The sample

For the purpose at hand I decided to focus on the main European banks since the BRRD is set at a European level. In this way, the most capitalized banks in the world are out

of my concern because they are headquartered mainly in the U.S or China40. In order to find

the leading EU banks, I targeted the Euro STOXX index. This index is a capitalization weighted one and includes only institution whose core activities are headquartered in Europe. STOXX Ltd. is an established global provider of innovative index concepts with a European heritage. The Euro STOXX index has a fixed number of constituents (26 for the year 2016) and is weighted according to free-float market capitalization, with base value 100€ on

December 31, 199141_{. The index includes banks from eight different countries, namely}

Austria, Belgium, France, Germany, Ireland, Italy, Netherlands and Spain42_.

3.2.1 Bloomberg balance sheets

Once I have detected the institutions to which focus on, I chose to rely on the software Bloomberg as the provider for the balance sheets of such institutions. The analysis is conducted on the balance sheets from 2012 until 2016, euro currency.

The accounting data of Bloomberg, being a U.S. based provider, are collected according to the Generally accepted accounting principles (GAAP), which are a common set of accounting principles, standards and procedures that companies must follow when they compile their financial statements. GAAP improves the clarity of the communication of financial information and facilitates the cross comparison of financial information across

different companies43.

The U.S-centered GAAP differentiate in some ways from the International Financial Reporting Standard (IFRS), which is taken worldwide as the main guideline in financial statement compiling. The key divergent features concern the treatment of intangibles, of inventory costs, of write-downs and of discontinued operations. Hence, these singularities should not cause huge bias in the final results of the research.

40 _{Data on the biggest banks by market cap, as of April 2017, are taken from Statista (web source).} 41 _{STOXX index methodology guide of Dec 2017.}

42 _{Except for Finland which has no representative bank in the index, these are also the top EU countries for GDP}

in 2016, according to the data of the World Bank.

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Table 3. List of the 26 banks composing the Euro STOXX index and their status in 2016

Bank G-SIB O-SIB

BNP Paribas Deutsche Bank Crédit Agricole Societé Générale Banco Santander UniCredit ING Group

Banco Bilbao Vyzcaya Argentaria Intesa Sanpaolo

Natixis Commerzbank ABN Amro Group Caixa Bank KBC Group Banco Sabadell Erste Group Bank Bankia

Bank of Ireland Group Unione di banche italiane Raiffeisen

Allied Irish Banks Mediobanca Bankinter BPER BPM Fineco Bank

The list is ranked by the values of balance sheet total assets as of 30 December 2016, from the highest to the lowest.

3.2.2 Building the simulation on the first scenario of bail-in

In the following, I outline the methodology employed to investigate the degree of bail- in resilience of the institutions in the sample.

Basically, the aim is to find the amount of losses that shareholders and other creditors would have borne if a bail-in had occurred.

In order to cope with this task only few items are necessary: the total amount of liabilities, the total amount of risk weighted assets (aka RWA), the value of the equity including minorities, of Tier 1 (preferably explicitly divided between Core Equity and Additional) and of Tier 2. These data are not hard to find and handle if the company is listed. For the sake of clarity, it should be stated that this is an ex-post analysis about the past trend of soundness of the banks not a stress test with a forecast purpose. It is not a precise measure

As seen in Chapter 2, the status of Globally Systemically Important Bank or Other Systemically Important Bank is important for the TLAC requirements.

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of soundness but it could give a first estimate of the degree of soundness of the institutions. The analysis may lack of accuracy, apart from the accounting principles employed, also because it comprises the term 2012-2016 where capital structures and buffers were (and still are) facing continuous changes since the requirements of Basel III are under the phase-in.

Table 4. Waterfall approach to calculate the extent of losses on 8% of TL

2016 DBK GY

Base case Deutsche bank AG

A) Total assets 1.590.546

B) Total liabilities 1.525.727

C) Risk weighted assets 356.235

D) 8% of liabilities 122.058

E) Equity + minorities 64.819

F) E-D -57.239

Equity cutting down % 100%

G) Core equity tier 1 47.782

H) Additional tier 1 7.704

I) Total Tier 1 55.486

L) F+I -1.753

Tier 1 cutting down % 100%

M) Tier 2 6.672

Tier 2 cutting down % 26,28%

What I am going to explain applies to each bank of the sample and for each year considered. The table above (Table 2) refers to a bail-in simulation computed on Deutsche Bank for the year 2016. The denomination DBK GY refers to the ticker used by the software Bloomberg to uniquely identify the German bank. For the complete excel tables with the entire set of computations, please see the Annex.

Recall that the objective here is to find out the amount of losses that the bank would have borne if a bail-in procedure had been implemented. In order to gauge this, I divided the total bank losses into three levels of loss: one for shareholders and the remaining two for the main tranches of creditors. These levels are expressed via the percentage of equity cut down, Tier 1 cut down and Tier 2 cut down. The procedure adopted starts from the total liabilities (B) and calculates the corresponding 8% (D), which is the minimum share of losses that bank must bear before any other possible aid (in the form or cash injection or guarantees) might come from the government or other funds. This amount has to be subtracted from the value of equity including minorities (E) and constitutes the record F.

The equity cut down is computed according to this formula: In other

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equity is reported, as happened for Deutsche Bank in 2016. Otherwise, the percentage of loss will be proportionate to the extent of loss over the initial value of equity.

In the case of a 100% cut down of equity, the next step is to add the total Tier 1 to F (L). If this last value is negative it means that neither the first cushion of protection is sufficient to

cover a loss. The Tier 1 cut down was calculated in the following way: .

Hence, a 100% Tier 1 cut down will be reported. When L is positive it means that Tier 1 was enough to deal with losses.

In the case of a 100% Tier 1 cut down, the last step is to calculate the extent of loss on the

Tier 2. The Tier 2 cut down comes out from a longer equation: .

As a matter of fact, if none of the cushions were sufficient, the bank will end up with 100% losses on all the three tranches. In this case Deutsche Bank would have been able to absorb the imposition of losses at 8% of the total liabilities with all its equity, its Tier 1 and a portion on Tier 2.

3.2.3 Building the simulation on the second scenario of bail-in

Table 5. Waterfall approach to calculate the extent of losses on 20% of RWA DBK GY

Deutsche bank AG

A) Total assets 1.590.546

B) Total liabilities 1.525.727

C) Risk weighted assets 356.235

D) 20% of RWA 71.247

E) Equity + minorities 64.819

F) E-D -6.428

Equity cutting down % 100%

G) Core equity tier 1 47.782

H) Additional tier 1 7.704

I) Total Tier 1 55.486

L) F+I 49.058

Tier 1 cutting down % 11,58%

M) Tier 2 6.672

Tier 2 cutting down %

2016

Exceptional case

What I am going to explain applies to each bank of the sample and for each year considered. The second bail-in simulation works similarly to the first one. I inserted this second scenario because in a provision of the BRRD it is stated that, under exceptional

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circumstances, on the discretionary will of the European Commission, the first losses may be imposed based on the 20% or risk weighted assets.

For consistency, the table above (Table 3) refers to the simulation applied to Deutsche Bank in 2016. Again, the procedure follows a waterfall approach. The starting point in this case are the risk weighted assets (C), from which it has to be calculated the relative 20% (D). Then the computations continue straightforward as in the other simulation, thus computing the three levels of losses with the same formulas, for the equity (100%), for Tier 1 creditors (11,58%) and Tier 2 creditors (null).

Notably the losses here had smaller impact in magnitude with the respect to the 8% case. The possible reasons will be analyzed in the next chapter.