workshop “banking and insurance – …frd f announcement of a $7.2 billion loss due to fraudulent...

Workshop “Banking and Insurance – Interconnectedness,

Systemic Risk and Regulation”

Frankfurt, May 8-9, 2014

Systemic Risk and Interconnectedness in the US Fina ncial Industry: Implications on Regulation of Financial Conglomerat es

(with Martin F. Grace and Sabine Wende)

University of Cologne

Jannes Rauch M. Sc.Department of Risk Management and Insurance


Agenda

1. Introduction

2. Research Focus

3. Data and Methodology

4. Results

5. Summary

2

5. Summary


1. Introduction


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Introduction

• The financial crisis of 2008-2009 exposed systemic linkages through the financial sectors of the economy.

• Large bank failures and insurer distress created questions about the linkages between the banking and insurance sectors as well as the direction and the size of the impacts.

• Given the effects of the Lehmann failure on the overall financial sector and that the bailout of AIG directly affected it counter party banks and that the bailout of AIG directly affected it counter party banks (Grace, 2011), we know that major events from one sector can affect companies from the other sector.

� How does a sector specific event from the insurance (banking) sector affect the stock price of a bank (insurance company)?

� Consequences for the regulation of the interconnected financial system?


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2. Research Focus


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Research Focus – Previous Literature

• Interconnectedness as a subcategory of the systemic risk literature is vast and is growing.

• Billio et al. (2012) and Chen et al. (2013) develop measures of systemic risk and interconnectedness in the overall financial sector based on financial market data.

• They find a high degree of interconnectedness and that the impact of banks on insurers is much stronger than vice versa.banks on insurers is much stronger than vice versa.

• However, these measures do not evaluate interconnectedness with respect to single, sector-specific events; they rather focus on measuring systemic risk in a given time period but do not examine the effect of severe and single events like catastrophes.


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• Several studies examine the stock market impact of sector specific events on companies from the same sector.

• For example, Angbazo and Narayanan (1996) examine the impact of hurricane Andrew on the US insurance industry while King (2012) assesses the effect of bank bailouts in several countries during October 2008 on the stock prices of a sample of banks.

• However, these studies do not examine spillover effects of these sector • However, these studies do not examine spillover effects of these sector specific events on companies from other sectors.


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• Another source of interconnectedness is described e.g. in Kaufmann (1999, 2000) or Halstead, Hedge and Klein (2005), stating that an externality or a shock like a severe sector specific event might create uncertainty about other firms and thus indirectly cause institutional instabilities, even though they have no direct or indirect exposure towards the event.

• Hence, severe events can cause a market response and affect other firms despite only a little real connection or interdependence between firms despite only a little real connection or interdependence between the firms (Bankruptcy of LTCM in 1998).

• However, to our best knowledge there is no research that examines if sector specific events can affect other sectors whose companies have no direct exposure towards these events within the overall financial sector.


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Research Focus – Interconnectedness

• Competition hypothesis (Lang and Stulz, 1992): Banks and insurers compete in certain services offered. Consumers might shift business from firms that were affected by those events to unaffected firms.

• Bank asset risks (Lambert, Noth and Schüwer, 2012): Hurricane Katrina caused uncertainty for banks. Banks in the region might be burdened with significant loan quality issues due to business bankruptcies and interruptions, increases in borrowers' operating costs, higher unemployment in the region and severe collateral damages. higher unemployment in the region and severe collateral damages.

• Previous literature: Given that relatively small operational losses have inter sector spillover effects between US banks and insurers (Cummins, Wei and Xie, 2011), severe sector specific events like hurricanes or trading frauds should affect companies from the other sector as well.


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Research Focus – Hypotheses

• Inter sector interconnectedness as a potential source of systemic risk (Cummins und Weiss, 2012).

• Research question:

– How do sector specific events affect firms from the other sector?

– Do banks and insurers stock prices react similarly?– Do banks and insurers stock prices react similarly?

– Stronger effect of banks on insurers (Billio et al., 2012; Chen et al., 2013)?

• Hypothesis 1: Sector specific events affect compani es from other sectors. Furthermore finance and banking related ev ents affect the insurance industry stronger than vice versa.


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Research Focus – Hypotheses

• Additionally: Interconnected banks (banks that show insurance revenue in their financial statements).

• In November 1999 the Gramm-Leach-Bliley Act removed any barriers between banks and insurance companies.

• With respect to Kaufmann’s (1999, 2000) and Halstead, Hedge and • With respect to Kaufmann’s (1999, 2000) and Halstead, Hedge and Klein (2005), these banks have a direct exposure to insurance-specific events due to their business in this sector, while non-interconnectedbanks are unexposed to these events.

• Hypothesis 2: Interconnected banks react differentl y to sector specific events than non- interconnected banks.


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3. Data and Methodology


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Data and Methodology• Our observation period covers the years from 2000 to 2012, focusing on

the US financial industry.

• We drop firms if the stock prices are not available for the observed events and their estimation period.

• For the regression analysis we drop firms with missing, negative or zero surplus, total assets, and net premiums written.

• Our analysis includes 11,981 overall firm observations covering 12


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• Our analysis includes 11,981 overall firm observations covering 12 events.

• Source: SNL Financial.

Data and Methodology

Variable Observations Percentage

Total Companies 11,981 100.00%

Banking Industry 10,704 89.34%

Interconnected Bank 3,745 34.99%

Non Interconnected Bank 6,959 65.01%

Insurance Underwriter 1,277 10.66%

P/C Insurers 836 65.47%


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P/C Insurers 836 65.47%

L/H Insurers 301 23.57%

Others 140 10.96%

Data and Methodology• Events from 4 categories:

– Catastrophes (e.g. hurricanes).

– Terror (e.g. 9/11 terror attacks).

– Fraud (e.g. trading scandals).

– Finance (e.g. bailouts).

• Choice of events based on previous literature


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– Catastrophes: Hurricane Andrew (Lamb, 1995).

– Terror: 9/11 attacks (Cummins and Lewis, 2003).

– Fraud: Operational losses (Cummins, Lewis and Wei, 2006).

– Finance: Greek debt crisis (Bhanot et al., 2014).

Data and MethodologyEvent Date Category Country Description

September 11, 2001 September 17, 2001

TER USATerrorist attack on the World trade Center in New York by hijackedairplanes.

Madrid Train Bombing March 11, 2004

TER ESP Terrorist attack on the public transportation system in Madrid.

London Bombings July 7, 2005 TER UK Terrorist attack on the public transportation system in London.

Hurricane Katrina August 23, 2005

CAT USAHurricane leading to massive damage in the southern part of theUS. Estimated property damage of $108 billion.

Northern Rock Bankrun September 17, 2007

FIN UKBank run caused by the financial crisis by customers of NorthernRock.

2008 Société Générale Trading


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2008 Société Générale Trading Loss Incident

January 24, 2008

FRD FAnnouncement of a $7.2 billion loss due to fraudulent trades by asingle trader by Société Générale.

Hurricane Ike September 13, 2008

CAT USA Landfall of Hurricane Ike, causing damages of appr. $29.5 billion.

Greece BailoutMay 2, 2010 FIN GRE

Eurozone countries and the IMF provide a €110 billion bailoutloan for Greece.

Tōhoku earthquake and tsunami

March 11, 2011

CAT JAPFukushima Daiichi nuclear disaster following the Tōhokuearthquake and tsunami.

Downgrade of US debt August 5, 2011

FIN USACredit-rating downgrade of US debt from AAA to AA+ byStandard & Poors.

UBS Rogue Trader Scandal September 15, 2011

FRD UKAnnouncement of a $2 billion loss due to fraudulent trades by asingle trader by UBS.

Hurricane Sandy October 29, 2012

CAT USAHurricane leading to massive damage in the eastern part of the US.Estimated property damage of $50 billion.

Two Step Approach: 1. Event Study

• Test, if an event leads to abnormal returns (AR) in a given event window:

r i = α + βi rs&p + ei

− ri = daily stock return of stock I

− rs&p = daily return of S&P 500 (Reference index)


s&p

− βi = Beta of stock i (based on historic returns, 250 days)

• Abnormal Return: AR i = ( r i - (α + βi rs&p ))

• Event Window: Period including several days before and after the event (different event windows, including different periods from -16 till +16 days around the event in order to identify short- and long term effects of the event).


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2. Regression analyses (Cummins, Wei and Xie, 2011)

• Test, if the event categories influence the reaction towards events:*

CAR i,t = θ’X i,k,t + δ‘X i,l,t + εi

− CARi = cumulated abnormal return for stock i (during the event window**)


window**)

− θ = A vector of dummies dummy equal to one if an event from the respective event category (Catastrophe, Terror, Fraud or Finance) occurs

− δ = Vector with control variables (Growth opportunities, firm size, profitability, capitalization, reinsurance)


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*The data from all events is pooled, using a fixed effects regression analysis.**We use the CAR of our standard event window [τi = -4; τj = +4] as dependent variable for banking events and a CAR using an event window [τi = -8; τj = +8] for insurance-specific events, given that previous literature indicated a longer response period for insurance events (Cummins, Lewis, and Wei, 2006).


Type Variable Description

Eve

nt D

umm

ies

CAT_event Dummy equal to one if an event from the “Catastrophe” category occurs.

Terror_event Dummy equal to one if an event from the “Terror” category occurs.

Finance_event Dummy equal to one if an event from the “Finance” category occurs.

Fraud_event Dummy equal to one if an event from the “Fraud” category occurs.

Ln Assets The natural logarithm of the firm’s total assets.


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Con

trol

Var

iabl

es

Ln Assets The natural logarithm of the firm’s total assets.

Equity Ratio Book equity divided by its total assets.

ROA The ratio of net income to total assets.

Price to Book The firm’s market value of assets divided by its book value.

Reinsurance Exposure* The ratio of assumed written premiums by gross written premiums

* Only P/L insurers

4. Results


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Results (selected events)N

on-I

nter

conn

ecte

d B

anks Event

WindowKatrina Ike UBS Trader Scandal US Debt DowngradeObs. CAR Obs. CAR Obs. CAR Obs. CAR

(-4; +4) 547 -0.08% 689 1.40% 570 -1.78% 548 -1.96%(-8; +8) 547 -0.03%* 689 2.63% 570 -3.62% 548 -2.45%(-12; +12) 547 -0.87% 689 5.00% 570 -2.51% 548 -3.95%(-16; +16) 547 -1.73% 689 3.80% 570 -2.44% 548 -4.27%(0; +4) 547 0.07% 689 1.82% 570 -1.55%** 548 -2.35%***(0; +8) 547 0.10%* 689 1.56% 570 -2.71%* 548 -2.74%***(0; +12) 547 -0.33% 689 2.84% 570 -1.94%* 548 -3.72%***(0; +16) 547 -0.84% 689 1.26% 570 -1.80%** 548 -3.43%***(-4; 0) 547 -0.03% 689 -0.17% 570 -0.50% 548 -0.38%(-8; 0) 547 0.00% 689 1.32% 570 -1.19% 548 -0.48%(-12; 0) 547 -0.42% 689 2.41% 570 -0.85% 548 -1.00%(-16; 0) 547 -0.77% 689 2.78% 570 -0.92% 548 -1.61%

Inte

rcon

nect

ed B

anks

Event Window Obs. CAR Obs. CAR Obs. CAR Obs. CAR(-4; +4) 217 -0.23% 251 2.71% 464 -0.97% 453 -0.35%(-8; +8) 217 -0.06% 251 4.18% 464 -0.41% 453 -0.44%(-12; +12) 217 -0.97% 251 7.66%*** 464 -0.36% 453 -0.35%(-16; +16) 217 -1.82% 251 8.49%** 464 -0.50% 453 -0.99%*


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Inte

rcon

nect

ed B

anks

(-16; +16) 217 -1.82% 251 8.49%** 464 -0.50% 453 -0.99%*(0; +4) 217 -0.28% 251 2.66% 464 -1.61% 453 -2.31%***(0; +8) 217 -0.61% 251 2.33% 464 -0.99% 453 -3.20%***(0; +12) 217 -0.98% 251 4.35% 464 -0.35% 453 -2.91%***(0; +16) 217 -1.44% 251 4.93% 464 -0.68% 453 -2.85%***(-4; 0) 217 0.04% 251 0.93% 464 0.40% 453 1.52%***(-8; 0) 217 0.53%* 251 2.74%* 464 0.33% 453 2.32%***(-12; 0) 217 -0.01% 251 4.19%*** 464 -0.25% 453 2.12%***(-16; 0) 217 -0.40% 251 4.45%*** 464 -0.07% 453 1.42%***

Insu

rers

Event Window Obs. CAR Obs. CAR Obs. CAR Obs. CAR(-4; +4) 101 -0.21% 111 2.28%*** 118 -2.64%*** 111 2.42%***(-8; +8) 101 -1.02% 111 2.95%*** 118 -1.21% 111 2.44%***(-12; +12) 101 -1.82%*** 111 8.33%*** 118 -0.31%* 111 0.84%***(-16; +16) 101 -2.11%** 111 3.99% 118 -1.36%* 111 -0.63%(0; +4) 101 -0.31% 111 4.22%*** 118 -2.55%*** 111 0.37%**(0; +8) 101 -1.26%*** 111 2.64%*** 118 -0.93%** 111 -0.70%(0; +12) 101 -1.87%*** 111 3.97% 118 -0.36% 111 -0.98%(0; +16) 101 -1.48%** 111 -1.47%** 118 -0.26% 111 0.26%(-4; 0) 101 0.15%* 111 -1.58% 118 -0.35% 111 1.18%**(-8; 0) 101 0.29%*** 111 0.68%** 118 -0.54% 111 2.27%***(-12; 0) 101 0.10% 111 4.72%*** 118 -0.20% 111 0.96%**(-16; 0) 101 -0.58% 111 5.82%*** 118 -1.35%*** 111 -1.76%

Results – Event Study

• Banks (both interconnected and non-interconnected) show only weak reactions towards insurance related events like catastrophes.

• However, interconnected banks react differently during most events than non-interconnected banks, indicating that a direct exposure towards an event can affect its stock market response.

• Consistent with previous literature, insurance companies show strong reactions to most banking/finance-related events.reactions to most banking/finance-related events.


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Results – Regression Analyses(1) (2) (3) (4) (5)

(P/C Insurers)

(L/H Insurers)

(Banking Industry)

(Interconnected Banks)

(Non-Interconnected

Banks)CAT_event 0.020 0.022 0.005 0.007 0.005

(0.013) (0.012) (0.003) (0.006) (0.004)Terror_event 0.014 0.011 -0.009** -0.001 -0.012**

(0.014) (0.016) (0.004) (0.006) (0.005)Finance_event 0.025** 0.037* -0.009* 0.001 -0.014*

(0.007) (0.016) (0.004) (0.007) (0.005)Price to Book -0.005 0.023 0.005 0.010* 0.001

(0.026) (0.014) (0.003) (0.005) (0.004)


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(0.026) (0.014) (0.003) (0.005) (0.004)Ln Assets 0.036 -0.004 0.020** 0.026** 0.022*

(0.029) (0.018) (0.007) (0.009) (0.010)Equity Ratio 0.044 0.142 0.010 0.108 0.009

(0.086) (0.163) (0.105) (0.129) (0.142)ROA 0.276 -0.927 -0.061 -0.652 0.041

(0.226) (0.457) (0.236) (0.409) (0.300)Reinsurance Exposure 0.102

(0.070)Constant -0.598 0.009 -0.268** -0.377** -0.286*

(0.456) (0.307) (0.096) (0.136) (0.133)AIC -1,204.695 -806.928 -14,989.489 -6,833.891 -8,809.701BIC -1,169.016 -780.978 -14,938.829 -6,790.485 -8,762.108R2 0.047 0.062 0.005 0.005 0.007Adj. R2 0.035 0.039 0.005 0.003 0.006Observations 639 301 10,272 3,644 6,628

Results – Regression Analyses

• Banks are negatively affected by Terror events and all firms are affected by Finance events, indicating a certain degree of interconnectedness in the US financial industry.

• Interconnected Banks are not affected.

• This indicates that the insurance- and banking related effects might cancel out for these companies.

• Hence, additional regulatory charges seem to be not necessary, based • Hence, additional regulatory charges seem to be not necessary, based on the results of our analysis.


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• Our results indicate a certain degree of interconnectedness in the financial sector, given that most events have impacts on companies from the other sector.

• In addition, our findings are consistent with the results of prior studies (e.g. Billio et al., 2012 and Chen et al., 2013), as we find that banking related events affect the insurance sector stronger than vice versa.

Results – Summary


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• Moreover, we show that interconnected banks react differently than non-interconnected banks to most events in our sample, indicating that insurance business affects the exposure of banks towards sector-specific events.

5. Summary


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Summary• We measure the effect of sector-specific events on the overall stock

market performance of financial companies using event study methodology.

• Our results indicate a certain degree of interconnectedness in the financial sector, given that most events have impacts on companies from the other sector. In addition, our findings are consistent with the results of prior studies (e.g. Billio et al., 2012 and Chen et al., 2013), as we find that banking related events affect the insurance sector stronger


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we find that banking related events affect the insurance sector stronger than vice versa.

• We find a different reaction of interconnected banks in our analysis, indicating that insurance revenue affects the banks’ exposure.

• Our findings are highly relevant for regulators when determining capital requirements for financial institutions and interconnected groups, and whether additional measures or capital surcharges for inter-sector events have to be implemented.

Thank you for you attention


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Questions?

Events• McWilliams and Siegel (1997)

– Under the assumption that markets are efficient , information should be quickly incorporated into stock prices. Hence, the use of long event windows can be interpreted as a violation of this assumption. However, as insurance events are in the center of our analysis and previous research recommends the use of longer event windows for insurance events given a longer post-event response period for these events (Cummins, Wei, Xie, 2011; Cummins, Lewis,


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period for these events (Cummins, Wei, Xie, 2011; Cummins, Lewis, and Wei, 2006), the use of long event windows should be appropriate. For insurance events, it takes time until final amount of insured damages and other claims that can affect the stock market response are certain


– Events have to be unanticipated . Stock markets have to react to new information, and prior information leakages can violate this assumption. For Catastrophes, Terror and Fraud events, this assumption should hold, as these types of events and their final size of impact are hardly possible to be anticipated. However, several Finance events in our sample might have been anticipated by the stock markets, for example the Greece Bailout in 2010. Fraud


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stock markets, for example the Greece Bailout in 2010. Fraud events in our sample might have been known by the respective banks and potentially by the regulators in advance, but not to the wide public and most investors why impact the stock market reaction.


– No confounding effects should occur, i.e. the event should be isolated from the effects of other events. Again, for large Catastrophes and Terror events, one can assume that these events are important enough to trigger industry-wide reactions, even though other, but less important events might occur at the same time. We carefully analyze if major events occurred during our event windows for all events included in our analysis and comment on this issue


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for all events included in our analysis and comment on this issue while discussing the results.

Type Variable Description

Con

trol

V

aria

bles Ln Assets The natural logarithm of the firm’s total assets.

Equity Ratio Book equity divided by its total assets.ROA The ratio of net income to total assets.Price to Book The firm’s market value of assets divided by its book value.

Ban

ks

International ExposureForegin Deposits by Total Deposits The proportion of foreign deposits by total deposits as indicated in the financial statement.Foreign Loans by Total Loans The proportion of foreign loans by total loans as indicated in the financial statement.

Catastrophes / Insurance ExposureInsurance Revenue by Total Revenue The proportion of the banks' insurance income by total revenue.

Dummy Bank's Regional Exposure (Event)

A dummy variable that equals one if the bank is located in the affected region of the Event. In addition, only firms are used that are not part of a holding structure to eliminate the effects of internal capital markets and guarantees.

Investment ExposureDummy Investment Bank Dummy variable that equals one if the company is an investment bank.Trading and Investment Banking Revenue by Total Revenue The share of Trading Revenue and Investment Banking & Brokerage income by total revenue.


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Revenue by Total Revenue The share of Trading Revenue and Investment Banking & Brokerage income by total revenue.

Insu

rers

International exposure

Dummy International WriterDummy variable equal to one if the firm shows direct premiums written or earned outside the US and Canada ("Other Alien").

Catastrophes / Insurance ExposureDummy Insurer's Regional Exposure (Event)

Dummy variable equal to one if the firm shows direct premiums written or earned in at least one of the affected regions of the Event.

Reinsurance Ceded By Gross Premiums Earned The amount reinsurance ceded by gross premiums earned.

Investment ExposureTrading Activities by Total Revenue The proportion of Trading Activities by Total Revenue.Investment Banking by Total Revenue The proportion of Investment Banking, Advisory, & Other by Total Revenue.Equity Instruments by Total Assets The proportion of Equity Instruments by Total Assets.Trading Account Securities by Total Assets The proportion of Trading Account Securities by Total Assets.

Type Variable Obs Mean Std. Dev. Min Max

Con

trol

V

aria

bles

Price to Book 11,532 1.15 0.67 0.01 7.47Ln Assets 11,865 13.57 1.77 9.15 21.57

ROA 11,857 0.60% 2.04% -55.24% 88.81%Equity Ratio 11,863 11.89% 7.62% 0.29% 77.67%

Ban

ks

International ExposureForegin Deposits by Total Deposits 10,704 0.36% 3.73% 0.00% 90.11%Foreign Loans by Total Loans 10,704 0.13% 1.73% 0.00% 58.32%

Catastrophes / Insurance Exposure

Insurance Revenue by Total Revenue 10,704 0.71% 2.75% -0.68% 44.29%Dummy Bank's Regional Exposure (Katrina) 10,704 0.00 0.06 0 1Dummy Bank's Regional Exposure (Ike) 10,704 0.00 0.06 0 1Dummy Bank's Regional Exposure (911) 10,704 0.01 0.10 0 1Dummy Bank's Regional Exposure (Sandy) 10,704 0.02 0.14 0 1

Investment ExposureDummy Investment Bank 10,704 0.35 0.48 0 1Trading and Investment Banking Revenue by Total Revenue 10,704 0.68% 2.48% -55.17% 97.69%Total Revenue 10,704 0.68% 2.48% -55.17% 97.69%

Insu

rers

International exposureDummy International Writer 1,277 0.24 0.43 0 1

Catastrophes / Insurance ExposureReinsurance Ceded By Gross Premiums Earned 1,277 14.30% 16.51% 0.00% 87.58%Dummy Insurer's Regional Exposure (Katrina) 1,277 0.50 0.50 0 1Dummy Insurer's Regional Exposure (Ike) 1,277 0.52 0.50 0 1Dummy Insurer's Regional Exposure (911) 1,277 0.45 0.50 0 1Dummy Insurer's Regional Exposure (Sandy) 1,277 0.45 0.50 0 1

Investment Exposure

Trading Activities by Total Revenue 1,277 0.00% 0.08% -1.63% 0.54%Investment Banking by Total Revenue 1,277 0.02% 0.73% 0.00% 26.23%Equity Instruments by Total Assets 1,277 3.29% 5.52% 0.00% 60.84%Trading Account Securities by Total Assets 1,277 3.16% 12.36% 0.00% 75.86%


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