Asymmetric effects of the business cycle on bank funding liquidity

PURPOSE: By bridging the funding gap between funding surplus units and deficit units, financial institutions like banks play a crucial role in fostering economic development in a nation. Banks provide the crucial task of organizing individual and institutional resources and directing them to those prepared to engage in business ventures or other productive uses. The aim of this paper is to evaluate the relation between funding liquidity and bank lending growth (BLG). An empirical analysis between bank capital and the funding liquidity ratio on bank lending growth (BLG) using the generalized method of moments (GMM) approach for the sustainable business has been not identified before. Therefore, this study tries to fill this gap. METHODOLOGY: The data was collected from 59 commercial banks in India from 2010 to 2022 which comprises of 21 public sector banks, 18 private sector banks, and 20 foreign banks. The GMM approach was what we employed. This strategy is typically utilized in situations in which the distribution of the data is uncertain and there is a concern with over identification. GMM offers a consistent, asymptotically normal, and efficient estimator in comparison to all of the other estimators that merely use the information presented by the moment conditions. FINDINGS: Findings suggests that there is a significantly negative influence of bank capital and funding liquidity on bank lending. This indicates that higher capital can limit the effect of funding liquidity on the growth of the banks’ loans, therefore the findings are consistent with the hypothesis that higher capital can lower the effect of funding liquidity. This study’s model also reveals the significantly favorable impact that funding liquidity has on the expansion of banks’ loan portfolios, which ultimately results in a more sophisticated increase in the growth rate of bank lending. IMPLICATIONS: This can be an importance piece of information for policy makers in taking accurate decisions to induce the BLG in the presence of an interactive association of funding liquidity and the lending growth rate at different capital levels. We found that the banks’ lending growth rate is significantly influenced by its past values with a significant p-value of less than 1%. The findings imply that capital funds and liquidity funds support the BLG rate in India by strengthening and neutralising the risk involved and absorbing the losses generated by stressed assets. ORIGINALITY AND VALUE: This study makes a significant contribution to the creation of a more in-depth understanding of the potential relationship between banks’ funding liquidity, capital funds, and bankers’ lending behavior, in particular with reference to developing market nations like India.

The impact of bank capital, liquidity and funding liquidity on sustainable bank lending: Evidence from MENA region

PurposeThe paper empirically investigates the link between banking market structure and funding liquidity risk.Design/methodology/approachWith a panel of Vietnamese commercial banks from 2007 to 2021, the system generalized method of moments (GMM) estimator is applied as the primary regression method, while the random-effect model and the corrected least square dummy variable (LSDVC) technique are also considered in robustness checks.FindingsCompetition may increase banks' funding liquidity risk. This finding holds for competition measures derived from the Boone index and concentration ratios but not in the case of the Lerner index as a proxy for market power. Further results indicate that the funding liquidity risk of banks that are larger and have better performance (less credit risk and higher return) tends to be less affected by competition. Besides, the overall impact of bank competition on funding liquidity risk is amplified by the financial crisis and the COVID-19 pandemic.Originality/valueThe study extends the empirical literature by exploring the relationship between bank competition and funding liquidity risk. Additionally, the paper also studies how the impact of bank competition on funding liquidity risk depends on the characteristics of the banking sector and the macroeconomic conditions of the economy, including the moderating effect of the COVID-19 pandemic.

The Joint Impact of Bank Capital and Funding Liquidity on the Monetary Policy's Risk-Taking Channel: A Bird's Eye View from the Euro Area Early Days

Debt finance and economic activity in the euro-area: evidence on asymmetric and maturity effects

Funding Liquidity is the key component of loanable funds of the bank. Sufficient liquidity also boosts banks’ ability to pay-off its dues timely but at the same time it has been proven to be a significant determinant of various historical banking sector crises all over the world. However, there exists very weak empirical evidence suggesting a clear relationship between funding liquidity and bank lending growth (BLG). We have attempted to address this gap by empirically testing the impact of bank capital, funding liquidity and their interaction variable on the BLG using a dataset of 59 commercial banks operating in India for the period 2006 to 2018 consisting of 21 public sector banks, 18 private sector banks and 20 foreign banks. An attempt has been made to examine the interactive impact of the bank capital and funding liquidity ratio on BLG rate using system GMM approach. Our model reveals a positive and significant impact of capital funding, indicating induction of capital in bank leads to higher growth in BLG rate. The results also suggest that the interaction impact of funding liquidity and bank capital on the bank lending growth is significantly negative. Further, a higher capital induction neutralises the overall impact of funding liquidity on the bank lending growth. The study provides implications for academicians and policy makers to comprehend the role of funding liquidity.

Do climate risks affect dirty–clean energy stock price dynamic correlations?

This study examines the relationship between business and tourism demand cycles in Aruba and Barbados during 1970–2015. The study uses a 2SLS method and is grounded in the output gap approach. The results indicate that business cycles explain nearly 49% of tourism demand flows to Aruba and nearly 91% to Barbados. Thus, the study sheds light on the nature of the relationship between business and tourism demand cycles, which could help managers and policy makers refine their strategies to further tourism development. Procyclical and asymmetric movements characterized the long-term co-movements between the business cycles and tourism demand variables. However, individual variables were stationary, hence transitory in nature, and therefore mainly driven by demand motivations. The asymmetric fluctuations were defined by positive and negative gaps, with the former displaying stronger duration effects compared to the latter. The relationship between the two variables seems country specific in nature.

This study investigates the significance of the U.S. funding liquidity measure, TED spread, obtained by subtracting 3-month T-bill rate from 3-month Libor, in explaining the business cycle of South Korea and takes a look at recent changes pertaining to the transition to SOFR(Secured Overnignt Financing Rate) from Libor. This study contributes to the existing literature by empirically demonstrating the statistical significance of the global funding liquidity on the business cycle of South Korea, thereby enhancing the understanding of the business cycle in South Korea, and by examining SOFR which is gaining increasing attention. In applying the probit estimation model, a binary dependent variable is generated based on the business cycle stages and descriptive variables are constructed using term spread, short rate, and funding liquidity measures. The estimation results show that TED spread is significant in explaining the business cycle of South Korea, while the domestic funding liquidity measure proves to be insignificant, and considering U.S. term spread and TED spread along with domestic variables remarkably enhances the explanatory power. Under the circumstance that TED spread is no longer available due to the transition to SOFR, it is necessary to reassess the measurement of funding liquidity, considering recent studies which demonstrate that SOFR is less informative than Libor concerning credit and liquidity risks. Especially in case of the term SOFR, which covers vulnerabilities of overnignt rate and promotes practical use of the new benchmark rate, further research is required to figure out its informational contents on the funding conditions.

This work empirically investigates how commercial banks' aggregate credit supply is associated with business cycle over different regimes of the Korean economy. Linear empirical models employed in most of previous studies are subject to a potential missapecification problem because it is well known that both real GDP and credit supply reveal different dynamic properties over different regimes. This work finds that credit supply has asymmetric effect on business cycle for expansion and contraction phases when the Smooth Transition Autoregressive Vector Error Correction Model (or STAR-VECM) is employed. Our empirical findings are as follows. Firstly, we find that credit supply has procyclical effect on real GDP in all phases. Secondly, the procyclical effects are significantly intensified especially in contractionary phases which indicates asymmetry of its effect. In sum, this result supports 'Credit Acceleration Hypothesis' of Bernanke et al. (1999). Lastly, we further find that real GDP has asymmetric effects on banks' credit supply with countercyclical effect on expansionary regimes.

Business Cycles, Credit Cycles and Bank Holdings of Sovereign Bonds: Historical Evidence for Italy 1861-2013

Funding liquidity has been considered as the lifeline of commercial banks. If there are abnormal fluctuations in funding liquidity, it may lead to bank runs, which may bring about the destruction of commercial banks and even spread to cause a financial crisis. Therefore, exploring the fluctuation characteristics and trend prediction of commercial bank funding liquidity has traditionally attracted much attention. Based on this, this paper analyzes the fluctuation characteristics of commercial bank funding liquidity by Multi-Fractal Detrended Fluctuation Analysis (MF-DFA), and innovatively uses the Moving Trend Entropy Dimension (MTED) for commercial bank funding liquidity fluctuation trend prediction. The results of the study show that the commercial bank funding liquidity is multifractal, so it has predictability; MTED can not only accurately predict the fluctuation trend of commercial bank funding liquidity, but also has robustness. Through effective monitoring of funding liquidity, commercial banks can develop their funding liquidity risk management programs and improve the effectiveness of their own funding liquidity risk control. At the same time, regulators can also achieve the goal of preventing and resolving funding liquidity risks as well as maintaining financial stability.

My dissertation investigated business cycle effects on US sectoral stock returns. The first chapter examined the relationship between the business cycle and sectoral stock returns. First, I calculated constant correlation coefficients between the business cycle and sectoral stock returns. Then, I employed the DCC GARCH model to estimate time-varying correlation coefficients for each pair of the business cycle and sectoral stock returns. Finally, I ran regression of sectoral returns on dummy variables designed to capture the four stages of the business cycle. I found that though sectoral stock returns were closely related to the business cycle, they did not share some of its main characteristics. The second chapter developed two models in order to discuss possible asymmetric business cycle effects on US sectoral stock returns. One was a GARCH model with asymmetric explanatory variables and the other one was an ARCH-M model with asymmetric external regressors. In the second model, square root of conditional variance of the business cycle proxy was characterized as positive or negative risk, depending on the algebraic sign of past innovations driving the business cycle proxy. I found that some sectors changed their cyclicities from expansions to recessions. Negative shocks to business cycles had most power to influence sectoral volatilities. Positive and negative parts of business cycle risk had same effects on some sectors but had opposite effects on other sectors. A general conclusion of both models was that business cycle had stronger effects than own sectoral effects in driving sectoral returns. The third chapter discussed Chinese business cycle effects on US sectoral stock returns at two horizons. At a monthly horizon, the third lag of Chinese IP growth rate had positive effects on most sectors. The second lag of US IP growth rate had positive effects on almost all sectors. At a quarterly horizon, besides the extensive positive effects of the first lag of Chinese IP growth rate, the third and fourth lags also had effects on some sectors. The US IP growth rate had the same pattern, namely positive first and fourth lag effects and negative third lag effects. Using a 5-year rolling fixed window, I found that these business cycle effects were time-varying. The major changes in parameters resulted from the elimination of quota on textiles by WTO, the terrorist attacks on the US, and the 2007 financial crisis.

Two models are developed in this paper in order to discuss possible asymmetric business cycle effects on US sectoral stock returns. One is a GARCH model with asymmetric explanatory variables and the other one is an ARCH-M model with asymmetric external regressors. In the second model, square root of conditional variance of the business cycle proxy is characterized as positive or negative risk, depending on the algebraic sign of past innovations driving the business cycle proxy. This helps to capture any asymmetric effects of positive and negative business cycle risk on returns. We find that some sectors change their cyclicities from expansions to recessions. Negative shocks to business cycles have most power to influence sectoral volatilities. Positive and negative parts of business cycle risk have same effects on some sectors but have opposite effects on other sectors. A general conclusion of both models is that business cycles has stronger effects than own sectoral effects in driving sectoral returns.

We present a dynamic general equilibrium model of production economies with adverse selection in the financial market to study the interaction between funding liquidity and market liquidity and its impact on business cycles. Entrepreneurs can take on short-term collateralized debt and trade long-term assets to finance investment. Funding liquidity can erode market liquidity. High funding liquidity discourages firms from selling their good long-term assets since these good assets have to subsidize lemons when there is information asymmetry. This can cause a liquidity dry-up in the market for long-term assets and even a market breakdown, resulting in a financial crisis. Multiple equilibria can coexist. Credit booms combined with changes in beliefs can cause equilibrium regime shifts, leading to an economic crisis or expansion.