Consequences of Regulatory Compliance Barry Quinn Queens University Belfast Barbara Casu Cass Business School Sami Ben Nacuer IMF Rym Ayadi HEC Montreal Today Motivation ID: 644868
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Slide1
Unintended Systemic Risk Consequences of Regulatory Compliance
Barry
Quinn
(Queen’s University Belfast)
Barbara
Casu
(Cass Business School)
Sami Ben
Nacuer
(IMF)
Rym
Ayadi
(HEC Montreal)Slide2
Today
Motivation
Contribution
Key Finding
Our Empirical
Design
Key Findings and Discussion
Concluding remarksSlide3
MotivationIn 2012 a comprehensive review of the Basel Committee on Banking Supervision’s Core principles for effective banking supervision(BCPs)sought
to:
More effectively
deal with Systemically Important Banks
Focus on systemic risk by applying
a
macro
perspective to the
microprudential
supervision.
F
ocus
on
reducing
both the probability and impact of a bank failure.
Horvath and Wagner (2017) argue that the systemic effect of countercyclical capital requirements is ambiguous.
Danielsson
(2002) critiqued Basel II capital policies as suffering from the fallacy of
composition.
R
isk
is endogenous to the system while nonetheless appearing exogenous to individual firm
.Slide4
ContributionWe contribute to the on-going policy debate by assessing whether international regulatory compliance predicts
systemic risk
.
We focus specifically on the adoption of international capital standards and the Basel Core Principles for Effective Bank Supervision (BCP
).
We further disaggregate the systemic risk consequence of regulatory compliance into:
Supervisors actions.
Banks actions.
We
evaluate how compliance with BCP
predicts
systemic risk of
993
publicly listed banks from North America and Europe over the period
1999-2015.Slide5
Key FindingsWe find a positive predictive
relationship between BCP compliance and systemic risk.
Encouragingly
, what supervisors are doing reduces systemic risk.
Surprisingly, what supervisors want banks to do increases systemic risk.
The latter findings is driven by capital adequacy compliance.Slide6
Our Empirical DesignWe
create annual aggregate % compliance scores and disaggregated scores for banks and supervisors actions for 25 country over the period
1999-2015
.
Systemic risk is measured using a time-varying version of
Conditional Value at Risk
introduced by Adrian and
Brunnermeier
(2016)
The precedent sensitivity
of systemic risk to BCP compliance is assessed using a dynamic Difference in Difference framework.
Slide7
Our Empirical DesignThree systems are defined to estimate systemic risk and empirical test the above relationship
A system of systemically important banks
A system of North American banks
A system of European Banks
Systemic
risk is estimate weekly using all publicly listed financial institutions in each system for the longest possible period that reliable data is available; 1990-2016.
Systemic risk is then aggregated to the quarterly level and matched with firm characteristics and BCP
scores
.
To control of the changing nature of our unbalanced panel over the period we use a portfolio
sort as per Adrian and
Brunnermeier (2008) Slide8
SampleDaily equity price data,
and quarterly balance sheet data for period
1993-2015 are sourced from Bloomberg.
The macroeconomic state variables are collected from Thomson Reuters
Datastream
.
The principal variable of interest, BCP compliance, is derived from the IMF and World Bank Basel Core Financial Sector Assessment Program (FSAP) database.
After
a number of sanity filters we have an unbalanced panel of
993
banks over a 1719 week period
Importantly this includes the publicly quoted global and domestic systemically important financial institutions identified by the FSB and EBA in the last three years.Slide9
Summary Statistics
Systemically Important Banks
European Banks
North American Banks
Variables
Mean
Std Dev
Obs
Mean
Std Dev
ObsMean
Std Dev
Obs
Δ
$
CoVaR
i95,t1173.852250.784378264.291276.097828137.14789.9339509 Δ$CoVaRi99,t2320.444542.144378370.181695.827828290.921696.4239509 VaRi95,t45.5224.12449648.3921.57827326.814.840430 VaRi99,t80.3437.31449676.6225.56827359.5429.4740430 Leverage %92.763.1544967.583.77827310.457.4140430 Size1.100.1944870.830.2181840.630.2440194 Maturity Mismatch %13.6611.4044960.130.1082730.050.0740430 Boom0.874.2944961.023.6882730.764.0140430 Overall BCP %12.597.7431818.979.418795.260.022257 Supervisors BCP %15.308.5131820.9210.968796.080.622257 Banks BCP %17.927.8531819.811.487918.592.642257 Capital Adequacy BCP %23.147.2931824.028.02879200.12257 Credit Risk BCP %23.087.2331827.529.70879210.12257 Concentration Risk BCP %31.269.9431830.7910.3587939.503.122257 Market Risk BCP %25.8511.1131829.3512.0387921.509.372257 Liquidity Risk BCP %29.129.9831826.179.6487938.585.132257 Operational Risk BCP %23.909.6631831.1713.94879190.22257
Note: The table reports statistics of the quarterly variables used in the
Δ$CoVaR
used in the dynamic
DiD
regressions. The data spans 1990:Q1-2015:Q4 and covers 993 banks, 209 in Europe and 784 in North America.
VaR
i
q,t
is expressed in quarterly percent.
Δ$CoVaR
i
q,t
is
normalised
by the cross sectional average of market equity each quarter and is expressed in quarterly basis points.Slide10
Systemically Important Banks
Dependent variable:
Panel A: Δ
$
CoVaR
i
95,t
Panel B: Δ
$
CoVaRi99,tOverall BCP8.31*** 9.93*** (1.74)
(4.04)
Supervisors BCP
-1.22***
-7.36*** (0.47) (1.09)Banks BCP 6.71*** 8.41*** (2.18) (1.26)Other coefficients estimates redacted for exposition purposesFixed EffectsYesYesYesYesObservations3,0823,0823,0823,082R20.610.610.600.60Adjusted R20.610.610.600.60This table reports coefficients from difference in difference regression for Δ$CoVaRi95,t on one year lag of the BCP variables, state variables, and bank characteristics in panel A and for Δ$CoVaRi99,t in panel B. Each regression has a panel of firms. Newey-west standard errors allowing for up to five periods of autocorrelation are displayed in parentheses *p<0.1;**p<0.05;***p<0.01. Bank characteristic and state variable coefficients have been redacted for visual exposition.199 (20*9.93) bps of quarterly market equity losses at the original BCP scaleSlide11
North American Banks
Dependent variable:
Panel A: Δ
$
CoVaR
i
95,t
Panel B: Δ
$
CoVaRi99,tOverall BCP19.04** 31.28** (8.63)
(15.81)
Supervisors BCP
-45.28**
-72.26*** (6.24) (19.73)Banks BCP 53.10*** 131.15*** (20.18) (49.85)Other coefficients estimates redacted for exposition purposesFixed EffectsYesYesYesYesObservations34,87634,87634,87634,876R20.250.250.240.24Adjusted R20.250.250.240.24Note: This table reports coefficients from difference in difference regression for Δ$CoVaRi95,t on one year lag of the BCP variables, state variables, and bank characteristics in panel A and for Δ$CoVaRi99,t in panel B. Each regression has a panel of firms. Newey-west standard errors allowing for up to five periods of autocorrelation are displayed in parentheses *p<0.1;**p<0.05;***p<0.01.626 (20*31.28) bps of quarterly market equity losses at the original BCP scaleSlide12
European Banks
Dependent variable:
Panel A: Δ
$
CoVaR
i
95,t
Panel B: Δ
$
CoVaRi99,tOverall BCP1.32*** 1.97*** (0.25)
(.70)
Supervisors BCP
-0.62***
-0.40*** (0.20) (0.17)Banks BCP 1.89*** 2.15*** (0.58) (1.01)Other coefficients estimates redacted for exposition purposesFixed EffectsYesYesYesYesObservations6,7946,7946,7946,794R20.510.510.540.54Adjusted R20.510.510.530.53Note: This table reports coefficients from difference in difference regression for Δ$CoVaRi95,t on one year lag of the BCP variables, state variables, and bank characteristics in panel A and for Δ$CoVaRi99,t in panel B. Each regression has a panel of firms. Newey-west standard errors allowing for up to five periods of autocorrelation are displayed in parentheses *p<0.1;**p<0.05;***p<0.01.39 (20*1.97) bps of quarterly market equity losses at original BCP scaleSlide13
Bank BCP principle level Analysis
Panel A: SIBs
Panel B: North American Banks
Panel C: European Banks
Dependent variable:
Δ
$
CoVaR
i
95,
Δ$CoVaRi99,Δ$CoVaRi95,Δ$CoVaRi99,Δ$CoVaRi95,Δ$CoVaRi99,
Supervisors BCP
-23.30**
-44.49**
-21.74***
-27.99***
-13.38***
-16.68*** (9.02)(6.95)(9.92)(9.21)(4.46)(6.37)Capital Adequacy 130.35**141.33***77.82***82.60***92.63***94.20*** (8.56)(17.18)(12.32)(30.99)(3.37)(4.68)Credit Risk-11.02-15.38-9.02**-8.38-11.24***-12.20** (15.04)(31.18)(5.04)(5.18)(3.87)(5.39)Concentration Risk-2.04-5.44-6.58-9.37-5.30-7.80 (10.43)(20.88)(13.74)(27.66)(4.28)(6.28)Market Risk-25.14**-44.31***-15.14**-24.21***-30.03**-20.50*** (11.52)(3.78)(3.52)(3.78)(3.65)(5.59)Liquidity Risk-33.14***-30.28***-23.10***-20.11***-30.74***-36.97*** (14.50)(6.04)(7.10)(4.04)(6.12)(9.11)Operational Risk-11.55-12.85-12.15-9.15**-11.39-8.41 (13.63)(29.45)(9.13)(5.45)(3.69)(5.44)Observations3,0823,08234,87634,8766,7946,794R20.610.600.250.240.510.54Adjusted R20.610.600.250.240.510.53 Note: This table reports coefficients from difference in difference regression for Δ$CoVaRi95,t on one year lag of the BCP variables, state variables, and bank characteristics in panel A and for Δ$CoVaRi99,t in panel B. Each regression has a panel of firms. Newey-west standard errors allowing for up to five periods of autocorrelation are displayed in parentheses *p<0.1;**p<0.05;***p<0.01.1640 (20*82.60) bps of quarterly market equity losses at original BCP scaleSlide14
Concluding RemarksOverall there is evidence that compliance has
positive predictive
effect on systemic risk
.
This effect is most prominent in the North American system equating to 6.26% of quarterly market equity return losses.
Encouragingly what supervisors are doing reduces systemic risk
Banks’ BCP compliance increases systemic
risk and is driving by capital adequacy policies
This latter finding
suggests
that capital regulation based on a bank’s own risk can have the unintended consequence of extenuating systemic
risk (Acharya ,2009)Slide15Slide16
Dynamic DiD Regression
BCP assessment are applied to different countries at different points in time.
Multiple groups and time periods allows us to examine the effects of BCP compliance by applying a general framework
DiD
introduced by Bertrand et al (2004).
Furthermore we use a forward looking specification to better understand the (countercyclical) macro prudential policy implications.
This specification captures the stylised fact that systemic risk is building up in the background, especially during low volatility periods.
Where
i
indexes individual banks, j indexes countries, sys indexes system, and t indexes quarters.
group/time period covariate (
)
Bank specific controls for systemic risk drivers (leverage, size, and maturity mismatch and Boom indicator)
captures the sensitivity of bank systemic risk to BCP compliance.
Slide17
Systemic RiskSystemic risk has two economic components:Volatility paradox
: a build up of background risk in times of credit boom when contemporaneous risk is low (time series component)
Spillover
effects:
the amplification of adverse shocks in times of crisis (cross sectional component)
Contemporaneous measures of systemic risk will only capture 2
Regulation that relies on contemporaneous measures suffers from a
procyclicality
pitfall.
Unnecessarily tight after period of adverse shock.
Unnecessarily loose in period of financial stability.