Second, understanding the dynamic interactions and causal feedbacks between financial inclusion, financial stability and macroprudential policies in Saudi Arabia during the period 1980-2019. Our empirical results indicate a long-run relationship between financial stability, financial inclusion and macroprudential policy.
Introduction
Mehrotra and Yetman (2015) stated that if greater financial inclusion stems from irresponsible credit growth or unregulated parts of the financial system, systemic risks to financial stability may increase. Therefore, when implementing their strategy, policymakers should take into account the existence of synergies or trade-offs between financial inclusion and financial stability on the one hand, and the direct effects of macroprudential policies on financial stability on the other.
Selective Literature Review: The Financial Inclusion-Financial Stability Nexus
In a recent paper, Ahamed and Mallick (2019), using data from a global sample of 2,635 banks in 86 countries, showed that the relationship between banking stability and financial inclusion is strong. In particular, banks perform better in reducing risks when financial inclusion is high.
Concepts: Definitions and Measurement
Financial Inclusion
One of the main objectives of this work is to resolve such a problematic question in the case of Saudi Arabia and conduct adequate econometric tests to deal with the nature of the financial inclusion-financial stability relationship. This improvement was mainly driven by the increase in the number of bank branches.
Financial Stability
The credit-to-GDP ratio gap (known as Basel gap or credit gap), is defined as the difference between the deviations of the credit-to-GDP ratio from the estimated trend. Consequently, the use and interpretation of the credit-to-GDP ratio gap as an indicator of financial stability should be used with caution in the context of emerging economies such as Saudi Arabia.
Macro-Prudential Policies
In the 2020 Financial Stability Report, we find the main macroprudential measures and policies such as reserve requirement ratio (RRR), loan-to-value ratio (LTV), debt-service-to-income ratio (DTR), countercyclical capital buffer (CCB) and capital conservation buffer (CCB) etc. Zulkhibri and Naiya (2016) believe that financial stability is not only influenced by macroprudential policy, but also by a range of other policies.
Contextual Settings: Financial Inclusion and Macro-Prudential Measures in Saudi Arabia . 14
Data Sources and Statistical Proprieties of the Variables
To investigate the causal relationship between financial inclusion and financial stability under macroprudential policy, we draw data from a variety of sources, including SAMA, the World Bank (WB), and the International Monetary Fund (IMF) depending on their availability. Financial stability is measured by credit-to-GDP ratio gaps obtained from the one-sided HP filter decomposition of the credit-to-GDP ratio series. Financial openness (FO) is measured by foreign direct investments, net inflows as a percentage of GDP and the size of the financial system (FSZ) is measured by.
The results obtained will dictate the nature of the model to be estimated and the appropriate econometric techniques to be used. In Table 3, we present the descriptive statistics of the variables with some insights into their correlations. From Table 4, we can see that the correlations between some of the independent variables are relatively high.
Unit Roots Tests
Model Specification and Econometric Techniques
To account for the effect of human capital development on financial inclusion and subsequently on financial stability, we use the human development index (HDI) which is available over a long time span. Good governance (GG) and quality of institutions (QI) are also mentioned as one of the most important variables influencing the relationship between financial inclusion and financial stability. Using data at the individual level, Allen et al. 2016) stated that stronger legal rights and a more politically stable environment are positively correlated with financial inclusion.
Navajas and Thegeya (2013) tested the effectiveness of Financial Soundness Indicators (FSIs), which are aggregate measures of the health of a country's financial sector and are indicators of potential banking crises, to see if they help predict crisis events. To study the interactions and causal relationships between financial inclusion and financial stability and macroprudential policies, we adopt a multi-stage approach. This method surpasses Granger's sequential methodology and is based on the Wald test, which is independent of the degree of stationarity of the series and their cointegration.
Theoretical Brief Review of ARDL Model
The optimal model is the model with the smallest value of the AIC or BIC5. In this model, the variable's short-term effect on is revealed while the long-term effect is obtained by. The procedure of the test consists of comparing the calculated F with the critical limits (lower, upper) value developed by Pesaran et al.
If the calculated F-statistic is above the upper critical value, the null hypothesis of no long-term relationship can be rejected regardless of the order of integration of the variables. Once the cointegration relationship is established, the ARDL long-term model can be estimated as it is in equation (4). The final step is to disentangle short-term and long-term dynamics by estimating an ARDL-EC model.
Empirical Results
Bounds test indicates that only one cointegration relationship exists between the three variables when the regression is normalized on financial stability variable. The calculated F-statistic is higher than the upper value, implying a long-run relationship between financial stability, financial inclusion and macroprudential policy. When normalizing the regression on financial inclusion index, the null hypothesis of no cointegration is accepted.
Results of Table 8 show that estimate of long-run coefficients of financial inclusion index and macroprudential policy are significant but negative. These results suggest that financial inclusion in Saudi Arabia can only have positive effects on financial stability if the development of such activities is supported by macroprudential measures. In other words, financial inclusion can have a negative impact on financial stability without proper supervision.
Stability and Robustness Check
- Diagnostic Tests
- Stability Tests
- Control Variables
As developed in subsection 4-3, the literature on the financial inclusion-financial stability link recognizes that many variables can condition such a link, such as Real GDP per capita, education or human development index, financial openness (FO), size of the financial system (FSZ) and good governance (GG). Using the World Bank's Global Financial Development Database, including 164 countries, Morgan and Pontines (2014) show that financial inclusion increases with GDP per capita, but the relationship between financial inclusion and financial stability is less clear. Bayar (2016) and Frankel and Saravelos (2012) observed that there is a strong relationship between financial openness and financial inclusion through financial development leading to greater stability of the financial system.
To account for the effect of this variable on the link between financial inclusion and financial stability, we run two more estimates of our ARDL for the period 1996–2019. To measure the effect of good governance on financial stability and financial inclusion, the model can be re-estimated using a different proxy or a longer period. The assessment of the ARDL model shows that SAMA's implementation of the Basel III recommendations led to an improvement in financial stability in Saudi Arabia, but with a delayed effect.
Causal Relationships Between Financial Stability, Financial Inclusion and Macro-Prudential
In what follows, we use the Toda-Yamamoto methodology to test Granger causality relationships between financial stability, financial inclusion and macroprudential policy. The empirical results of the Granger causality test based on the Toda and Yamamoto (1995) methodology are presented in Table 12. We only see one-way causality running from financial inclusion to financial stability and from macroprudential policy to financial stability and from the combined effect of financial inclusion. and macroprudential policy for financial stability.
Such empirical results support the fact that higher levels of financial inclusion would lead to greater financial stability. Moreover, financial inclusion combined with macroprudential measures could lead to greater financial stability. The joint effect of both variables on financial inclusion is positive and the causality test is highly significant.
Conclusions and Policy Recommendations
In addition, empirical findings show that both financial inclusion and macroprudential measures Granger cause financial stability, and their joint effect is also significant. Overall, our empirical results confirm the importance of considering the synergies and trade-offs between financial inclusion and financial stability in policy making. Given the recognized positive impact of financial inclusion on financial stability, Saudi authorities should work to remove or reduce barriers to financial inclusion, especially among the most excluded segments such as stateless persons, women and people outside the labor force.
In addition, the strong development of digital financial services will also increase financial inclusion; therefore, it should be accompanied by strong cyber security. Most importantly, macroprudential policies should be revised in line with the strengthening of financial inclusion and the accelerating development of financial services. Moreover, policy makers should consider positive and negative aspects of financial inclusion jointly and control financial inclusion policy to avoid financial instability.
Financial Inclusion for Financial Stability: Access to Bank Deposits and Deposit Growth in the Global Financial Crisis. Capital Adequacy Ratio (CAR) Common Equity Tier 1 (CET1) and total regulatory capital must be at least 4.5 and 8 percent of risk-weighted assets, respectively. Net Stable Funding Ratio (NSFR) Banks must maintain a minimum ratio of 10 percent (banks' net stable funding profile in relation to their asset composition).
SAMA Liquidity Ratio (SLR) Bank's liquidity reserves must be at least 15 percent of their deposit liabilities. Liquidity Coverage Ratio (LCR) Maintain high quality liquid assets (HQLA) equal to at least 100 percent of projected net cash outflows over a 30-day stress period. Loan-to-Value (LTV) First Mortgage must be less than or equal to 90 percent of residential real estate value.
Other Mortgages must be less than or equal to 70 percent of residential property value. Debt-to-income ratio The limit varies depending on the level of income and customer's total obligations.
