Forecasting systemic impact in financial networks ∗Nikolaus Hautsch

Julia Schaumburg

Melanie Schienle

Abstract
We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9
financial crisis, we demonstrate how the approach can be used for timely systemicrisk monitoring of large European banks and insurance companies. We predict firms’
systemic relevance as the marginal impact of individual downside risks on systemic
distress. The so-called systemic risk betas account for a company’s position within
the network of financial interdependencies in addition to its balance sheet characteristics and its exposure towards general market conditions. Relying only on publicly available daily market data, we determine time-varying systemic risk networks,
and forecast systemic relevance on a quarterly basis. Our empirical findings reveal
time-varying risk channels and firms’ specific roles as risk transmitters and/or risk
recipients.

Keywords: Forecasting systemic risk contributions, time-varying systemic risk network, model selection with regularization in quantiles
JEL classification: G01, G18, G32, G38, C21, C51, C63∗

Nikolaus Hautsch, Department of Statistics and Operations Research, University of Vienna, and Center
for Financial Studies, Frankfurt, email: nikolaus.hautsch@univie.ac.at. Julia Schaumburg, Faculty of Economics and Business Administration, VU University Amsterdam, email: j.schaumburg@vu.nl. Melanie
Schienle, CASE and Leibniz Universität Hannover, email: schienle@ewifo.uni-hannover.de. Researchsupport by the Deutsche Forschungsgemeinschaft via the Collaborative Research Center 649 “Economic
Risk” is gratefully acknowledged. Hautsch acknowledges research support by the Wiener Wissenschafts-,
Forschungs- und Technologiefonds (WWTF). J. Schaumburg thanks the European Union Seventh Framework Programme (FP7-SSH/2007-2013, grant agreement 320270 - SYRTO) for financial support.

11. Introduction
The breakdown risk for the financial system induced by the distress of an individual firm
has long been neglected in financial regulation. Up to the financial crisis 2008-2009,
this systemic risk has been exclusively attributed to the idiosyncratic risk of an institution, abstracting from the strong network cross-dependencies in the financial sector causing potential risk spillover effects. In an extensive study for the U.S. financial system,
however, Hautsch, Schaumburg, and Schienle (2012) (HSS) show that it is mainly theinterconnectedness within the financial sector that determines the systemic relevance ofa particular firm, i.e. its potential to significantly increase the risk of failure of the entire system - denoted as systemic risk. To quantify the systemic impact of an individualcompany, they propose the so-called realized systemic risk beta, the total effect of a company’s time-varying Value at Risk (VaR) on the VaR of the entire system. Thus realized
systemic risk betas measure a firm’s contribution to systemic risk which then acts as ameasure for its systemic relevance. Firms’ tail risk is determined from company-specificrelevant factors among other companies’ tail risks, individual balance sheet characteristics, and financial indicators, where components are selected as being “relevant” via adata-driven statistical regularization technique. The resulting individual-specific models
give rise to a financial risk network, capturing exposures of financial firms towards the
distress of others. These network risk spill-over channels contain important information
for supervision authorities as sources for systemic risk. Their data-driven determinationof firms’ systemic relevance from publicly available data distinguishes HSS from thenumber of other recently proposed methods for refined measurement and prediction ofsystemic risk, see, e.g., Adrian and Brunnermeier (2011), White, Kim, and Manganelli(2010), Huang, Zhou, and Zhu (2009), Brownlees and Engle (2011), Acharya, Pedersen,
Philippon, and Richardson (2010), Giesecke and Kim (2011), Billio, Getmansky, Lo, andPelizzon (2012), Koopman, Lucas, and Schwaab (2011), Engle, Jondeau, and Rockinger(2012), or Schwaab, Koopman, and Lucas (2011) among many others.

2Effective supervision requires models which can be used for forecasting and which arereliable even if estimation periods are short. The original HSS framework, however, is
not tailored to short-term forecasting of systemic risk and must be adapted for prediction
purposes. Firstly, the HSS-systemic risk network is static, i.e., it is estimated once using the entire dataset and then forms the basis for estimation of respective time-varying
realized betas. However, empirical evidence suggests that network links might changeover time, especially in crisis periods. Secondly, in order to exploit additional variation,quarterly balance sheet characteristics are interpolated by cubic splines over the analyzed
time period. Therefore, out-of-sample forecasting is not possible. Thirdly, the penalty
parameter required for the model selection step is chosen such that a backtest criterion
is optimized. VaR backtests, however, generally rely on counting and analyzing VaR exceedances, which is reasonable when the time series is long. Though for short estimation
periods, these tests should be replaced by more adequate quantile versions of F-tests.
In this paper, we extend the HSS framework to allow for flexible systemic risk forecasting. The estimation period is shortened using rolling windows of only one year of
data. This excludes influence of back-dated events on current forecasts while still pertaining sufficient prediction accuracy. The models are re-estimated each quarter, resulting
in time-varying systemic risk networks. Instead of interpolating, information on firmspecific balance sheets is only updated when it is published at the end of each quarter.
The model selection penalty is chosen such that the in-sample fit in the respective annual
observation window is optimal. This is examined via an F-test for quantile regression.
The empirical analysis investigates systemic risk in Europe. The data set covers stock
prices and balance sheets of major European banks and insurance companies as well as
financial indicators, including country-specific variables, during the period around the
2008/9 financial crisis. We illustrate that our approach could serve as a monitoring tool
for supervisors as it captures and effectively predicts systemic relevance over time.
The remainder of the paper is structured as follows. Section 2 outlines the forecasting
methodology. It provides an algorithm for model selection and estimation of firm-specific3

VaRs and introduces how to estimate and forecast realized systemic risk betas. Section
3 describes the data set. Estimation results, their detailed implications and respective
robustness checks are contained in Section 4. Section 5 concludes.

2. Forecasting Methodology
We extend the framework of Hautsch, Schaumburg, and Schienle (2012) (HSS) and the
HSS measure for systemic relevance in the presence of network effects, the realized systemic risk beta. Whereas HSS focus on a single static network as a basis for estimating
systemic impact of financial institutions, we progress by determining time-varying networks in a forecasting setting. These allow capturing changing risk spillover channels
within the system, which are tailored to short-term forecasts from the model.

2.1. Time-Varying Networks
In a densely interconnected financial system, the tail risk of an institution i at a timeipoint t is determined not only by its own balance sheet characteristics Zt−1
and generalmarket conditions Mt−1 but also by indications for distress in closely related banks inthe system. For each bank in the system, we regard a corresponding return observation as
marking a distress event whenever this return is below the empirical 10% quantile. In suchcases, these extreme returns might induce cross-effects on the riskiness of other banks inthe system. We record these as so-called loss exceedances, i.e., the values of returns incase of an exceedance of the 10% quantile and zeros otherwise. Accordingly, the set of
potential risk drivers R for a bank i therefore comprises network impacts Nt−i from anyother bank in the system, where each component of Nt−i consists of loss exceedances forany bank but firm i in the system.
We measure tail risk by the conditional Value at Risk, V aRi , for firm i and by V aRs
for the system, respectively. Using a post-LASSO technique as in HSS, the large set of4i, Mt−1 , Nt−i ) for institution i can be reduced to a grouppotential risk drivers Rt = (Zt−1(i)

of “relevant” risk drivers Rt . Selected tail-risk cross-effects from other banks in the system constitute network links from these banks to institution i. Repeating the analysis forall banks i in the system, relevant risk channels can be depicted and summarized in a respective network graph. The recent financial crisis, however, has shown that such network
interconnections may change over time as the relevance of certain institutions for the riskof others might vary substantially. Thus adequate short-run predictions of systemic importance should mainly be based on current dependence structures. We address this issue(i,t)

by a time-dependent selection of relevant risk drivers Rt

according to the algorithm de-scribed below. Driven by the quarterly publication frequency of companies’ balance sheet
information we re-evaluate the relevance of all potential risk drivers for each institution
in the system at the beginning of each quarter based on data from the respective previous
12 months and incorporate the latest balance sheet news. We therefore obtain quarterly
time-varying tail risk networks which reflect the most current information of risk channels
within the financial system. They are tailored for short-term quarterly predictions of thesystemic riskiness of firms in the system.
With the relevant risk drivers R(i,t) for firm i and time t in a specific quarter, individual
tail risk can be determined from observations up to one year before t as
i

(i,t)
V[
aRt = ξb0i,t + ξbi,t Rt ,(1)

where coefficients ξb are obtained in the post-LASSO step from quantile regression of X ion (1, R(i,t) ) as part of the procedure described below.

Selecting relevant risk drivers and determining their effects in firms’ tail risk
We adapt the data-driven procedure of HSS to account for time-variation in tail risk networks and marginal systemic risk contributions. The automatic selection procedure isbased on a sequential F-test in contrast to the backtest criterion in HSS. Determination of5relevant risk drivers R(i,t0 ) at the beginning of a quarter t0 uses information of observations within the previous year. Hence it is based on approximately τ = 250 observations
Rt0 −τ , . . . , Rt0 , where Rt is a K-vector of centered observations of the potential regressors. We fix a ν-equidistant grid ∆c = {c1 > . . . > cl = c1 − ν(l − 1) > cL = 0} forvalues of a constant c, where c1 is chosen such that the corresponding penalty parameteris sufficiently large for selecting not more than one regressor into the model. For our
purposes, we set c1 = 30 and ν = 1.
Step 1: For each c ∈ ∆c , determine the penalty parameter λit0 (c) from the data in thefollowing two sub-steps as in Belloni and Chernozhukov (2011):
Step a) Take τ + 1 iid draws from U[0, 1] independent of Rt0 −τ , . . . , Rt0 denoted as
U0 , . . . , Uτ . Conditional on observations of R, calculateτ

1 X Rt0 −t,k (q − I(Ut ≤ q))p.
1≤k≤K τ + 1
σ̂
q(1
−
q)
k
t=0

Λit0 = (τ + 1) max

Step b) Repeat step a) B=500 times generating the empirical distribution of Λit0
conditional on R through Λit0 1 , . . . , Λit0 B . For a confidence level α = 0.1 inthe selection, set
λit0 (c) = c · Q(Λit0 , 1 − α|Rt0 −t ),where Q(Λit0 , 1 − α|Rt0 −t ) denotes the (1 − α)-quantile of Λit0 given Rt0 −t .
Step 2: Run separate l1 -penalized quantile regressions for λit0 (c1 ) and λit0 (c2 ) from step
1 and obtainτξeqit0 (c) = argminξi1 X
ρq Xti0 −t + Rt0 0 −t ξ i + λit0 (c)
τ +1
t=0

p
K
q(1 − q) X
σ̂k |ξki | , (2)τ
k=1with the set of potentially relevant regressors Rt0 −t = (Rt0 −t,k )K
k=1 , componentwisePτ1
2
variation σ̂k2 = τ +1
t=0 (Rt0 −t,k ) and loss function ρq (u) = u(q − I(u < 0)),where the indicator I(·) is 1 for u < 0 and zero otherwise.

6

Step 3: Drop all components in R with absolute marginal effects |ξeti0 (c)| below a threshold τ = 0.0001 keeping only the K it0 (c) remaining relevant regressors R(i,t0 ) (c)for c ∈ {c1 , c2 }. As c1 > c2 , the sets of selected relevant regressors are nested
R(i,t0 ) (c1 ) ⊆ R(i,t0 ) (c2 ) = {R(i,t0 ) (c1 ), R(i,t0 ) (c2 \c1 )}. If R(i,t0 ) (c2 \c1 ) is the empty
set, restart Step 2 with λi (c2 ) and λi (c3 ) from Step 1. Otherwise re-estimate (2)without penalty term for the larger model c2 only with the respective selected relevant uncentered regressors R(i,t0 ) (c2 ) and an intercept. This regression yields theit0
post-LASSO estimates ξc(c ). Apply an F-test for joint significance of regressorsq2

R(i,t0 ) (c2 \c1 ). If they are significant, restart Step 2 with λi (c2 ) and λi (c3 ) from Step
1b. Continue until additional regressors R(i,t0 ) (cl+1 \cl ) from penalty cl to cl+1 are
no longer found to be significant. Then the final model is obtained from cl yielding
the set of relevant regressors R(i,t0 ) (cl ) with corresponding post-LASSO estimatesit0ξc(c ) for the coefficients.
qlNote that we aim at keeping the model parsimonious. Therefore we set the significancelevel underlying the F-test in Step 3 to 5%. This corresponds to the minimum feasiblelevel still guaranteeing stability of the procedure given the available sample size and thesubstantial correlation structure of regressors in the LASSO selection step. We found that
imposing higher accuracy of lower F-test levels, tends to induce robustness problems suchas non-nested models in the sequential upward procedure. In contrast, higher significancelevels generally result in larger systemic risk networks corresponding to a wider view ofpotential “relevance”.

7

2.2. Forecasting Systemic Impact
In an interconnected financial system, we measure the systemic risk impact of a specificbank i as the total realized effect of its riskiness on distress of the entire financial systemgiven network and market externalities.1 This can be empirically determined viai(−i,t)i∗ [V aRst = αs,t + β s|i,t (Zt−1)V aRt + γ s,t Mt−1 + θs,t V[
aRtwhere V[
aR

(−i)

,

(3)

comprises tail risks of all other banks in the system selected as relevantrisk drivers for bank i in the corresponding network topology. The marginal effect β s|i,t of
the risk of company i might vary linearly over time in selected firm-specific balance sheeti∗. Coefficients in (3) can be obtained via standard quantile regression
characteristics Zt−1

analogously to (2) without penalty term. Corresponding to the one-year estimation window for the time-varying network, we also determine parameters in (3) at the beginningof each quarter, based on observations dating back no longer than one year. The systemic
relevance of a company can then be predicted from the beginning of a quarter t0 to thenext quarter t0 + τ̃ as realized betais|i
βet0 +τ̃ |t0 − = βbs|i,t0 (Zti∗0 −1 )V[
aRt0

(4)

where t− denotes information up to time t. Within a quarter, predictions are updated byis|i
βet+1|t− = βbs|i,t0 (Zti∗0 −1 )V[
aRt

(5)

for any time point t0 ≤ t ≤ t0 + τ̃ .
1Please note that we use the terms ‘systemic impact’, ‘systemic relevance’, and ‘systemic risk (contribution)’ synonymously.

8

3. Data
Our sample of financial firms comprises 20 European banks and insurance companies. A
list can be found in Table 2. The dataset covers Europe-based banks deemed as systemically relevant by Financial Stability Board (2011), for which complete data sets over theconsidered period are available.2 It includes the ten largest European banks by assets in2010. Furthermore, six insurance companies are selected, all belonging (by assets) to the
top 10 insurers in the world in 2010. The regressors explaining the individual Value at
Risk (V aRi ) are selected among other companies’ loss exceedances, individual balance
sheet ratios, and several financial indicators, including country-specific variables.
From quarterly balance sheets obtained from Datastream/Worldscope, three key ratios
are calculated: Leverage, correponding to total assets divided by total equity; maturity
mismatch, the quotient of short-term debt and total debt; and size, defined as the logarithm of total assets. Furthermore, we include quarterly stock price volatility in the setof possible regressors, which is estimated over the time span between quarterly reports.
Instead of interpolating the data to daily values, we keep them constant until new information is published.3
The set of financial indicator variables contains the return on EuroStoxx 600, relative changes of the volatility index VStoxx, and returns on three major bond indicesfor Europe: IBOXX Sovereign, containing government bonds, iBOXX Subsovereigns,
consisting of bonds issued by government owned banks, supranationals and other subsovereigns, and iBOXX Corporates. Furthermore, we include changes in three months
Euribor, the interbank lending interest rate, and a liquidity spread between three months
Eurepo, the average repo rate reflecting the cost of repurchase agreements, and the threemonth Bubill (German government bond rate) as proxy for the risk free rate. To capture
aggregate credit quality in Europe, we also add the change in the one year and five year2

Banco Espirito Santo is the only bank which is not listed by the Financial Stability Board. We include
it because otherwise, financial firms from Southern Europe would be underrepresented.
3
For simplicity, we assume that quarterly balance sheets become public information on fixed dates:
March 31, June 30, September 30 and December 31.

9default probability indices from Fitch as well as the change in the five year continued
series of the credit default swap index iTraxx Europe. Another two relevant economic
indicators are the gold price and relative changes of the MSCI Europe Real Estate Price
Index.
As proxies for the market’s expectations on economic growth and to capture countryspecific effects on individual VaRs, we include several ten year government bond yields(Germany, United Kingdom, Spain, United States, and Greece) as well as yield spreads(ten years minus three months yields) of German and U.S. government bonds. Finally,
accounting for the global interconnectedness of financial markets, we include returns on
financial sector indices, FTSE Financials Japan, Asia, and US.
When estimating systemic risk betas in the second stage, a subset of the above macrofinancial indicators is required as control variables. Here, we take the changes in the
EuroStoxx 600 index, VStoxx, Euribor, iTraXX, the three FTSE Financial indices, thereal estate index, and the spread between Eurepo and the Bubill rate.

4. Results
4.1. Time-varying tail risk networks
Having identified the tail risk drivers for each firm allows constructing a tail risk network.
Following HSS, we take all firms as nodes in a network and identify a network link fromfirm i to firm j whenever the loss exceedance of i is selected as a tail risk driver for j.
Figures 1 to 4 show the resulting systemic risk networks for the 20 financial institutions
computed based on one-year rolling windows from 2006 to 2010. In order to illustratecross-country and inter-country risk channels, we order the institutions in the graph according to their (main) home countries.

10With our analysis we can statistically determine “relevant” (directed) risk connectionsin the financial network. Identifying the underlying economic causes for a link between
two companies, however, is more difficult given the available data. Nevertheless, the inclusion of firm-specific characteristics and macroeconomic state variables allows us to acertain extent to control for situations where firms have (similar) exposure to the same risk
factors. Such factors cause bi-directional relationships due to firms’ dependence on common situations, such as, for instance, periods of high volatility, flattening of yield curves,increased sovereign default or falling overall credit quality. Accordingly, the identified
risk connections are not due to companies’ exposure to same economic conditions or risk
factors as captured by the included control variables, but are caused by (possibly remaining) factors inherently related to inter-bank connections. These are most likely counterparty relations (i.e., one firm is the counterparty of the other) and/or the same exposure to
toxic assets in firms’ balance sheets.
In this spirit, we identify several risk connections which remain quite stable over time
and thus appear as fundamental risk channels of the European financial network during
the period under consideration. An interesting such case is the tail risk connection between Deutsche Bank and various big insurance companies, particularly Allianz as well
as between Deutsche Bank and Commerzbank. The latter faced significant distress due
to investments in toxic assets originating from the U.S. housing market, and was the firstcommercial lender in Germany accepting capital injections from the government. In the
beginning of 2009, Commerzbank was partly nationalised with the government taking a
25% stake. Our analysis reflects that the distress of Commerzbank also spilled over toDeutsche Bank. Hence, governmental support of Commerzbank was an important step
to reduce its systemic risk contribution. This is empirically in line with our study as we
observe a declining tail risk connectedness of Commerzbank after the bailout. Persistentrisk connections are also identified between Royal Bank of Scotland (RBS) and Barclays.
Despite their perceived quite different situation (see Table 1 ), the network analysis, however, reveals that both banks have been deeply connected. Being bi-directional before
the crisis, the links became particularly pronounced and rather one-directional during the11Table 1: Schematic overview of the situation of two UK banks during the sample period.
RBS
April 2008: substantial write-downs due to
break-down of U.S. housing and credit markets
Start 2009: record loss, bailed out by UK government (stake increase to 70%)

Barclays
April 2008 and before: relatively well funded,even explored options to take over Lehman
Brothers
Fall 2008: raise of new capital by investors
Start 2009: no participation in government’s
insurance schemes for toxic assets required.

financial crisis. Probably caused by counterparty relations, RBS received substantial tailrisk from Barclays further increasing RBS’s potential losses and making both companies
systemically risky. The strong risk connection between Barclays and RBS vanishes in theaftermath of the financial crisis which might be a result of RBS’s bailout and ongoingre-structuring in both banks.
Furthermore, the networks reveal persistent connections between UBS and Credit Suisse, UBS and Crédit Agricole, Agricole and Société Générale as well as Credit Suisseand Agricole. The strong interconnections between these Swiss and French banks arelikely to be driven by exposure to the same toxic assets and resulting liquidity shortages
stemming from the U.S. market making these banks facing common funding problems.
This happened during 2008/09, where all of these banks also received substantial tail risk
spillovers from others. For instance, our analysis reveals that Credit Suisse was subject
to tail risk inflow from Barclays and BNP Paribas which - according to the identified network connections - spilled over to the ’risk neighbors’ of Credit Suisse. All of these banksreceived bailout packages from the Swiss and French government, respectively. As a possible consequence of these bailouts and a relaxation of the bank’s funding situation in theaftermath, Credit Suisse’s sensitivity to tail risk inflow from Barclays and BNP Paribas
actually declined in 2009.
Although all of these institutions operate on a global level, we still observe a substantial extent of persistent country-specific risk channels. These effects reflect a strong interconnectedness and consequently inherent instability of national banking systems. These
within-country dependencies are complemented by cross-country linkages and industry-

12

specific channels. Examples for the latter are tail risk connections prevailing within theinsurance sector including Allianz, AXA, Aviva, Münchener Rück and Aegon. Their interconnectedness even increased during the financial crisis which is likely to be caused byexposure to the same classes of toxic assets.
Our approach, however, also captures interesting time variations in tail risk channels.
In particular, in 2008/09, we observe high fluctuations of network connections which are
likely to be caused by counterparty relations in combination with funding liquidity shortages. Accordingly, they vanished in the aftermath of the crisis. Examples are connections
from Santander to HSBC, BNP Paribas, Allianz and AXA. These links make Santander
systemically quite risky as the bank obviously produced and transmitted tail risk to various major players in the system. These findings are confirmed by the estimated systemicrisk betas shown below. A further example is a strong connection between ING and Aviva
which built up and increased through the crisis and vanished thereafter. The Dutch bank
ING realized significant losses, had to cut jobs in 2009 and received capital injections
from the Dutch government.
Analyzing the pure number of outgoing tail risk connections (illustrated by the sizeof nodes in the network graphs), we identify Barclays, Santander, AXA, BNP Paribas,
ING, Société Générale and Crédit Agricole as deeply connected companies. Actually,
the latter four were companies which have been bailed out by their governments and got
partly nationalized. Our analysis indicates that these governmental capital injections wereindeed justifiable as these companies have been (and still are) in the core of the networkand therefore serve as distributors and multipliers of systemic risk. According to theidentified network connections, failure of one of these institutions would substantiallythreaten the stability of the financial system.

13

4.2. Systemic risk rankings
After having determined individual companies’ VaRs, realized systemic risk betas can be
estimated and forecasts for each quarter can be computed according to equation 4. Table
4 reports systemic risk rankings for all quarters between the beginning of 2007 and theend of 2010. They are based on realized systemic risk betas at the end of the respective
foregoing quarter, and therefore contain forecasts of relative systemic relevance. Prior to
the estimation, we conducted a test on joint significance of V aRi and V aRi · Z i∗ with i =1, ...20, for V aRs , using all five years of data. Apart from two exceptions, all individual
VaRs turn out to be statistically significant for the system’s VaR. The two exceptions
are, on the one hand, Banco Espirito Santo, which is the largest bank in Portugal, butmuch less internationally active than the other banks in our sample. On the other hand,Société Generale is found to be insignificant. We attribute this finding to the fact that in2008, the bank was affected by large losses induced by the unauthorized propriety tradingof one of its employees. This was a materialization of (idiosyncratic) operational risk,
and may have distorted the test results concerning systemic relevance. We expect thaton a longer horizon, Société Generale’ systemic risk beta would be significant. In thefollowing, however, we exclude it from the systemic risk rankings, together with Banco
Espirito Santo.
It should be noted, that often differences in beta estimates between direct neighboring firms in the obtained rankings are small and thus not statistically significant. Henceorderings in Table 4 should rather be seen as an indication for a company’s relative systemic importance characterizing groups of similar relative systemic impact. We therefore
suggest a ”traffic light system” of high, medium and low ranked systemically risky banks
as reported in Table 3. 4 Clearly, when a financial firm is distressed, bailout decisions
should not be based solely on this categorization at the respective point in time. Instead,4

At some time points, estimated systemic risk betas become negative. We interpret this finding as
negligible systemic impacts of the respective firm in the respective quarter and therefore omit it in the
ranking.

14

the evolvement of the measure should be observed over time and past periods should betaken into consideration, in order to obtain a full picture of the firm’s systemic impact.
Figure 5 illustrates the time-varying cross-sectional distribution of the estimated betas
and the three traffic light groups. We observe the overall highest systemic risk betas during
the height of the financial crisis. Furthermore, representatively for other firms, we depict
the estimated systemic impacts of Barclays, Crédit Agricole, Santander and UBS. It turns
out that the respective systemic risk betas move in locksteps before mid 2008, but strongly
diverge during the crisis. Similar relationships are also shown for other companies andreflect distinct crisis-specific effects.
These effects are supported by the pointwise, ungrouped results in Table 4 revealingstrong variations of the relative systemic riskiness during the crisis. This is obviously
induced by a severe instability of the financial system during this period and is also confirmed by the high variability of network connections as discussed above. Conversely, ahigher stability of systemic risk patterns over time is observed in the periods before andafter the financial crisis (i.e., 2007 and 2010). Note that the high variation of pointwise
predicted systemic risk betas is neither an artefact of the LASSO-procedure for network
selection nor an indication of problems in selecting the penalization constant in practice.
Plots of estimated individual VaRs rather reveal a major part of the volatile behavior stemming from the hard thresholding with which other companies’ loss exceedances are measured and thus appear and disappear as potential candidates for network links over time.
We leave it for future work to determine appropriate smoothed versions of exceedances.
In our study we remain conservative towards the type II error in detecting network links
and keep the extreme cut-off behavior where firms can only be risk drivers if in distressand not on the way towards potential distress. Our recommended classification of firms
for supervisors in this setting is thus broad and groupwise as shown in Table 3.
Overall, we identify BNP Paribas, HSBC and Santander as being most risky with thehighest realized risk betas between 2007 and 2010. BNP Paribas was strongly affectedby the credit crunch and an evaporation of liquidity in the funding market in 2007/08
15

and was bailed out by the French government end of October. Our findings reflect thatafter the bailout, BNP’s systemic riskiness was still comparably high. According to the
network analysis above, this is obviously due its strong interconnectedness, particularly
in 2010. In contrast HSBC’s connectedness is only moderate. However, its size andthe identified tail risk connections to Barclays, BNP and Santander make it systemicallyquite risky. These connections became obviously quite relevant due to HSBC’s heavy
exposure to U.S. housing and credit markets. Consequently, the bank’s distress inducedby significant losses during the crisis have been spread out in the system resulting ina particularly high systemic riskiness around beginning of 2009. Our results indicatethat also in the aftermath of the crisis, HSBC still remains systemically quite risky. In
case of Santander, the relative systemic riskiness (compared to other banks) even tends
to increase after the financial crisis (particularly in 2010). This finding might alreadyindicate funding problems in the Spanish banking market becoming particularly evident in2012. These results are in line with the findings of the network analysis above identifying
Santander as a deeply interconnected bank being linked to several insurance companiesand (particularly during the crisis) to other major players like Barclays and HSBC.
Monitoring systemic risk rankings over the course of the financial crisis provides interesting insights into the systemic importance of individual firms under extreme conditionsof market distress. Four prominent examples are RBS, Barclays, Deutsche Bank and
HBSC. According to the estimated systemic risk betas, we classify RBS as belonging
to the most systemically risky companies in 2008. Also Barclays is identified as being
systemically very relevant in several (though not all) periods in 2008/09. The identified
network connections revealed that the strong connection between Barclays and RBS was
obviously one driving force of the systemic relevance of both. This is also confirmedby the fact that the systemic relevance of both (as indicated by the realized betas) declined as the tail risk connection between both vanishes in 2009. Likewise, Deutsche
Bank faces a steady increase of its systemic relevance in 2007 and belongs to the groupof systemically most risky companies in 2008. This is confirmed by the network analysis
above showing that particularly during 2008, Deutsche Bank was deeply interconnected16with risk channels to various major insurance companies. Although Deutsche Bank was
not subject to any government bailouts it went through a process of substantial internal
restructuring. This is confirmed by our estimates showing a decline of Deutsche Bank’ssystemic importance during 2009 and 2010. Finally, for the post-crisis period, we observea tendency for the insurance companies becoming relatively more risky. Particularly in2010, Allianz, Aviva, Axa, Generali and Münchener Rück reveal relatively high (though
not always significant) systemic risk betas. Likewise, also Société Générale and Credit
Suisse are identified as systemically risky in 2010. These findings are confirmed by the
network analysis showing a comparably high connectedness of Société Générale, Axa andGenerali.
To analyze whether systemic risk betas are related to companies’ balance sheet characteristics, we compare rankings of quarterly averaged realized systemic risk beta estimates
to rankings according firms’ size, leverage, and maturity mismatch. In particular, weestimate Kendall’s rank correlation coefficient according toτb =number of concordant pairs − number of discordant pairs.
0.5n(n − 1)τb is known to be more robust towards deviations from normality than the Pearson correlation coefficient (see, e.g., Dehling, Vogel, Wendler, and Wied (2012), and aims directly
at comparing the ordering of variables.
To distinguish between a pre-crisis and (post) crisis period, we compute Kendall’s τfor pooled data from 2006 to the end of 2007 (8 quarters) as well as for the subsequent
period including the crisis and its aftermath (12 quarters). Table 5 reports the estimatedrank correlations together with the outcomes of one-sided significance tests, with the null
hypothesis H0 : τ ≤ 0. Based on the pre-crisis period, we find that correlations of 0,11
between systemic risk betas and leverage as well as maturity mismatch are significant
at a 5% level, whereas the correlation with size is smaller and only significant at 10%.
These results indicate that even in non-crisis periods mainly network effects do drive predictions of systemic relevance in realized systemic risk betas rather than idiosyncratic
17

characteristics. Within the firm specific effects, we also find that size is not the dominating factor which is in contrast to the well-known ”to big to fail” statement. Important
idiosyncratic risk drivers are rather leverage and funding risk, approximated by maturity
mismatch. During the (post) crisis period, estimated correlations become insignificant
and are virtually zero. This shows that from 2008 onwards, the influence of observablefirm characteristics even decreases further and network connections are the pre-dominant
drivers for short-term predictions of firm’s systemic riskiness. This also corresponds to a
sharp increase of realized systemic risk beta forecasts as shown in Figure 5.

4.3. Out-of-sample validation of forecasts
A direct evaluation of realized systemic risk beta forecasts is not possible, since theycannot be observed even ex post. As systemic risk betas measure the effect of firms’ tailrisk on the tail risk of the system, an observable proxy benchmark is the tail correlation
between the system return and each individual company’s return. Accordingly, for a firstrough forecast validation setting, we compute quarterly tail correlations based on 10%
quantiles balancing the need of a sufficient number of observations on the one hand andthe need to capture tail risk. In particular, we estimate the correlations for each quarter k
as

s
i
s
s
i
i
ρ̄s,i
k = corrk X , X |X < q0.1 (X ), X < q0.1 (X ) ,for from observations Xts , Xti with t = t0,k + 1, ..., t0,k + τ̃k for each end-of-quarter time
point t0,k , where τ̃k denotes the length of the next quarter, using the Pearson correlation
coefficient, see e.g. Ang and Chen (2002).
As a naive benchmark for assessing a firms’ marginal relevance in the financial system,
we compute a simple financial system CAPM-type beta defined as the slope coefficient intime series regressions of individual returns on the system return. We take this simplisticcompetitor as a lower bound benchmark, which is much easier to obtain than our realized
systemic risk beta but is obviously ”naive” as it does not account for tail dependencies but

18

just mean dependencies and reverts the causality between system returns and individual
returns. To evaluate the two different forecasts, we compute the R2 in separate forecast
regressions of the forms,ib̄ρk = γ0 + γ1 bik + εs,i
k

(6)

n
o
s|i
M,i
where k is the quarter index and bi ∈ βet0 +τ̃ |t0 , βtCAP. The higher the respective R2 ,0 +τ̃ |t0
the more variation in future tail correlation is explained by the respective systemic risk
forecast. Boxplots of all R2 s for the different companies are shown in Figure 6. It turns
out that the realized systemic risk beta clearly outperforms the ”financial system beta” inforecasting future tail dependence between the system and individual banks and insurancecompanies.
In a second forecast evaluation scenario, we study the ability of the two measures to
explain variations in returns in periods of extreme (negative) realizations denoted as the
10% worst outcomes of equity returns for each firm. Accordingly, we take the average¯ i of all firms in the quarter k following the estimation period,
10% loss exceedances Ex
and run cross-sectional regressions thereof on the respective realizations of the two competing betas,¯ i = ζ0 + ζ1 bi + εs,i
Ex
t ,
k
k

(7)

with bik defined as above. Such a regression has some analogy to a typical second pass
CAPM regression linking cross-sectional variations in excess returns to the cross-sectionalvariation in market betas. Although the systemic risk beta is not tailored to such a setting,our findings in Figure 7 show that on average it provides a better prediction of extreme
market valuations than a the simple financial system CAPM-type beta.

5. Conclusion
In this paper, we propose a framework for forecasting financial institutions’ marginal contribution to systemic risk based on their interconnectedness in terms of extreme downside19

risks. There are four major challenges in this context: Firms’ (conditional) tail risks
are unobserved and must be estimated from data. Determining such individual risk levels appropriately results in high-dimensional models due to the large number of potential
network connections. These network dependencies, however, vary substantially over timein the considered hard-thresholding case for cross-effects. Therefore forecasting stability
and responsiveness require careful balancing and yield a traffic light system for systemicrisk forecasts. To tackle these issues, we adapt the two-stage quantile regression approach
by Hautsch, Schaumburg, and Schienle (2012) to a rolling window out-of-sample prediction setting based on time-varying networks.
In a sample of large European banks covering the period 2007 to 2010, the adapted procedure reveals the dynamic nature of interconnectedness and corresponding risk channels
in the European financial system around and during the financial crisis. The time evolution of network dependencies provides valuable insights into a bank’s role in the system
identifying originators and transmitters of tail risk over time. Determined relevant tailrisk connections and systemic risk rankings both provide valuable input for supervisionauthorities. Given the need for better and more timely market surveillance, our approach
can thus serve as a useful vehicle for providing a continuous assessment of systemic risk
dependencies based on market data.

20

Appendix
Table 2: List of included financial institutions. As most of them provide a
broad range of services, we differentiate between banks and insurance companies, according to their main field of business activities. Furthermore, we
state the country their headquarters are located in.
Aegon (Insurance, NL)
Allianz (Insurance, DE)
Aviva (Insurance, UK)
AXA (Insurance, FR)
Banco Espirito Santo (Bank, PT)
Barclays (Bank, UK)
BNP Paribas (Bank, FR)
Commerzbank (Insurance, DE)
Crédit Agricole (Bank, FR)
Credit Suisse (Bank, CH)

Deutsche Bank (Bank, DE)
Generali (Insurance, IT)
HSBC (Bank, UK)
ING Groep (Bank, NL)
Lloyds Banking Group (UK)
Munich Re (Insurance, DE)
Royal Bank of Scotland (Bank, UK)
Santander (Bank, ES)
Société Générale (Bank, FR)
UBS (Bank, CH)

21

22

BancoEspiritoSanto

Aviva

Barclays

ING

HSBC

BancoEspiritoSanto

ING

Aegon

Generali

SocieteGenerale

Generali

CreditSuisse

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole

UBS

Santander

BancoEspiritoSanto

Aviva

Barclays

ING

ING

HSBC

BancoEspiritoSanto

Aviva

Barclays

HSBC

Aegon

LloydsUBS

SocieteGenerale

Generali

CreditSuisse

AXA

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole

UBS

Allianz

BNPParibas
CreditAgricole

CreditSuisse

Estimation period: Q4.2006 − Q3.2007

Generali
SocieteGenerale

RoyalBankScotland

Aegon

LloydsRoyalBankScotland

Estimation period: Q2.2006 − Q1.2007

Figure 1: Estimates of yearly systemic risk network rolled over from Q4/2006 to Q3/2007

Aegon

LloydsRoyalBankScotland

Estimation period: Q3.2006 − Q2.2007

CreditAgricole
SocieteGenerale

BNPParibas

AXA

Commerzbank

Muenchener.Ruck

Allianz

Aviva

UBS

DeutscheBank

CreditSuisse

Barclays

HSBC

LloydsRoyalBankScotland

Estimation period: Q1.2006 − Q4.2006

23

Barclays

ING

BancoEspiritoSanto

Aviva

Aegon

LloydsGenerali
SocieteGenerale

CreditSuisse

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole

UBS

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole

UBS

Estimation period: Q3.2007 − Q2.2008

SocieteGenerale

GeneraliRoyalBankScotland

Aegon

CreditSuisse

BancoEspiritoSanto

Aviva

Barclays

ING

ING

HSBC

BancoEspiritoSanto

Aviva

Barclays

HSBC

Aegon

LloydsGenerali
SocieteGenerale

CreditSuisse

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole

UBS

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole
SocieteGenerale

UBS

Estimation period: Q4.2007 − Q3.2008

GeneraliRoyalBankScotland

Aegon

LloydsRoyalBankScotland CreditSuisse

Estimation period: Q2.2007 − Q1.2008

Figure 2: Estimates of yearly systemic risk network rolled over from Q4/2007 to Q3/2008.

ING

HSBC

BancoEspiritoSanto

Aviva

Barclays

HSBC

LloydsRoyalBankScotland

Estimation period: Q1.2007 − Q4.2007

24

Generali
SocieteGenerale

Commerzbank

Barclays

BancoEspiritoSanto

Aviva

ING

Aegon

LloydsGenerali
SocieteGenerale

CreditSuisse

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole

UBS

Estimation period: Q3.2008 − Q2.2009

RoyalBankScotland

BNPParibas
CreditAgricole

AXA

Santander

Barclays

BancoEspiritoSanto

Aviva

ING

ING

HSBC

BancoEspiritoSanto

Aviva

Barclays

HSBC

Aegon

LloydsUBS

GeneraliUBS

AXASantander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

SocieteGenerale
CreditAgricole

CreditSuisse

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole
SocieteGenerale

CreditSuisse

Estimation period: Q4.2008 − Q3.2009

GeneraliRoyalBankScotland

Aegon

LloydsRoyalBankScotland

Estimation period: Q2.2008 − Q1.2009

Figure 3: Estimates of yearly systemic risk network rolled over from Q4/2008 to Q3/2009.

ING

HSBC

BancoEspiritoSanto

Muenchener.Ruck

Allianz

Aviva

UBS

DeutscheBank

Aegon

CreditSuisse

Barclays

HSBC

LloydsRoyalBankScotland

Estimation period: Q1.2008 − Q4.2008

25

BancoEspiritoSanto

Aviva

Barclays

ING

Aegon

LloydsSocieteGenerale

Generali
SocieteGenerale

CreditSuisse

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole

UBS

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole

UBS

Estimation period: Q3.2009 − Q2.2010

GeneraliRoyalBankScotland

Aegon

CreditSuisse

Barclays

BancoEspiritoSanto

Aviva

ING

ING

HSBC

BancoEspiritoSanto

Aviva

Barclays

HSBC

Aegon

LloydsCreditSuisse

Generali
SocieteGenerale

CreditSuisse

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole

UBS

AXA

Santander

Muenchener.Ruck

DeutscheBank

Commerzbank

BNPParibas

Allianz

CreditAgricole
SocieteGenerale

UBS

Estimation period: Q4.2009 − Q3.2010

GeneraliRoyalBankScotland

Aegon

LloydsRoyalBankScotland

Estimation period: Q2.2009 − Q1.2010

Figure 4: Estimates of yearly systemic risk network rolled over from Q4/2004 to Q3/2010.

ING

HSBC

BancoEspiritoSanto

Aviva

Barclays

HSBC

LloydsRoyalBankScotland

Estimation period: Q1.2009 − Q4.2009

2.0

2007

●

●
●

●

●

●

●
●
●

●

●

●

●
●

●
●

●
●

●
●

●
●

●
●

●

●

●

2008

●
●

●

●
●

●
●

●
●

●

●

●

●

●

●
●

●

●
●

●

●

2009

●

●

●

●

●

●

●

●

●
●

●

●

●

●

●

●

●
●

●

●

●

●

●

●

●

●

●

2010

●

●

●
●

●

●

●

●

●
●
●
●

●
●

●

●

●
●
●

●

●

●

●
●
●

●

●
●

●

Barclays
CreditAgricole
Santander
UBS

Figure 5: Illustration of time-varying risk rankings, highlighting the evolution of realized systemic risk beta forecasts β̃ of four major banks.
The upper shaded area depicts the pointwise range between the maximum and the 75%-quantile of β̃ for all systemically relevant firms. The
lower one marks the corresponding pointwise lower interquartile range of significant realized systemic risk beta forecasts.

realized beta forecast*100

1.5

1.0

0.5

0.0

26

Table 3: Group rankings of systemically risky companies, according to their quarterly realized systemicrisk beta forecasts βes|i (see equation 4). ’High’ systemic risk is reflected by a realized systemic risk beta
above the 75% quantile of all realized systemic risk betas at the respective end-of-quarter time point. Companies listed in the ’medium’ group have realized systemic risk betas above the 25% quantile but below the
75% quantile. ’Low’ represents the ones below the 25% quantile.
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low
high
med.
low

Q1.2007
Aegon, Allianz, Commerzbank, CreditAgricole, Generali
Aviva, AXA, Barclays, BNPParibas, HSBC, ING, Lloyds, RoyalBankScotland
CreditSuisse, DeutscheBank, Munich Re, Santander, UBS
Q2.2007
Aviva, BNPParibas, Commerzbank, DeutscheBank, UBS
Aegon, Allianz, AXA, Barclays, CreditSuisse, ING, Munich Re, Santander
CreditAgricole, Generali, HSBC, Lloyds, RoyalBankScotland
Q3.2007
AXA, Barclays, DeutscheBank, HSBC, ING
Aviva, CreditAgricole, CreditSuisse, Generali, Lloyds, RoyalBankScotland, Santander, UBS
Aegon, Allianz, BNPParibas, Commerzbank, Munich Re
Q4.2007
Aviva, AXA, BNPParibas, DeutscheBank, RoyalBankScotland
Aegon, Allianz, Commerzbank, CreditAgricole, CreditSuisse, HSBC, ING, Munich Re
Barclays, Generali, Lloyds, Santander, UBS
Q1.2008
Barclays, Commerzbank, CreditAgricole, CreditSuisse, Santander
Aegon, Aviva, BNPParibas, DeutscheBank, Lloyds, Munich Re, RoyalBankScotland, UBS
Allianz, AXA, Generali, HSBC, ING
Q2.2008
AXA, CreditAgricole, Generali, Munich Re, RoyalBankScotland
Aegon, Aviva, BNPParibas, Commerzbank, DeutscheBank, HSBC, Lloyds, Santander
Allianz, Barclays, CreditSuisse, ING, UBS
Q3.2008
Aviva, Barclays, CreditSuisse, DeutscheBank, Santander
BNPParibas, Commerzbank, Generali, HSBC, ING, Lloyds, Munich Re, RoyalBankScotland
Aegon, Allianz, AXA, CreditAgricole, UBS
Q4.2008
BNPParibas, DeutscheBank, HSBC, RoyalBankScotland, Santander
Allianz, AXA, Commerzbank, Generali, ING, Lloyds, Munich Re, UBS
Aegon, Aviva, Barclays, CreditAgricole, CreditSuisse
Q1.2009
Aegon, Aviva, AXA, Barclays, BNPParibas
Allianz, Commerzbank, CreditAgricole, Generali, HSBC, RoyalBankScotland, Santander, UBS
CreditSuisse, DeutscheBank, ING, Lloyds, Munich Re
Q2.2009
Aegon, Barclays, CreditAgricole, ING, Santander
Allianz, Aviva, AXA, BNPParibas, HSBC, Lloyds, Munich Re, UBS
Commerzbank, CreditSuisse, DeutscheBank, Generali, RoyalBankScotland
Q3.2009
Aviva, Commerzbank, ING, Lloyds, Santander
Aegon, AXA, BNPParibas, CreditAgricole, CreditSuisse, HSBC, RoyalBankScotland, UBS
Allianz, Barclays, DeutscheBank, Generali, Munich Re
Q4.2009
Barclays, BNPParibas, CreditAgricole, HSBC, Santander
Allianz, Aviva, AXA, DeutscheBank, ING, Lloyds, Munich Re, RoyalBankScotland
Aegon, Commerzbank, CreditSuisse, Generali, UBS
Q1.2010
Allianz, AXA, Generali, Lloyds, UBS
BNPParibas, Commerzbank, CreditAgricole, CreditSuisse, DeutscheBank, HSBC, ING, Santander
Aegon, Aviva, Barclays, Munich Re, RoyalBankScotland
Q2.2010Aviva, CreditSuisse, DeutscheBank, ING, UBS
Aegon, Allianz, AXA, BNPParibas, Generali, HSBC, RoyalBankScotland, Santander
Barclays, Commerzbank, CreditAgricole, Lloyds, Munich Re
Q3.2010Aviva, AXA, Generali, HSBC, Santander
Aegon, Allianz, Commerzbank, CreditAgricole, CreditSuisse, ING, Munich Re, UBS
Barclays, BNPParibas, DeutscheBank, Lloyds, RoyalBankScotland
Q4.2010Aviva, BNPParibas, Generali, Munich Re, RoyalBankScotland
Aegon, Allianz, AXA, Commerzbank, CreditSuisse, ING, Santander, UBS
Barclays, CreditAgricole, DeutscheBank, HSBC, Lloyds

27Table 4: Systemic risk rankings for 2007 - 2010, based on quarterly realized beta forecasts βes|i · 100, see equation 4.6
rank

name

forecast

rank

name

forecast

0.76670.68190.56710.50770.47040.38580.36110.34720.28870.26150.25840.17940.16510.16450.0358

1
2
3
4
5
6
7
8
9
10
11
12
13

Q2.2007
BNPParibas
UBS
Aviva
Commerzbank
DeutscheBank
AXA
Munich Re
Aegon
Allianz
CreditSuisse
ING
Santander
Barclays

0.85510.42620.28440.27320.23810.17340.16250.13320.12240.09520.05130.03930.00670.31270.30680.28490.26870.21250.20870.2016
0.19470.17260.10070.09060.087
0.06720.0143

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Q1.2007
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1
2
3
4
5
6
7
8
9
10
11
12
13
14

Aegon
Commerzbank
Generali
CreditAgricole
Allianz
BNPParibas
HSBC
Royal Bank of Scotland
LloydsAviva
AXA
Barclays
ING
DeutscheBank
CreditSuisse
Q3.2007
HSBC
DeutscheBank
ING
Barclays
AXA
Generali
CreditSuisse
Santander
Aviva
CreditAgricole
Royal Bank of Scotland
UBS
LloydsCommerzbank

1
2
3
4
5
6
7
8
9
10
11
12
13
14

Q1.2008
CreditAgricole
CreditSuisse
Barclays
Commerzbank
Santander
BNPParibas
Royal Bank of Scotland
DeutscheBank
UBS
Aviva
Munich Re
LloydsAegon
HSBC

0.63050.61010.59230.51670.45070.38750.37540.34820.27940.26060.13510.11480.02550.0166

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

1
2
3
4
5
6
7
8
9
10
11
12

Q3.2008
Barclays
Santander
Aviva
CreditSuisse
DeutscheBank
ING
LloydsGenerali
HSBCRoyal Bank of Scotland
Munich Re
BNPParibas

1.10021.07
0.695
0.6931
0.611
0.5266
0.3671
0.349
0.3427
0.29640.23840.1691

1
2
3
4
5
6
7
8
9
10
11

Q4.2007
DeutscheBank
Aviva
Royal Bank of Scotland
AXA
BNPParibas
CreditAgricole
CreditSuisse
HSBC
Aegon
ING
Allianz
Munich Re
Commerzbank
LloydsSantander
Barclays
Q2.2008
AXA
Royal Bank of Scotland
Munich Re
Generali
CreditAgricole
LloydsBNPParibas
DeutscheBank
Aviva
Commerzbank
HSBC
Aegon
Santander
CreditSuisse
Barclays
UBS
Q4.2008
HSBC
DeutscheBank
Santander
Royal Bank of Scotland
BNPParibas
UBS
Generali
Munich Re
AXA
ING
Lloyds0.72960.57050.57010.55560.50560.42050.35860.32030.31230.27910.25840.19540.14
0.09570.07790.021
0.92330.8246
0.76610.543
0.5402
0.52720.412
0.38840.23780.23460.22610.16830.15820.15270.028
1e-04
1.4576
1.33930.51480.49980.38730.346
0.31180.29260.28770.07970.0626

Continued on next page

28

rank

name

Table 4 – Continued from previous page
forecast rank name
2.15
1.7684
1.5562
1.4611
0.92370.91
0.81890.66970.55140.34260.30330.26530.2347

1
2
3
4
5
6
7
8
9
10
11
12
13

1
2
3
4
5
6
7
8
9
10
11
12
13

Q1.2009
Aegon
Barclays
Aviva
AXA
BNPParibas
Allianz
CreditAgricole
HSBC
UBS
Commerzbank
Santander
Royal Bank of Scotland
GeneraliQ3.2009
Commerzbank
ING
Aviva
Santander
LloydsCreditSuisse
BNPParibas
AXA
Aegon
HSBC
CreditAgricole
UBS
Royal Bank of Scotland

1.10650.764
0.76150.66390.58240.50090.46880.38780.33930.31030.28860.0276
0.02

1
2
3
4
5
6
7
8
9
10
11
12
13
14

1
2
3
4
5
6
7
8
9
10
11
12
13

Q1.2010
Commerzbank
ING
Aviva
Santander
LloydsCreditSuisse
BNPParibas
AXA
Aegon
HSBC
CreditAgricole
UBS
Royal Bank of Scotland

1.10650.764
0.76150.66390.58240.50090.46880.38780.33930.31030.28860.0276
0.02

1
2
3
4
5
6
7
8
9
10
11
12
13
14

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Q3.2010
Aviva
Generali
AXA
HSBCSantander
CreditSuisse
Munich Re
Aegon
ING
UBS
CreditAgricole
Allianz
Commerzbank
LloydsBarclays
RoyalBankScotland

0.67420.60080.50160.49510.45880.44930.261
0.22260.21
0.151
0.14750.11480.07490.0426
0.03450.0222

1
2
3
4
5
6
7
8
9
10
11
12
13
14

1
2
3
4
5
6
7
8
9
10
11
12
13

Q2.2009
Aegon
CreditAgricole
ING
Barclays
Santander
Lloyds
AXA
HSBC
Munich Re
Allianz
BNPParibas
UBS
Aviva
Q4.2009
Santander
HSBC
CreditAgricole
BNPParibas
Barclays
Allianz
Royal Bank of Scotland
LloydsMunich Re
ING
DeutscheBank
Aviva
AXA
Generali
Q2.2010
Santander
HSBC
CreditAgricole
BNPParibas
Barclays
Allianz
Royal Bank of Scotland
LloydsMunich Re
ING
DeutscheBank
Aviva
AXA
GeneraliQ4.2010
BNPParibas
Generali
Munich Re
Aviva
RoyalBankScotland
Santander
Allianz
AXA
ING
UBS
Aegon
CreditSuisse
Commerzbank
CreditAgricole

forecast
2.3266
1.6192
1.49761.45671.32591.01070.47530.46070.44170.41110.31340.10280.08691.00970.84520.83850.701
0.62650.62250.62230.47730.47170.4241
0.33270.20570.16750.07971.00970.84520.83850.701
0.62650.62250.62230.47730.47170.4241
0.33270.20570.16750.07971.41040.503
0.49140.48620.43710.37840.35890.25530.20520.18860.13670.13550.09790.0334

Avoiding multicollinearity, we include in Z i∗ only the one component of Z i which exhibits the lowest
correlation with V aRi in the respective interaction term in (3).
629

Figure 6: Boxplots of R2 from forecast regressions according to equation 6.
0.5

Realized systemic risk betas

0.5

CAPM−type betas

0.4
R−squared

0.3

●

0.2

0.2

R−squared

0.3

0.4

●

0.1
0.0

0.0

0.1

●

Figure 7: Boxplots of R2 from forecast regressions according to equation 7.
CAPM−type betas

Realized systemic risk betas
0.5

0.5

●

0.4
R−squared
0.2
0.1
0.0

0.0

0.1

0.2

R−squared

0.3

●

0.3

0.4

●

30

Table 5: Estimated rank correlation (Kendall’s τ ) between three quarterly balance sheet
characteristics and average realized systemic risk betas.
firm characteristic
size
leveragematurity mismatchτb-rank correlation with βes|i for pooled data
Q1/2006-Q4/2007
Q1/2008-Q4/2010
0.07**
0.11***
0.11***-** /***: p-val. (H0 : τ ≤ 0) significant at 10% / 5%. - : p-val. (H0 : τ ≤ 0) not rejected at 30% .

31References
Acharya, Viral V., Lasse H. Pedersen, Thomas Philippon, and Matthew Richardson, 2010,
Measuring Systemic Risk, Working Paper.
Adrian, Tobias, and Markus K. Brunnermeier, 2011, CoVaR, Working Paper, Princeton
University.
Ang, Andrew, and Joseph Chen, 2002, Asymmetric correlations of equity portfolios, Journal of Financial Economics 63, 443–494.
Belloni, A., and V. Chernozhukov, 2011, l1-penalized quantile regression in highdimensional sparse models, The Annals of Statistics 39, 82–130.
Billio, Monica, Mila Getmansky, Andrew W. Lo, and Loriana Pelizzon, 2012, Econometric measures of connectedness and systemic risk in the finance and insurance sectors,
Journal of Financial Economics 104, 535Ű559.
Brownlees, C. T., and R. F. Engle, 2011, Volatility, Correlation and Tails for Systemic
Risk Measurement, Working Paper, Stern School Business, New York University.
Dehling, Herold, Daniel Vogel, Martin Wendler, and Dominik Wied, 2012, An efficient and robust test for change-points in correlation, Working Paper, available via
arXiv:1203.4871v2.
Engle, Robert, Eric Jondeau, and Michael Rockinger, 2012, Systemic Risk in Europe,
Working Paper.
Financial Stability Board, 2011, Policy Measures to Address Systemically Important Financial Institutions, http://www.financialstabilityboard.org/publications.
Giesecke, K., and B. Kim, 2011, Systemic risk: What defaults are telling us, Management
Science, forthcoming.
Hautsch, Nikolaus, Julia Schaumburg, and Melanie Schienle, 2012, Financial Network
Systemic Risk Contributions, CRC 649 working paper 2012-053.
Huang, Xin, Hao Zhou, and Haibin Zhu, 2009, A framework for assessing the systemicrisk of major financial institutions, Journal of Banking & Finance 33, 2036Ű2049.
32

Koopman, S. J., A. Lucas, and B. Schwaab, 2011, Modeling frailty correlated defaults
using many macroeconomic covariates, Journal of Econometrics 162, 312–325.
Schwaab, B., S. J. Koopman, and Andre Lucas, 2011, Systemic risk diagnostics: coincident indicators and early warning signals, Working Paper.
White, Halbert, Tae-Hwan Kim, and Simone Manganelli, 2010, VAR for VaR: Measuringsystemic risk by multivariate regression quantiles, mimeo, ECB.

33