Jul-2007 (p-Value 0.008), persistence break 23-Jul-2007 (p-Value 0.000).
  - Example threshold interpretations (Table 8; U.S.):
    - Extreme SFS threshold: SFS ≥ 0.25 defines extreme event.
    - Time-varying CoVaR level at turning point (6-Aug-2007) = 7.17 (risk indicator in percentage points).
    - DD banks level at turning point (9-Jul-2007) = 8.9 (risk indicator in number standard deviations).
    - LIBOR-OIS spread turning point 30-Jul-2007 indicator value = 18.5 (basis points); alternate persistence break 6-Aug-2007 indicator value = 48.30.
    - VIX turning point values and ranges: level break 9-Jul-2007 value = 17.57; persistence break 23-Jul-2007 value = 24.17; threshold range noted 18 - 24.

### VI. Main results — Euro area (summary of performance)
- Top overall indicators (Table 7 overall average scores):
  - LIBOR-OIS spread: 0.65
  - Time-varying CoVaR: 0.55
  - Diebold-Yilmaz: 0.54
  - JPoD: 0.53
  - Systemic Liquidity Risk Index: 0.45
  - VIX (VSTOXX): 0.44
  - Credit Suisse Fear Barometer: 0.41
  - DD banks: 0.39
  - Yield curve: 0.28
  - Systemic CCA: 0.20
- Notable differences vs. U.S.:
  - LIBOR-OIS spread is the best overall near-coincident indicator for the euro area.
  - Distance-to-default performs relatively poorly in the euro area driven by an insignificant turning point in persistence (QABP p-value 0.32).
  - SLRI performs better in the euro area than in the United States.
- Selected euro area turning points and thresholds (Table 9):
  - LIBOR-OIS spread: turning point 6-Aug-2007 indicator value = 14.92 (basis points); persistence break 26-Aug-2007 same value 14.92.
  - VIX (EURO STOXX 50 implied volatility): turning point 2-Jun-2008 indicator value = 23.41; persistence break 30-Jun-2008 value = 26.79; threshold range noted 23 - 27.
  - Time-varying CoVaR: turning point 14-Apr-2008 indicator value = 1.8 (risk indicator in percentage points); persistence break 16-Aug-2007 indicator value = 0.9.

### VII. Robustness checks
- Robustness perturbations included:
  - Recomputing JPoD sample composition for the U.S. (including Bear Stearns, including Lehman Brothers, including only non-failed institutions).
  - Varying extreme SFS threshold to 33 percent and 20 percent.
  - Using probit and extreme value regressions instead of logit for Test 2.
- Findings:
  - Ordering of some indicators (e.g., VIX, CSFB in U.S.; JPoD, yield curve in euro area) sensitive to test perturbations.
  - Top two indicators stable for both U.S. and euro area across robustness tests.

### VIII. Interconnectedness and spillover measures
- Spillover measures derived from market data used as proxies for perceived interconnectedness when balance-sheet exposure data are unavailable.
- Four spillover measures plotted against total assets (2007) to assess contribution to systemic spillovers (Figure 9): Diebold-Yilmaz, Chan-Lau/Mitra/Ong, Segoviano-Goodhart ("JPoD" PCE), and Adrian-Brunnermeier ("CoVaR").
- Key empirical finding:
  - Striking disassociation between firm size and perceived spillover potential.
  - Based on data up to June 2007, Diebold-Yilmaz and Chan-Lau/Mitra/Ong indicators identify disproportionately high spillover risk for Bear Stearns in June 2007; other indicators (Segoviano-Goodhart JPoD, Adrian-Brunnermeier CoVaR) show Bear Stearns with similar spillover potential as Bank of America or Citigroup as of June 2007.
- Implication: size alone is a poor proxy for systemic spillover potential; there is an urgent need for better data on actual inter-institution exposures (on- and off-balance-sheet).

### IX. Policy implications and recommendations
- Choice of indicator:
  - Authorities should monitor a wide range of indicators rather than rely on a single measure because indicators serve different purposes.
  - Time-varying CoVaR and Diebold-Yilmaz performed well across samples; simple indicators (yield curve slope, LIBOR-OIS spread) also performed strongly and can be useful near-term coincident indicators.
- Indicator thresholds and policy triggers:
  - Policy tools (e.g., drawdown of countercyclical capital buffers) could be tied to pre-specified indicator thresholds derived from historical turning points.
  - Examples of threshold interpretations (U.S. Table 8 and euro area Table 9):
    - Extreme SFS: SFS ≥ 0.25.
    - VIX U.S. turning point range: 18 - 24; euro area: 23 - 27.
    - LIBOR-OIS spread U.S. level at turning point: 18.50 (with persistence up to 48.30).
    - Time-varying CoVaR U.S. level at turning point: 7.17.
  - For some indicators (yield curve slope; Credit Suisse Fear Barometer), the change in the indicator around the turning point may be more informative than the level itself.
- Other uses of near-coincident indicators:
  - CoVaR and Diebold-Yilmaz can be used as cross-sectional spillover measures; JPoD suited as a coincident measure (strong for extreme events); SLRI more suited as coincident measure of systemic liquidity risk; SCCA useful for stress tests estimating expected shortfall and capital shortfalls during crises.
- Data gaps:
  - Better, granular data on inter-institution exposures would improve measurement of interconnectedness and early-warning capacities; addressing G-20 Data Gaps Initiative recommendations 8 and 9 is critical.

### X. Conclusions
- The paper develops the Systemic Financial Stress (SFS) metric: continuous SFS (percent of institutions in 5th percentile left-tail abnormal returns) and extreme SFS (binary: SFS ≥ 0.25).
- SFS is continuous, standardizable across institutions/countries/time, and easily updated with market data.
- In-sample tests over January 2003–April 2011 (and turning-point subsamples) identify Diebold-Yilmaz, time-varying CoVaR, and simple macro indicators (yield curve, LIBOR-OIS) among top near-coincident indicators; performance differs across U.S. and euro area samples.
- Lack of out-of-sample evaluation noted as a caveat given limited crisis episodes and data availability; nevertheless SFS and extreme SFS could meet G-20 recommendation to develop standard measures of tail risk.

*Source: _wp13115 - 9. Interconnectedness: Spillover Risk for U.S. Financial Institutions in 2007 (PDF chapter content provided).*

### References

### References

### Listed works

- Adrian, Tobias and Markus Brunnermeier, 2010, “CoVaR,” Federal Reserve Bank of New York Staff Reports.  
- Aït-Sahalia, Yacine, Jochen Andritzky, Andreas Jobst, Sylwia Nowak, and Natalia T.Tamirisa, 2009, “How to Stop a Herd of Running Bears? Market Response to Policy Initiatives during the Global Financial Crisis,” IMF Working Paper, September 2009.  
- Bisias, Dimitrios, Mark Flood, Andrew W. Lo, and Stavros Valavanis, 2012, “A Survey of Systemic Risk Analytics,” Office of Financial Research Working Paper 1, January.  
- Blancher, Nicolas, Srobona Mitra, Hanan Morsy, Akira Otani, Tiago Severo, and Laura Valderrama, forthcoming, 2013, “SysMo—Practical Guidance on the Toolkit for Systemic Risk Monitoring,” IMF Working Paper.  
- Borio, Claudio, and Mathias Drehmann, 2009, “Assessing the Risk of Banking Crises—Revisited,” BIS Quarterly Review, March, pp. 29–44.  
- Chan-Lau, Jorge, Srobona Mitra, and Li Lian Ong, 2012, “Identifying Contagion Risk in the International Banking System: An Extreme Value Theory Approach,” International Journal of Finance and Economics Vol. 17 (4), October.  
- Chicago Board Options Exchange, 2009, The CBOE Volatility Index - VIX, available at http://www.cboe.com/micro/VIX/vixwhite.pdf.  
- Gray, Dale F. and Jobst, Andreas A., 2010, “New Directions in New Directions in Financial Sector and Sovereign Risk Management,” Journal of Investment Management, Vol. 8, No. 1, pp. 23–38.  
- De Nicolò, Gianni and Myron Kwast, 2002, “Financial Risk and Consolidation: Are They Related?” Journal of Banking and Finance, Vol. 26, no. 5, pp. 861–80.  
- Diebold, Francis X. and Kamil Yilmaz, 2009, “Measuring Financial Asset Return and Volatility Spillovers, With Application to Global Equity Markets,” Economic Journal, Vol.119, pp.15–71.  
- Tom, Edward K. and Dennis Davitt, 2009, “Fear Factor: The Credit Suisse Fear Barometer (CSFB),” Credit Suisse Equity Derivatives Strategy, April 13.  
- Estrella, Arturo, 2005. “Why Does the Yield Curve Predict Output and Inflation?” Economic Journal 115, no. 505 (July): 722–44.  
- European Central Bank, 2008, “The Euro Area Financial System,” Financial Stability Review, December, pp. 100–05.  
- Forbes, K and R. Rigobon, 2002, “No Contagion, Only Interdependence: Measuring Stock Market Comovements,” Journal of Finance, Vol LVII, No. 5, October.  
- International Monetary Fund, Bank of International Settlements, Financial Stability Board (IMF-BIS-FSB), 2009, “Guidance to Assess the Systemic Importance of Financial Institutions, Instruments, and Markets: Initial Considerations,” Report to the G-20 Finance Ministers and Governors (October).  
- International Monetary Fund Staff and Financial Stability Board Secretariat (IMF-FSB), 2009, “The Financial Crisis and Information Gaps,” Report to the G-20 Finance Ministers and Central Bank Governors (October).  
- International Monetary Fund, 2009, “Detecting Systemic Risk,” Chapter 3 in Global Financial Stability Report, World Economic and Financial Surveys (Washington, April).  
- _________, 2010, “The IMF-FSB Early Warning Exercise: Design and Methodological Toolkit”, available at www.imf.org/external/np/pp/eng/2010/090110.pdf, September.  
- _________, 2011a, “Macroprudential Policy—An Organizing Framework,” IMF Policy Paper, April.  
- _________, 2011b, “Towards Operationalizing Macroprudential Policy—When to Act?” Chapter 3 of Global Financial Stability Report, September.  
- _________, 2011c, United Kingdom: Stress Testing the Banking Sector Technical Note, IMF Country Report 11/227, July.  
- _________, 2011d, “How to Address the Systemic Part of Liquidity Risk,” Chapter 2 of the Global Financial Stability Report, April.  
- _________, 2012a, “Factsheet: IMF-FSB Early Warning Exercise”, available at http://www.imf.org/external/np/exr/facts/ewe.htm, March.  
- McCulley, Paul, 2008, “PIMCO Global Central Bank Focus: A Perfervid Moment,” available at http://www.pimco.com/EN/Insights/Pages/GCBF%20Jan%202008.aspx, January.  
- Office of Financial Research, 2012, “A Survey of Systemic Risk Analytics,” Working Paper Number 1, U.S. Department of Treasury, January.  
- Reinhart, Carmen M. and Kenneth Rogoff, 2011, “This Time is Different: Eight Centuries of Financial Folly”, Princeton, NJ: Princeton University Press  
- Rodriguez-Moreno, Maria, and Juan Ignacio Peña, 2011, “Systemic Risk Measures: The Simpler the Better?” available at SSRN: http://ssrn.com/abstract=1681087, February.  
- Rose, Andrew K. and Mark M. Spiegel, 2009, “Predicting Crises, Part II: Did Anything Matter (to Everybody)?” FRBSF Economic Letter, available at http://www.frbsf.org/publications/economics/letter/2009/el2009-30.html, 28 September.  
- Schwaab, Bernd, Siem Jan Koopman, and Andre Lucas, 2011, “Systemic Risk Diagnostics: Coincident Indicators and Early Warning Signals,” ECB Working Paper No. 1327 (Frankfurt: European Central Bank).  
- Segoviano, Miguel and Charles Goodhart, 2009, “Banking Stability Measures,” IMF Working Paper No. 09/04 (Washington: International Monetary Fund).  
- Segoviano, Miguel and Pablo Padilla, 2006, “Portfolio Credit Risk and Macroeconomic Shocks: Applications to Stress Testing Under Data-Restricted Environments,” IMF Working Paper No. 06/283 (Washington: International Monetary Fund).  
- Severo, Tiago, 2012, “Using Violations of Arbitrage to Measure Systemic Liquidity Risk and the Cost of Liquidity Insurance,” IMF Working Paper 12/194 (Washington: International Monetary Fund)  
- Solé, Juan and Marco Espinoza-Vega, 2010, “Cross-Border Financial Surveillance: A Network Perspective,” IMF Working Paper No. 10/105 (Washington: International Monetary Fund).  
- Taylor, John B. and John C. Williams, 2009, “A Black Swan in the Money Market,” American Economic Journal: Macroeconomics, American Economic Association, Vol. 1(1), pp.58–83, January.

*Source: _wp13115 - References*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp13115.pdf_