*4.1.1. Class I and II banks*

We investigate liquidity for Class I banks that hold more than US \$ 4 billion in Tier 1 capital (T1K) and are internationally active. Moreover, we consider Class II banks that violate one or both of these conditions (see, for instance, [9] and [17]). In reality, some Class II banks considered could have been classified as Class I if they were internationally active. Nevertheless, these banks make a large contribution to the total assets of Class II banks. Invariably, all Class I banks can also be classified as large in that their gross total assets (GTA) exceed US \$ 3 billion. Many of the banks in our study come from jurisdictions affiliated to the BCBS and Macro-Economic Assessment Group (MAG).

Secondly, the EMERG global banking data has several limitations in terms of granularity and format when compared with the information required to determine the Basel III liquidity standards (see, for instance, [9] and [17]). In all instances, we had to make difficult choices

Optimizing Basel III Liquidity Coverage Ratios

http://dx.doi.org/10.5772/58395

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In the absence of suitable data, we were heavily dependent on the interpolation and extrapolation techniques discussed below. Firstly, it is clear that the LCR calculation requires information about liabilities with a remaining maturity of less than 1 month. However, quarterly EMERG data provides information about liabilities with a remaining maturity of less than 3 months. So we had to extrapolate the liabilities with a remaining maturity of 1 month. There are two approaches to doing this. In the first instance, we can assume the maturity schedule is evenly distributed, such that the amount of liabilities with a remaining maturity of less than 1 month equals 1/3 of the amount of liabilities with a remaining maturity of less than 3 months. This is the approach adopted in this chapter. Secondly, as a robustness check, we can assume an extreme case, such that all liabilities with a remaining maturity within 3 months mature within the first month. In this instance, the guidelines require dividing liabilities into subcategories of retail deposits, unsecured wholesale funding and secured funding with different run-off rates (see, for instance, [9] and [17]). However, the information available from the EMERG global data lacks such granularity. Out of necessity, we have to make assumptions on the distribution of subcategories within their primary category. Without additional information, we generally assume equal distribution of subcategories within the primary category , . Finally, except for unused commitments, letters of credit and the net fair value of derivatives, we do not have the information required for calculating the liquidity needs of all other OBS items, such as increased liquidity needs related to downgrade triggers embedded in financing transactions, derivatives and other contracts. Therefore, our calculations of the LCR and NSFR are partial measures that capture a bank's liquidity risk as mainly reflected by its BS and to a lesser extent its OBS items (see

when applying Basel III guidelines to such a large diversity of banks.

[9] and [17] for more information).

**4.2. 2002 to 2012 LCRs for class I and II banks**

from Figure 1 is that the LCR time series is non-stationary.

**4.3. Descriptive statistics for LCRs of class I and II banks**

the Basel III LCR standard had complied with these standards.

In this subsection, we provide 2002 to 2012 LCRs for Class I and II banks.

Table 1 shows that the LCR has been in a downward trend from 2002 through 2007. The average LCR had risen sharply from 2007 to 2009 and peaked in 2009. The general impression

In this subsection, we provide 2002 to 2012 LCR descriptive statistics for Class I and II banks. Table 2 reports the summary statistics of the approximate measures of the LCR for Class I banks, where the mean for the LCR is 74.96 %. In this table, the LCR displays positive skewness. The value of the kurtosis for the LCR in Table 2 is equal to or less than 3, that means that the distribution is flat. The LCR risk measure exhibits normality because the *p*-values are greater than 5 %. Nevertheless, the normality test is very sensitive to the number of observations and may only produce desirable and efficient results if observations are large. From Table 2, it is clear that, in the absence of empirical evidence, it is hard to conclude that

Our investigation includes 157 Class I and 234 Class II LIBOR-based banks from 38 countries. These banks (with the number of Class I and Class II banks in parenthesis for each jurisdiction, as well as \* and ' denoting BCBS and MAG members, respectively) are located in Argentina\* (1,3), Australia\*' (5,2), Austria (2,5), Belgium\* (1,2), Botswana (1,1), Brazil\*' (3,1), Canada\*' (7,3), China\*' (7,1), Czech Republic (4,3), Finland (0,14), France\*' (5,5), Germany\*' (7,24), Hong Kong SAR\* (1,8), Hungary (1,2), India\* (6,6), Indonesia\* (1,3), Ireland (3,1), Italy\*' (2,11), Japan\*' (14,5), Korea\*' (6,4), Luxembourg\* (0,1), Malta (0,3), Mexico\*' (1,8), Namibia (0,1), the Netherlands\*' (3,13), Norway (1,6), Poland (0,5), Portugal (3,3), Russia\* (0,3), Saudi Arabia\* (4,1), Singapore\* (5,0), South Africa\* (4,5), Spain\*' (2,4), Sweden\* (4,0), Switzerland\*' (3,5), Turkey\* (7,1), United Kingdom\*' (8,5) and United States\*' (35,66). In order to limit depositor losses, all 38 jurisdictions have explicit deposit insurance schemes or implicit government protection schemes for banks.
