Variable Definitions and Hypothesis Development

The primary dependent variable used in this study is the change in capital buffer, (ΔBUF). The capital buffer is the ratio of Tier 1 and Tier 2 capital to risk-weighted assets less the minimum 8 percent requirement specified under Pillar 1 of the Basel capital framework. An alternative dependent variable used in this study is the change in the Tier 1 capital buffer (ΔT1BUF), which is the ratio of Tier 1 capital to risk-weighted assets less the minimum 4 percent requirement.

As previously mentioned, explanatory variables used to model bank target capital ratios are those suggested by prior literature. According to the literature which was reviewed in section 4.3, a bank’s optimal capital ratio is based on a trade-off between three costs: capital adjustments costs, financial distress costs and financing costs. The following explanatory variables are selected as proxies and listed in Table 7.1 along with their expected sign49.

7.4.1. Capital Adjustment Costs

In this study, a lagged capital buffer (L.BUF) is used as a proxy for the presence of adjustment costs. Berger et al (1995) explains that capital adjustment costs exist because of a pure transaction costs component as well as informational asymmetry between the bank and

49 _{As mentioned in Chapter 4 banks also hold capital buffers because of fixed costs associated with issuing new}

the market. If the speed of adjustment coefficient (δ) is equal to zero, no adjustment is made. If the speed of adjustment coefficient (δ) is equal to one, a full adjustment is made within one time period (one quarterly period). As a result, a positive sign is anticipated50. The null and alternative hypotheses are as follows:

H1: The speed of adjustment coefficient (δ) is not significantly different from zero,

such that capital adjustment costs (L.BUF) has no relationship with the change in bank capital buffer (ΔBUF).

H1a: The speed of adjustment coefficient (δ) is positive, such that capital

adjustment costs (L.BUF) is positively related to a change in bank capital buffer (ΔBUF).

7.4.2. Financial Distress Costs

Estrella (2004) explains that a key determinant of target capital ratios is a bank’s expected cost of financial distress. Expected costs of financial distress relate to the likelihood of bank failure and three variables are chosen to best account for these costs. The first variable is RISK, calculated as the ratio of total risk-weighted assets to total assets51. The variable is chosen based on previous literature (Francis and Osborne, 2010) to control for the riskiness of the bank’s business model from the perspective of the regulator. This ratio is closely followed by APRA supervisors when making ADI risk assessments. The RISK variable will vary between 0 in the limiting case that all assets in a bank’s portfolio are risk-weighted at 0

50

Note, the expected sign of the coefficient in equation 7.4 (−δ) is negative. However the results which are

reported in this study for the speed of adjustment coefficient are in the form of δ and so are positive in all cases,

with 0 ≤δ≤1. 51

The General Method of Moments (GMM) model used in this study removes potential endogeneity (since the dependent variable’s denominator is RWA) by using an instrumental variable weighting matrix. This is discussed further in section 7.7 on econometric issues.

percent, and 1 if all assets are risk-weighted at 100%, with the unlikely potential for RISK > 1 since risk-weightings greater than 100% exist for certain exposures. The average RISK for Australian ADIs in this study’s sample is 53 per cent. The expected sign of RISK is ambiguous with respect to outcome. A positive sign would imply banks with riskier portfolios hold higher capital, above that suggested by regulators. On the other hand, a negative association is consistent with moral hazard behaviour52. The null and alternative hypotheses for RISK are as follows:

H2: The inherent risk of a bank’s portfolio (RISK) has no relationship with the

change in total capital buffer (ΔBUF).

H2a1: The inherent risk of a bank’s portfolio (RISK) is positively related to a change

in bank capital buffer (ΔBUF) indicating banks raise capital buffers to account for greater inherent portfolio risk.

H2a2: The inherent risk of a bank’s portfolio (RISK) is negatively related to a change

in bank capital buffer (ΔBUF) indicating moral hazard.

The second variable used to account for expected costs of financial distress on the bank is the variable IMP, calculated as the ratio of total impaired assets to total assets. This variable reflects the credit risk of the bank at the current point in time, accounting for potential losses in the bank’s portfolio. Impaired facilities reflect bank exposures for which there is doubt as to whether the full amount due will be repaid in a timely manner. The expected sign for IMP, like RISK, is ambiguous. The null and alternative hypotheses for IMP are as follows:

52 _{The ambiguous relationship between risk and capital is discussed in greater detail in the literature review}

H3: The level of credit risk in a bank’s portfolio (IMP) has no relationship with the

change in total capital buffer (ΔBUF).

H3a1: The level of credit risk in a bank’s portfolio (IMP) is positively related to a

change in bank capital buffer (ΔBUF) indicating banks raise capital buffers to account for greater portfolio credit risk.

H3a2: The level of credit risk in a bank’s portfolio (IMP) is negatively related to a

change in bank capital buffer (ΔBUF) indicating moral hazard.

Finally, the variable SIZE is the third and final variable used to account for expected costs of financial distress. SIZE is the natural logarithm of total assets. Previous studies have found evidence of a size effect on bank capital buffers (Stolz and Wedow, 2011; Jokipii and Milne, 2008, Francis and Osborne, 2010). The expected sign of SIZE is negative. Large firms have better access to funding, more advanced monitoring techniques to screen risky borrowers and better diversification of portfolio risk. Past literature argues that this allows larger banks to hold lower levels of target capital buffers than smaller banks. In addition, theory regarding the too-big-to-fail hypothesis argues larger banks will hold lower capital buffers given there is a higher probability that larger banks will be bailed out by the government in the case of financial distress because of the threat of systemic effects on the wider economy. The null and alternative hypotheses for SIZE are as follows:

H4: SIZE has no influence over the change in total capital buffer (ΔBUF).

7.4.3. Financing costs

Past literature has identified an important determinant of banks target capital ratios to be the cost to banks of holding capital (Berger et al, 1995; Estrella, 2004)53. Following previous empirical literature, these costs are accounted for using banks’ return on equity (ROE). ROE is the ratio of bank after-tax earnings to total equity. ROE is selected because it represents the return the bank could have expected to earn if capital that is held by the bank were invested instead in profitable bank exposures. In other words, a bank’s ROE represents the opportunity cost of holding capital. Under this interpretation, the expected sign of ROE is negative. However, as noted by Stolz and Wedow (2011) since raising capital is costly, banks rely on retained earnings to bolster capital when needed54. This would imply a positive relationship is conceivable. Thus, past literature implies the relationship is ambiguous. The null and alternative hypotheses for ROE are as follows:

H5: A bank’s return on equity (ROE) has no relationship with the change in total

capital buffer (ΔBUF).

H5a1: A bank’s return on equity (ROE) is positively related to a change in bank

capital buffer (ΔBUF) indicating an opportunity cost of holding capital is present.

H5a2: A bank’s return on equity (ROE) is negatively related to a change in bank

capital buffer (ΔBUF) indicating retained earnings are relied on directly to increase capital.

53 _{The ROE variable reflects banks ability to finance their capital base with retained earnings.}

7.4.4. Basel II

A dummy variable is included (BASEL2) to account for the regulatory capital regime change from Basel I to Basel II on the 1st of January, 2008 in the Australian banking system. As mentioned, the move to Basel II had the objective of greater risk-sensitivity. The sign is expected to be positive, given Australian ADIs are thought to benefit from the new requirements which reduce risk-weighting for residential mortgage exposures that make up a significant proportion of Australian ADI portfolios. Additionally, the move to Basel II provided supervisors with new tools for assessment of bank risk, such as the ICAAP. This might have caused banks to respond by increasing capital ratios. The null and alternative hypotheses for BASEL2 are as follows:

H6: The implementation of the Basel II framework (BASEL2) has no relationship

with the change in total capital buffer (ΔBUF).

H6a1: The implementation of the Basel II framework (BASEL2) is positively related

to a change in bank capital buffer (ΔBUF). 7.4.5. Economic Cycle

Finally a business cycle variable is included to account for economic conditions. Past literature has argued that the economic cycle impacts on a bank’s ability or willingness to raise capital or alter its balance sheet. Two variables are used; the change in gross domestic product (GDP) and a one quarter lagged rate of unemployment (L.UR). A positive sign (negative for L.UR) suggests that banks increase capital ratios during upturns, potentially due to greater earnings and lower business risk. This also suggests that banks are building up capital in upturns to help withstand economic downturns. This essentially suggests that banks are exhibiting the capital management practices of a countercyclical capital buffer, due to be

enforced by regulation under Basel III from 2013. A negative sign (positive for L.UR) would suggest that bank capital management practices are procyclical and that banks are short- sighted, reducing capital buffers during upturns by increasing exposures, such that during downturns there is less capital on hand to withstand losses. As already discussed this has the effect of amplifying the business cycle, and causing deeper and longer recessionary periods. The null and alternative hypotheses for GDP and L.UR are as follows:

H7: The economic cycle (GDP, L.UR) has no relationship with the change in total

capital buffer (ΔBUF).

H7a1: The economic cycle (GDP, L.UR) is positively related to a change in bank

capital buffer (ΔBUF) indicating foresight in capital management practice. H7a1: The economic cycle (GDP, L.UR) is negatively related to a change in bank

capital buffer (ΔBUF) indicating short-sightedness in bank capital management practice.

7.4.6. Prudential Capital Ratio

The PCR is used as an explanatory variable to measure the impact of individual bank regulatory capital requirements imposed by APRA on a bank’s total capital ratio. The PCR reflects the minimum requirement imposed by APRA for an individual bank’s total capital ratios55. A positive sign on the coefficient of PCR indicates that a change in APRA’s PCR is having the intended effect on banks’ capital ratios. Note that the impact of PCR is examined on the change in total capital ratio (ΔTCR) instead of capital buffer; this is because the Basel regulatory minimum is no longer important when considering individual bank capital requirements. The null and alternative hypotheses for PCR are as follows:

H8: Prudential capital ratios (PCR) have no relationship with the change in total

capital ratio (ΔTCR).

H8a1: Prudential capital ratios (PCR) are positively related to a change in total

capital ratio (ΔTCR), demonstrating regulatory requirements are successfully influencing bank capital.

An interaction variable B2_PCR is constructed as the interaction of BASEL2 and PCR variables, designed to examine whether the influence of PCR over capital ratios is significantly different after the implementation of the Basel II framework in Australia. Basel II implemented a greater focus on increased risk-sensitivity and increased emphasis on supervisory review. Basel II gave regulators greater powers and tools than previously available to assess bank risk, such as the ICAAP. As discussed, APRA formalised a link between PCR assessment and PAIRS and the ICAAP with the move to Basel II. A positive sign for B2_PCR would indicate PCRs have had a stronger influence on bank capital ratios in the presence of greater risk-sensitivity and regulatory powers implemented with Basel II. Alternatively, one explanation for a negative result could be that after the implementation of Basel II capital requirements, banks react more quickly to conditions influencing target capital ratios and update capital ratios accordingly. Since PCRs are updated relatively infrequently, changes in individual bank capital requirements may have already been compounded into bank capital ratios resulting in a more subdued relationship between PCR and the capital ratio under Basel II. The null and alternative hypotheses for B2_PCR are as follows:

H9: The implementation of Basel II does not significantly influence the

H9a1: The implementation of Basel II has a positive influence on the relationship

between PCR and the change in total capital ratio (ΔTCR) indicating the more formalised and stronger supervisory review powers under Basel II have increased the influence of PCRs on bank capital.

H9a1: The implementation of Basel II has a negative influence on the relationship

between PCR and the change in total capital ratio (ΔTCR) indicating the Basel II framework has resulted in a more subdued relationship between PCR and TCR.

Two interaction variables are included to examine the influence of PCRs during an upturn (UPTURN_PCR) compared to a downturn (DOWNTURN_PCR). The downturn period is defined as the period of the global financial crisis, widely documented to begin in 2007:q3 and assumed to finish at the end of 2009. All other periods are classified as upturn periods. The interaction variables are designed to test whether the influence of PCRs on bank capital ratios differ between upturn and downturn periods in the economic cycle.

Past literature has found a significantly different relationship between individual bank capital requirements and capital ratios during downturns. Francis and Osborne (2010) find a negative relationship, suggesting that changing capital requirements during more favourable times has a greater effect on capital ratios than during a downturn. Alternatively, during a downturn banks may be more aware of capital positions, due to greater market discipline and regulatory intervention. If this is the case, DOWNTURN_PCR may be significantly larger than UPTURN_PCR. Thus, no expectation is assumed on the differences between the interaction terms, and the hypothesis is left to empirical investigation. The null and alternative hypotheses for the PCR interactions with UPTURN and DOWNTURN are as follows:

H10: A downturn (upturn) does not significantly influence the relationship between

PCR and the change in total capital ratio (ΔTCR).

H10a1: A downturn (upturn) has a significant influence on the relationship between

PCR and the change in total capital ratio (ΔTCR).

The definitions of variables used in the empirical investigation and their expected signs are summarised in Table 7.1. The dependent variable used in the base model for the determinant analysis is capital buffer (BUF). The dependent variable used for models examining the impact of individual bank PCRs is the total capital ratio (TCR).

Table 7.1

Variable Definitions and Expected Sign

This table presents the variables used in empirical estimation and the expected signs of their corresponding coefficients.

Variable Description

Expected Sign

BUF Ratio of excess regulatory capital to risk-weighted assets

TCR Risk-based total capital ratio

L.BUF Lagged regulatory capital buffer +

L.TCR Lagged risk-based capital ratio +

IMP Ratio of impaired assets to total assets + / -

RISK Ratio of risk-weighted assets to total assets + / -

SIZE Natural logarithm of total assets -

ROE Return on equity (ratio of net income to total equity) + / -

BASEL2 Dummy variable: 0: Basel I and 1: Basel II +

GDP percentage change in Gross Domestic Product + / -

L.UR Lagged unemployment rate + / -