La evaluación: Un modelo para la reflexión

Thus far, the bank liquidity risk has been studied scantly in both emerging and developed markets, and such studies were done using standard liquidity measures. Amongst the known empirical studies on liquidity phenomenon, liquidity was explored within the

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segments of market liquidity and funding liquidity; yet, these two are profoundly interconnected. Liquidity standard measures also failed to grasp the banking sector important characteristic of liquidity spirals (Bai et al., 2014; Bouwman, 2009; Krishnamurthy et al., 2016). Despite the introduction of theoretically baseless Basel III liquidity measures, there are still some shortcomings as both the standard and Basel III liquidity measures are not comprehensive enough, cannot be aggregated and therefore cannot be used as part of DSGE models (Brunnermeier et al. (2013).

Similar to prior studies, the empirical evidence in the present study showed that banks have a tendency to reduce their liquidity mismatches as interest rates increase. In addition, banks increase their liquidity as the loan portfolio deteriorates. The ALMI, like the LMI, is an aggregate measure and therefore provides a macro-prudential liquidity measure that can be utilised in DSGE models. This study makes seven major contributions to the literature on the liquidity risk embedded in the asset–liability mismatches of banks.

Thus, the study advanced the frontier of knowledge by developing two liquidity measures, which together with the Basel measures and traditional liquidity measures were compared and contrasted using a 2-step GMM model. The constructed BLMI and ALMI appeared to perform better than the existing liquidity measures. Firstly, the BLMI and ALMI were an improvement on Basel III and standard liquidity measures since they had been built upon Brunnermeier et al.’s (2013) LMI. The LMI is a quantum leap from the traditional liquidity measures (Bai et al., 2016), as it considers the most essential features of bank liquidity spirals. This measure is inclusive of the funding liquidity of a bank and the market liquidity of its assets. Brunnermeier et al. (2013) observe that it is not the level of gearing that is important, but rather the proportion of debt comprising short-term demandable deposits. Moreover, the LMI can account for bank systemic risk and can be estimated empirically unlike other liquidity measures that cannot be measured in timely fashion (Brunnermeier & Oehmke, 2012). Despite, the extensive pluses associated with LMI, Bai et al. (2014) highlight the major shortcomings of the LMI inform of how the liquidity weights were computed. The liquidity weights used in empirical testing of the LMI were arbitrarily chosen and were therefore not backed by theory. The development of the new measures

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of liquidity in terms of the BLMI and ALMI was the main contribution of this study. The MLMI measures were developed based on theoretically motivated liquidity weights. The asset liquidity weights were modified considerably and are different from the weights computed and utilised by Brunnermeier et al. (2013), Bai et al. (2014) and Krishnamurthy et al. (2016). These scholars utilised the haircuts on assets as a measure of asset weights. Krishnamurthy et al. (2016) argue that haircuts are natural measures of asset liquidity sensitivity because they vary with measures of asset price volatility and tail risk for a given asset class. However, Corrigan and De Terán (2007) indicate that haircut usage in the determination of assets weight is not consistent since haircuts are not uniform to all dealers in the money market, even in a situation where different counterparties use the same type of collateral. Moreover, the repo haircut data for each bank are inaccessible in an ideal world (Krishnamurthy et al., 2016). Instead of using haircuts, we computed the asset liquidity weight, using a combination of spread and volume, where the absolute bid–ask spread was scaled by the volume traded on that day. This is a theoretically and empirically validated measure of market liquidity (e.g. Roll, 1984; Glosten and Milgrom, 1985; Chordia , 2001, Huberman and Halka, 2001).

Another modification was done on the liability weights. Instead of using OIS–TBill spread as per Bai et al. (2014), we used the spread between the treasury bill rate and the SABOR. This measure, according to Nagel (2014), accurately measures the time variation of a money market instrument. Since the liquidity condition was assumed to be accurately depicted by the SABOR–TBill spread (STBS, we used it in the calculation of liability side liquidity weights.

Furthermore, we contribute to the body of knowledge by empirically testing the new liquidity measures against the determinants of liquidity as means of validating the BLMI and the ALMI. Thus, the empirical findings of this study highlight the determinants of liquidity and the importance of measuring bank liquidity in the context of asset–liability mismatches. An understanding of liquidity risk was achieved through examination of determinants of liquidity. The determinants of liquidity were tested in terms of how and to which extent they influence Basel III liquidity measures, other traditional liquidity measures and the new liquidity measure (MLMI). An analysis of Basel III liquidity

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measures and selected traditional liquidity measures was done at bank-specific level while the analysis of the determinants of LMI was done at both bank level and market- wide level, taking into account that banks manage their assets and liabilities in the sphere of interdependence in the financial sector. The analysis was therefore, done in the context of interbank linkages, since in modern finance theory, financial markets are interconnected in multiple ways. In addition, the ALMI provide more information on the fragility of the financial sector because it is an aggregate measure of liquidity that can be used as part of DSGE models. Our results provide new insights into the study of liquidity risk in light of asset–liability mismatches since we investigated how the determinants of liquidity are endogenous.

Firstly, we found that banks increased their liquidity buffers during times of turmoil as both BLMI and ALMI improved during the period 2007–2009. Secondly, the improvement in economic performance as measured by GDP growth resulted in a rise in the LMI at market-wide level. However, the increase in GDP growth resulted in a decrease in the LMI at bank level. Thirdly, we found no evidence to support the theory that banks that heavily depend on external funding end up in serious liquidity problems as the south African banks were resilient during the 2007–2009 crisis. Neither did our results support the view that external funding is costly and therefore banks should minimise depending on external funding. Fourthly, we also found that bank liquidity is influenced by both bank- specific factors and macro-economic factors. Nevertheless, the liquidity measures had different associations with the determinants. Finally, there is negative relationships between bank performance as measured by ROA and the MLMI measures.

The findings in this study imply that any policy implemented with the intention of increasing bank capital is good for bank liquidity since the financial fragility crowding out hypothesis is outweighed by the risk absorption hypothesis as the relationship between capital and bank liquidity is positive. Banks should monitor closely the banks specific and market wide liquidity developments because MLMI measures were found to negatively affect bank performance.

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