Stability in banking has been a long-standing issue in the financial system at the financial institution. Since after financial crisis 2007/2008, the issue has been gained much important. The limiting bank of size is the way policy makers in US and European by demanding more capital and liquidity in line with Basel III requirements and also restricting bank’s involvement in risky market [1]. Then, several recent studies have shown interest in the bank business model, in addition to limiting the size of the bank-to-bank stability [2,3].

In this study Kohler’s argued the business model related to how banks make profit, serve they costumer and distribution channel they have. The study also revealed that banks can find profitable sources of funding with the help of business models. Smiliarly, Ali [2], encourages banking system in Pakistan to increase bank stability by efforts in mobilizing customer deposit. Furthermore, previous literature report that a debate on bank deposit funding and wholesale funding agreement still required empirical evidence [2-6]. Therefore, this aim paper is show that how bank’s funding risk affects its stability more specifically.

This study focuses on Indonesian banking, because at the time of financial crisis the interest rate in Indonesia was still the highest than ASEAN countries and the United States. So that many investors make funding that can support the financial stability of the bank. After crisis, Indonesian banking was stable and exhibited the highest performance in Asia on 2010 - 2015 [7,8]. In other study Trinugroho et al. [9], shows that banking in Indonesia has a uniquely behavior. During the financial crisis, net interest margins were actually very high compared to other banks. So, we interest to examine funding risk and stability more spesifically. To achieve our aim, we adopt two-step generalized method of moment (GMM) system analysis.                

This study contributes to examine the effect of funding risk to bank stability. The evidence of this study is important as a complement to findings of a previous study Ali and Puah [2], analyze the effect of funding risk to bank stability on Islamic and conventional in Pakistan Banking. 

Then, study Adusei [3], analyze the effect of Funding risk to bank Stability in Ghana rural bank. However, this paper focus analyzes in commercial bank in Indonesian banking.

We also examine the effect of funding risk on bank stability within ownership in indonesian banking. Then, this study following previous study [7], using ownership with type based on the size of core capital can be divided into four categories (BUKU 1, BUKU 2, BUKU 3 and BUKU 4). This research is important to find ownership of funding risk and bank stability in previous study. Pak [10], study Focuses on ownership of bank stability in the Eurasian Economic Union. Then, Ghenimi et al. [11] and Djebali and Zaghdoudi [12], focussing in the MENA region. Study Mutarindwa et al. [13], focussing in the africans bank. Next, the study Shaban and James [14], show the private banks in Indonesia are the best performers in terms of cost and profit efficiency. When the bank's performance improves, it will increase the stability of the bank. This study provides a perspective on how the effect ownership of Funding risk to bank stability on state owned bank and private bank based on previous study [11-14].

The remainder of the study will be organized as follows: Section 2 presents the Hypotheses development. Section 3 outlines the data and methodology. Section 4 results the research. Section 5 discusess with empirical studies. Section 6 concludes the research and offers.

Hypotheses Development

Funding Risk and Bank Stability: Many recent studies have discussed bank stability in relation to competition [15-24]. This is also still a debate, Islam et al. [16], shows that competition can increase stability in Malaysia and Singapore, the results are similar Tongurai and Vithessonthi [15], Saif-Alyousfi et al. [17], in GCC Countries, Danisman and Demirel [18], in Developed Countries, Soedarmono et al. [25], in 12 Asian economies, Liu et al. [26], for South East Asian commercial banks and Yeyati and Micco [27], in 8 Latin American countries. Another study, competition can reduce stability in Indonesia and Thailand. Similar results were found Yusgiantoro et al. [7], in Indonesia, Agoraki et al. [28], in Central and Eastern European banks, Fu et al. [29], in the Asia Pacific and Kasman and Kasman [30], in Turkey.

In addition, some studies are getting interested in seeing how stability can be affected apart from competition. In Acosta-Smith's et al. [31], finding leverage ratio requirement can be increase bank risk-taking in EU bank. Then, Kim et al. [32], show bank diversification decreased the variance of bank stability prior to the financial crisis but increased its variance during the crisis. Chen et al. [33], find high corruption increase risk-taking behavior of banks in emerging countries. Cubillas and Gonzalez [34], find financial liberalization has been increasing risk-taking in developed and developing countries. After the financial crisis of 2007/2008, academicians and researchers have moved to focus on the effect of financial crises on the stability of banks of bussiness models. In several studies, business models are often linked to funding structures. This section will discuss the literature relevant to funding risk and bank stability, then developing hypothesis:

Recently much of the literature is reviews each type of funding structure are liquidity risk or liquid bank creation [12,35-37]. Then, several empirical studies examined the effect of liquidity risk or liquidity bank creation on bank stability [11,12,38]. study Ghenimi et al. [11], show that credit risk and liquidity risk do not have an economically meaningful reciprocal contemporaneous or time-lagged relationship. However, both risks separately influence bank stability and their interaction contributes to bank instability. Then, Djebali and Zaghdoudi [12], find the positive effects below these optimal thresholds, credit risk and liquidity risk become detrimental to bank stability in high regime. Gupta and Kashiramka [38], show increasing liquidity creation in small and large banks can be reduces their financial stability compared to the medium-sized banks.

Based on theory of Calomiris and Kahn [4], states that bank risk decreases in the presence of effective monitoring of bank funds and sophisticated diversification of funding resources. Then, Huang and Ratnovski [5], presented their work to better understand the dark side of bank wholesale funds. Their investigation argues that wholesale funds reflect bank risk because of the potential to quickly change prices. They further suggest that wholesale funds are less volatile in nature. In the same way, the findings of Shleifer and Vishny [6], show that customer deposits are relatively more stable, but their pricing process is too slow. It is also a noteworthy argument that banks face greater volatility in the presence of a larger share of deposit-free funding [39]. However, in the study Köhler [40], argued reports that different types of banks are linked to different types of non-deposit funding. In addition, this study finding retail-oriented banks become unstable when they increase non-deposit funding. Besides that, only a few studies examine the effect of funding risk and bank stability in commercial banking. Adusei [3], presented their work on funding risk and bank stability on rural bank in Ghana Banking. This study finding rural bank in Ghana show effective deposit mobilization strategy is more likely to be stable, that bank use of larger customer deposit funding is increase stability. Then, Ali and Puah [2], analyze the relationship between funding risk and bank stability in Pakistan Banking. This study finds the stability of Pakistan retail banks increases due to increase in the stability of retail banks. Overall, the literature suggest that funding risk can be increase stability. Hence, the formulation of our first hypothesis is as follow:

• H1:Funding risk is positively related with bank stability

Ownership, Funding Risk and Bank Stability

Most of this research concentrated on the effect of the ownership and bank stability of the banking sector. There has been, however, a lack of studies that dealt with the relationship between ownership and bank stability. Whereas, the funding risk are important, since it frequently led to financial crisis. this paper to invertigate the relationship between funding risk and bank stability with the ownership. According, Pak [10], analyze relationship between ownership and stability. This study find State ownership has a significant positive effect on bank financial stability. who report that European public banks are less prone to default due to their easier access to government funding. Samet et al. [41], find publicly traded banks engage in less risky activities than their privately owned peers. Similarly, Alkhouri find private-owned banks are riskier than government-owned banks in GCC Countries. Then, Tan and Anchor [42], find government-owned banks are more stable than privately owned banks during the financial crisis. However, Shaban and James [14], show the private banks in Indonesia are the best performers in terms of cost and profit efficiency.

Besides that, we see a link with funding risk when ownership is linked to bank stability. According, Tan and Anchor [42], analyze the impact of competition on credit risk, liquidity risk, capital risk and insolvency risk in the Chinese banking industry. This study finding each the ownership type to reduce insolvency risk leads to higher liquidity risk. However, Study Pak [10] and Samet [41], there are indicating that banks that have large funding risk but whose activities are limited will cause banks to be more unstable. However, a bank that has a large funding risk and has more activities makes the bank more stable. smiliarty, study Kohler finding small banks has more stabe than large banks, because small banks have increased their share of non-interest income, since this reduces their dependence on deposit funding and net interest income. Our hypotheses as follow:

• H2: Small bank has negative effect with higher funding risk on bank stability; large bank has positive efffect with higher funding risk on bank stability

Data Description

The main objective of this study is to investigate the relationship between funding risk and bank stability within certain periods and among banks. The study data include coventional banks operating in Indonesian Banking. Our focus on the Indonesian banking is stable and exhibited the highest performance in Asia on 2010 - 2015. Our primary analysis over the period 2004–2018 includes 141 conventional banks in Indonesian. The main data sources are collected from the audited annual financial report of each bank. However, the data on the macroeconomic variables are obtained from the Indonesian Statistics Agency.

Variables Description

Dependent Variable: In terms of the dependent variables, we use several measures reflecting bank insolvency risk and capital ratio as bank stability. In order to measure insolvency risk, we follow Lepetit and Strobel [43], in constructing two measures of Z-score for bank i at year t based on the following formula:

MROA represents the average value of the return on assets, while SDROA is the standard deviation of the return-to-assets ratio. For each bank, both MROA and SDROA are calculated from 2004 to 2018. Meanwhile, EQTA is the ratio of total equity to total assets.

Independent Variable

In order to measure funding risk as our main variable independent, we construct variable is funding risk (FUNDRISK) which is measured by a Z-score, we follow Ali and Puah [2] and Adusei [3]. The Z-score is computed as follows:

Where Z-score (FUNDRISK) is the funding risk Z-score of bank i in time t which measures the number of deviations customer deposits would have to fall to compel the bank to wipe out equity finance; DEP/TAit is the deposit to total assets ratio of bank i in time t; E/TAit is the equity to total assets ratio of bank i in time t; and σ(DEP/TAip) is the standard deviation of the deposit-to- asset ratio. This measure of funding risk is important because retail-oriented banks fund their activities with customer deposits [40]. It is, therefore, expected that funding risk will positively impact bank stability.

Then, we aslo addition independent variable ownership type with core capital divided into four categories (BUKU 1, BUKU 2, BUKU 3 and BUKU 4) stipulated by Bank Indonesia in the regulation PBI No. 14/26/2012. Banks under BUKU 1 are those with core capital of less than IDR 1 trillion. BUKU 2 comprises banks with core capital from IDR 1 trillion to IDR 5 trillion. BUKU 3 comprises bank with core capital from IDR 5 trillion to IDR 30 trillion, while BUKU 4 comprises banks with core capital exceeding IDR 30 trillion [7].

Besides Funding risk measured by a Z-score and ownership type, we also consider bank-specific control variables that might affect bank stability. These include the market concentration (HHI), size bank measured the logarithm of banks’ total assets (SIZE), the cost-to-income ratio (CTI), the ratio of loans to total assets (LTA), the ratio of non-interest income to total assets (NON), inflation (INF), GDP per capita growth (GDP), Dummy variable for goverment bank (GOV) and Dummy variable for publicly traded bank (LISTED). we incorporate SIZE as control variable to account for the role of the too big to faileffect in which larger banks tend to undertake risky projects to exploit the government bailout [21]. Because Indonesia financial safety nets law No.9/2016 eliminating the explicit government bailouts is only effective since 2016, the issues of bank moral hazard due to the too big to fail effect in Indonesian banking is still prevalent with empiris studies [2,3, 7].

CTI is measured by the ratio of operating expenses to operating income to control for bank inefficiency. Higher bank inefficiency is expected to negatively affect bank profitability, which in turn reduces the capacity of banks to raise capital and strengthen financial stability. Banks’ third-party funds include time deposits, current account and savings. Wa also include LTA has negative impact, because loans might be a source of bank risk coming from financial intermediation activities, especially if loans are granted excessively [7].

The ratio of non-interest income to total assets (NON) is also considered as control variable to take into account the impact of bank income diversification. The impact of NON on bank stability measured by bank solvency ratios based on Z-score and capital ratios remains unclear. On the one hand, higher non-interest income might strengthen bank profitability and stability when banks have the capacity to manage risk through product diversification instead of relying on lending activities, although such a relationship might be conditional on bank-specific factors [44,45]. On the other hand, higher non-interest income might reflect that banks undertake cross-subsidization strategies between lending activities and non-interest income generating activities [9]. Consequently, such bank behavior might increase bank riskiness due to the fact that banks can loosen credit standards and underestimate credit risk. We also addition with macroeconomic variable control is inflation this country and GDP per capita growth (GDP).

Methodology

Regarding the econometric methodology, we run regressions in two stages. In the first stage, we regress the equation of bank stability on the funding risk, ownership and a set of control variables simultaneously. In the second stage, we repeat the previous stage, but we also adding the interaction terms between the funding risk and dummies representing the size of bank core capital size (BUKU 1, BUKU 2, BUKU 3 and BUKU 4) on bank stability.

In order to estimate these models, we utilize dynamic panel data techniques because bank riskiness can be affected by its past values [7, 46, 47]. Yet, the link between funding risk and stability in banking might also suffer from a reverse causality problem. Our dynamic panel data model is estimated using the two-step GMM (generalized methods of moments) or the system GMM following Blundell and Bond [48], in order to produce more efficient estimates than using the one-step GMM [49]. We further take into account a finite sample correction developed by Windmeijer [50] and specify orthogonal transformations of instruments that might somehow account for unobservable factors related to bank-specific characteristics. Overall, our system GMM is valid when the AR (2) test and the Hansen-J test are not rejected.

Descriptive Statistic

We applied the descriptive statistics of bank stability, funding risk, ownership type (BUKUI, BUKUII, BUKUIII and BUKUIV) and specific bank including market concentration (HHI), bank size (SIZE), CTI, LTA, NON, inflation (INF), Gross Domestic Product (GDP), goverment (GOV) and LISTED in Indonesian. The descriptive statistics of the definition, Observsation (Obs), mean and the standard deviation (Std.dev) of these different variables are presented in Table 1.

From Table 2, it can be shown that only SIZE and BUKUIII are higher value is 0.4888. If the correlation value between two variables is 0.9 or greater, then there exists a problem of multi-collinearity in the model [2,51,52]. Therefore, Table 2 reports the correlation between independent variables, which is not exceeding the minimum threshold level, suggests that multi-collinearity is not a problem in our case.

Table 1: Descriptive Statistics

Source: Author Calculation

Table 2: Correlation Matrix

Source: Author Calculation

Table 3 presents the empirical results of the estimation of model (1-4) using four measure bank stability include funding risk and bank specific. After trying these specifications, we end up with these two main specifications, which pass all the econometric concerns discussed in the methodology section above. Therefore, the model appears to fit the dynamic panel data well, since all relevant tests are highly significant as presented below in Table 3. We are interested using the GMM-system estimator, more specifically with the use of the GMM system estimator of Arellano and Bond, Arellano and Bover and Blundell and Bond [48], to verify the existence of the effect of the explanatory variables on the bank stability. Table 3 presents the results of the Hansen test for the most restriction identification and the AR (2) of the second-order correlation series. According to Table 3 (Columns 1, 2, 3 and 4) for among ownership type, the Hansen test with a p-value much greater than 0.1, which means that the null hypothesis H0 of the validity of over identification restrictions (validity of instruments) cannot be rejected. It can therefore be concluded that the instruments used for this regression are valid, thus inducing the validity of the results. The second-order autocorrelation tests of disturbances of BUKUI, BUKUII, BUKUIII and BUKUIV (Columns 1, 2, 3 and 4) show that the AR (2) test values (0.885, 0.923, 0.932, 0.891). This implies that the empirical model has been correctly specified because there is no serial (autocorrelation) correlation in the transformed residues; therefore, the instruments used in the models are valid.

In addition, we document that funding risk has a positive and significant impact on bank stability measuring z-score EQTA. Then, among ownership type has difference effect on bank stability. First, BUKUI has negative significance effect on bank stability. Second, BUKUII and BUKUIII has positive effect on bank stability but no significance. Last, BUKUIV has positive significance on bank stability. Then, our result bank-specific control variables, These are the market concentration (HHI) has positive effect in bank stability, then size of total assets (SIZE) has a negative and significance of bank stability, the Cost-To-Income ratio (CTI) has a negative and significance of bank stability, the ratio of Loans to Total Assets (LTA) has positive effect on bank stability, the ratio of non-interest income to total assets (NON) has a positive of bank Stability, Inflation (INF) has negative significance on bank stability. Finally, GDP per capita growth (GDP), Dummy variable for government bank (GOV) and Dummy variable for publicly traded bank (LISTED) has negative effect on bank stability. Yet, our dynamic panel data models are also valid, because the AR (2) test and the Hansen-J test are not significant at least at the 5% level.

In table 4, we also examine the interaction terms between the funding risk and dummies representing the size of bank core capital size (BUKU 1, BUKU 2, BUKU 3 and BUKU 4) on bank stability. The result is BUKUI has negative no signifcance on bank stability, but when it is interacted with funding risk it becomes negative significance on bank stability. BUKUII and BUKUIII has positive effect on bank stability with funding risk. Then, BUKUIV has positive effect on bank stability, but when interacted with funding risk, BUKUIV has negative effect on bank stability. Then, our dynamic panel data models are also valid, because the AR (2) test and the Hansen-J test are not significant at least at the 5% level.

Table 3: Funding Risk and Bank Stability; Baseline

t-Statistics in parentheses. * p <0.1, ** p <0.05, *** p <0.01

Robustness Check

In this part we run two robustness tests to examine the difference between the effect of funding risk on banks’ stability. For the first test we use alternative dependent variable is Loan Loss Provisions. The second test we examine the effect of funding risk to bank stability with Alternative econometric methodology (Fixed Effect method). Hence, the study shows in Table 4 our result th effect funding risk negative significant on loan loss provisins. This result is consistent previous study [2,3]. Then, we use fixed effect be alternative econometric methodology, the result is funding risk positive significant on financial stability. Then, BUKUI mostly consistent with negative effect on financial stability. Our result also the effect funding risk of bank stability [2,3,6].

This research aims to examine the effect of funding risk on bank stability using Z-score EQTA. This result signifies that bank stability increase with the improvement in funding risk of the banks. The findings are associated with the prior expectations of this study, signifies that funding risk should impact positively on bank stability. The possible implication of this finding could be that commercial banks in Indonesia with the higher funding risk, the bank can mobilize customer deposits to fund their activities so as to increase bank stability. This result is supporting Ali and Puah [2] and Adusei [3], find retail banks that fund their activities with customer deposits, the higher the funding risk, the more stable the funding source will create stability for the bank. Then, khan [53], finding the higher funding risk decrease bank risk in business activities than the bank with higher deposits. Ghemini also finding Liquid assets enable banks to overcome any urgent problem due to unexpected money withdrawal which may affect the overall banking stability if the bank is not holding sufficient liquid assets that could be transformed into cash immediately and at a low cost.

This result is strengthened by the robustness test replace bank stability measure Z-score simple leverage ratio with Loans loss provisions (LLP) to Total Loan. which shows that the funding risk has negative effect on loan loss provision. This means with the higher funding risk, the bank in indonesian can redusce risk their acticity. Then, using alternative ecenometric is fixed effect, this result shows the funding risk consistent positive effect on bank stability.

Table 4: Funding Risk and Bank Stability; The Role of Core Capital

t-Statistics in parentheses. * p <0.1, ** p <0.05, *** p <0.01

Then our result examines the effect of funding risk on bank stability with ownership. This study finding small bank (BUKUI) with core capital less the IDR 1 trillion has negative effect on bank stability with funding risk. Small banks have activities that are limited based on core capital, causing their income to be limited outside of their operations so that they depend on deposit funding and net interest income which causes bank instability. This result is supporting study Kohler finding small, medium and large retail-oriented banks stronger in maintaining stability that is less dependent on non-savings funding. The small banks have increased their share of non-interest income, rather than their dependence on deposit funding and net interest income. Smiliarty, Study Pak [10] and Samet [41], find banks that have a large funding risk but whose activities are limited, causing the bank to be more unstable. However, a bank that has a large funding risk and has more activities makes the bank more stable. 

Then, the large bank (BUKUIV) has positive significance effect on bank stability, but while the interaction between large banks (BUKUIV) and funding risk has a negative effect on bank stability but not significant. This study indicates large banks fund their activities with customer deposits make bank instability. This result supporting study Khan [53], finding lower funding liquidity risk increases bank risk more in the banks having higher levels of deposits. Big banks take less risk in response to lower funding liquidity risk because they have in relative terms less scope due to their more complicated business models that are less focused on traditional bank lending and they also face tighter prudential supervision and regulatory constraints given their systemic importance within banking sectors. 

This result is strengthened by the robustness test replace bank stability measure Z-score simple leverage ratio with Loans loss provisions (LLP) to Total Loan. which shows that the BUKUI mostly consistent has negative effect on loan loss provision as well as to bank stability using fixed effects.

In this paper, we investigate the effect of funding risk on stability in Indonesian banking. The study employs a panel of 141 banks in Indonesia during the period 2004-2018. The indicator used as a dependent variable to proxy for stability is the Z-score. In an attempt to address the problems of heteroskedasticity and endogeneity and to offer precise and consistent parameter estimations, we use two-step GMM estimations.

The study has several findings, first, finding the higher funding risk increasing bank stability in general. Second, the effect of funding risk on bank stability with ownership. Based core capital, the small bank has higher funding risk can be decrease bank stability. however, the large bank has higher funding risk maybe increasing of bank stability.

Our finding has important policy implications, particularly relevant to Indonesian banking objectives for the greater financial stability that is sustained by funding risk. So, the policy makers can improve bank stability by increasing efforts to mobilize customer deposits with among ownership.

Limitation and Suggestions

This research only uses a measure of bank stability based on simple leverage ratio and does not involve other bank-specific factors as well. Future studies are expected to be able to use other measures of bank stability such as the bank insolvency risk Lepetit and Strobel [43] and focussing with among period as financial crisis 2007/2008.

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