# The Determinants of Capital Adequacy in the Jordanian Banking Sector: An Autoregressive Distributed Lag-Bound Testing Approach

## Abstract

**:**

## 1. Introduction

## 2. Literature Review and Empirical Evidence

## 3. Data and Methodology

## 4. Empirical Results and Discussion

#### 4.1. Unit Root Test

#### 4.2. Lag Selection Criteria

#### 4.3. The ARDL Model

^{2}coefficient of 0.853693, which is above 0.60 and implies the goodness-of-fit on Model 1. In addition, having an F-value of 10.693, with its corresponding p-value of 0.000, which is below 0.05, implies the significance of Model 1. The t-statistic value of above or equal to 2 and the probability value of below or equal to 0.05 imply the significance of the regressors in explaining the CAR. As the ARDL is a short-run model, the results here are related to short-run causalities. Table 4 reveals CTD, LEVRR, and LIQR as statistically significant at 0.05 level, indicating that there is a short-run causality relationship running from these variables into CAR. Therefore, banks’ credit-to-deposits ratio, banks’ leverage ratio, and banks’ liquidity ratio are major determinants of the CAR in the short run. Inflation, however, is observed to be less important in explaining the CAR in the short run, as revealed by its p-value of 0.5061, which is greater than 0.05. For Model 2, the table reveals that ROE(−1) is the only significant factor in explaining the CAR in the short run, as proved by its p-value of 0.032, which is below 0.05. The remaining variables of Model 2 are shown to be insignificant in explaining the CAR in the short run. Model 3, conversely, denotes the significance of CTA, CTA(−1), LATD, and COVR in explaining the CAR in the short run. This finding implies that the capital-to-assets ratio, one-year lagged capital-to-asset ratio, liquid assets-to-deposits ratio, and coverage ratio have short-run causality relationships with the CAR. Model 1 demonstrates goodness-of-fit and statistical significance, as revealed by the R

^{2}value of 0.854, and the corresponding f-statistics probability of 0.000. Similarly, Model 2 exhibits statistical significance and goodness-of-fit, as seen by its R

^{2}value of 0.676, and the f-statistics probability of 0.0261. both demonstrate goodness-of-fit and statistical significance, as revealed by their R

^{2}values of 0.854 and 0.676, and their f-statistic probabilities of 0.000 and 0.0261 respectively.

#### 4.4. ARDL Cointegration Bound Test

^{−5}) and (0.177), respectively. Similarly, these two variables suffer from statistically insignificant associations with CAR. This is proven by their p-values that are greater than 0.05. for Model 2, EC = CAR − (−0.992 × NIM − 0.892 × OHC + 0.000 × M2 + 0.177 × ROE).

^{2}coefficient value of 0.880, the F-statistic coefficient of 63.472, and the corresponding probability value of (0.000) imply the goodness-of-fit of Model 1.

^{2}coefficient value of 0.735, the F-statistic coefficient of 24.592, and the corresponding probability value of (0.000) imply the goodness-of-fit of Model 2. Likewise, a negative coefficient of (−1.422) and a corresponding statistically significant probability value of (0.000) of ECM imply a short-run association between the variables of Model 3. The adjusted R

^{2}coefficient value of 0.935, the F-statistic coefficient of 82.271, and its corresponding probability value of (0.000) imply the goodness-of-fit of Model 3.

#### 4.5. Diagnostics Tests

#### 4.5.1. Serial Correlation

#### 4.5.2. Heteroscedasticity

#### 4.5.3. Heteroskedasticity Test: ARCH

#### 4.5.4. Normality Test

#### 4.5.5. Stability Diagnostics

^{2}coefficient value of 0.900 and F-statistic coefficient of 12.875 and its corresponding probability value of (0.000) indicate the goodness-of-fit of Model 1. Likewise, the R

^{2}coefficient value of 0.701, the F-statistic coefficient of 3.354, and its corresponding p-value of (0.041), indicate the goodness-of-fit of Model 2. Similarly, the R

^{2}value of 0.926, the F-statistic coefficient of 14.011, and the corresponding probability value of (0.000) indicate the goodness-of-fit of Model 3.

## 5. Conclusions and Policy Implications

## 6. Scope and Limitation of the Study

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Variable | Description/Definition |
---|---|

Capital Adequacy Ratio (CAR) | The CAR is a measure used to assess the financial stability and safety of a bank. It is calculated as the ratio of a bank’s capital to its risk-weighted assets. |

Coverage Ratio (COVR) | The COVR is calculated by dividing a bank’s high-quality liquid assets by its total net cash outflows over 30 days. |

Capital-to-Assets Ratio (CTA) | The CTA is calculated by dividing the total capital by the total assets. |

Bank Credit-to-Deposits Ratio (CTD) | CTD is used for measuring a bank’s liquidity by dividing the bank’s total loans disbursed by the total deposits received. |

Gross Domestic Products Growth (GDPG) | GDGP is calculated by dividing the change in GDP by the initial GDP. |

Inflation Rate (INFL) | The INFL is a measure of the average change in prices over time that consumers pay for a basket of goods and services. |

Liquid-Assets-to-Deposits Ratio (LATD) | The LATD indicates the extent to which banks have liquidity on hand, funded by relatively stable and predictable deposits. |

Leverage Ratio (LEVRR) | The LEVRR is a measure of the amount of debt that a bank has compared to its capital. |

Liquidity ratio (LIQR) | The LIQR is a measure of the ability to meet short-term financial obligations. |

Money Supply (M2) | M2 is a measure of the total amount of currency and near money in a country’s economy. |

Net Interest Margin Ratio (NIM) | NIM is a measure of the difference between the interest income generated by banks or other financial institutions and the amount of interest paid out to their lenders. |

Overhead Cost Ratio (OHC) | The OHC is calculated by dividing the operating expenses by (operating income + taxable bet interest income). |

At Level | At First Difference | ||||
---|---|---|---|---|---|

Variable | Intercept | Trend and Intercept | Intercept | Trend and Intercept | Order of Integration |

CAR | 0.140 | 0.085 | 0.016 * | 0.042 * | I(1) |

COVR | 0.237 | 0.043 * | 0.057 | 0.224 | I(0) |

CTA | 0.001 * | 0.087 | 0.021 * | 0.012 * | I(1) |

CTD | 0.403 | 0.477 | 0.000 * | 0.000 * | I(1) |

GDPG | 0.595 | 0.049 * | 0.001 * | 0.008 * | I(1) |

INFL | 0.009 | 0.007 * | 0.000 * | 0.000 * | I(1) |

LATD | 0.247 | 0.576 | 0.021 * | 0.076 | I(1) |

LEVRR | 0.002 * | 0.099 | 0.191 | 0.251 | I(0) |

LIQR | 0.215 | 0.126 | 0.009 * | 0.028 * | I(1) |

M2 | 0.871 | 0.405 | 0.073 | 0.008 * | I(1) |

NIM | 0.033 * | 0.509 | 0.157 | 0.180 | I(0) |

OHC | 0.294 | 0.335 | 0.022 * | 0.086 | I(1) |

ROE | 0.309 | 0.123 | 0.005 * | 0.029 * | I(1) |

Lag | LogL | LR | FPE | AIC | SC | HQ | |
---|---|---|---|---|---|---|---|

Model 1 | 0 | −206.422 | NA | 10,965.37 | 23.491 | 23.739 | 23.525 |

1 | −165.199 | 54.963 * | 2062.753 * | 21.689 * | 23.173 * | 21.893 * | |

Model 2 | 0 | −240.825 | NA | 501,376.2 | 27.314 | 27.561 | 27.348 |

1 | −159.776 | 108.066 * | 1129.165 * | 21.086 * | 22.570 * | 21.291 * | |

Model 3 | 0 | −190.453 | NA | 1859.679 | 21.717 | 21.964 | 21.751 |

1 | −121.990 | 91.283 * | 16.95945 * | 16.888 * | 18.372 * | 17.092 * |

Variable | Coefficient | Std. Error | t-Statistic | Prob. * | |
---|---|---|---|---|---|

Model 1 | CAR(−1) | −0.056 | 0.158 | −0.353 | 0.731 |

CTD | 0.081 | 0.035 | 2.273 | 0.044 | |

INFL | −0.033 | 0.049 | −0.687 | 0.506 | |

LEVRR | 1.226 | 0.212 | 5.769 | 0.000 | |

LIQR | 0.059 | 0.021 | 2.765 | 0.018 | |

LIQR(−1) | 0.042 | 0.022 | 1.915 | 0.082 | |

C | −16.641 | 6.526 | −2.550 | 0.027 | |

R-squared | 0.854 | ||||

Prob(F-statistic) | 0.000 | ||||

Model 2 | CAR(−1) | 0.229 | 0.185 | 1.237 | 0.242 |

NIM | −0.764 | 1.430 | −0.535 | 0.603 | |

OHC | −0.687 | 1.460 | −0.471 | 0.647 | |

M2 | 7.85 × 10^{−6} | 8.45 × 10^{−5} | 0.093 | 0.928 | |

ROE | −0.185 | 0.101 | −1.823 | 0.096 | |

ROE(−1) | 0.3213 | 0.131 | 2.453 | 0.032 | |

C | 16.590 | 4.371 | 3.795 | 0.003 | |

R-squared | 0.676 | ||||

Prob(F-statistic) | 0.0261 | ||||

Model 3 | CAR(−1) | −0.422 | 0.284 | −1.485 | 0.168 |

CTA | 1.116 | 0.275 | 4.057 | 0.002 | |

CTA(−1) | 0.849 | 0.333 | 2.554 | 0.029 | |

GDPG | 0.059 | 0.127 | 0.463 | 0.653 | |

LATD | 0.316 | 0.049 | 6.528 | 0.000 | |

LATD(−1) | 0.173 | 0.139 | 1.246 | 0.241 | |

COVR | −0.066 | 0.0187 | −3.508 | 0.006 | |

C | −11.445 | 3.982 | −2.874 | 0.017 | |

R-squared | 0.927 | ||||

Prob(F-statistic) | 0.000 |

Dependent Variable: D(CAR) | |||||
---|---|---|---|---|---|

The Variable | The Coefficient | The Standard Error | The T-Statistic | The Prob. | |

Model 1 | CTD | 0.077 | 0.037 | 2.045 | 0.066 |

INFL | −0.032 | 0.048 | −0.667 | 0.519 | |

LEVRR | 1.161 | 0.180 | 6.439 | 0.000 * | |

LIQR | 0.096 | 0.019 | 4.941 | 0.000 * | |

EC = CAR − (0.076 × CTD − 0.032 × INFL + 1.161 × LEVRR + 0.096 × LIQR) | |||||

Model 2 | NIM | −0.992 | 1.976 | −0.502 | 0.626 |

OHC | −0.892 | 1.851 | −0.482 | 0.640 | |

M2 | 1.02 × 10^{−5} | 0.000 | 0.092 | 0.928 | |

ROE | 0.177 | 0.220 | 0.805 | 0.438 | |

EC = CAR − (−0.992 × NIM − 0.892 × OHC + 0.000 × M2 + 0.177 × ROE) | |||||

Model 3 | CTA | 1.382 | 0.110 | 12.569 | 0.000 * |

GDPG | 0.041 | 0.095 | 0.435 | 0.673 | |

LATD | 0.344 | 0.032 | 10.655 | 0.000 * | |

COVR | −0.046 | 0.008 | −5.756 | 0.000 * | |

EC = CAR − (1.382 × CTA + 0.041 × GDPG + 0.344 × LATD − 0.046 × COVR) |

Test Statistic | Value | Significance | I(0) | I(1) | |
---|---|---|---|---|---|

Model (1) | F stat. | 17.08260 | 10% | 2.45 | 3.52 |

k | 4 | 5% | 2.86 | 4.01 | |

2.5% | 3.25 | 4.49 | |||

1% | 3.74 | 5.06 | |||

Model (2) | F-stat. | 6.537633 | 10% | 2.45 | 3.52 |

k | 4 | 5% | 2.86 | 4.01 | |

2.5% | 3.25 | 4.49 | |||

1% | 3.74 | 5.06 | |||

Model (3) | F-stat. | 12.72002 | 10% | 2.45 | 3.52 |

k | 4 | 5% | 2.86 | 4.01 | |

2.5% | 3.25 | 4.49 | |||

1% | 3.74 | 5.06 |

Unrestricted Constant and No Trend | |||||
---|---|---|---|---|---|

Variable | Coefficient | Std. Error | t-Statistic | Prob. | |

Model 1 | C | −16.641 | 1.566 | −10.626 | 0.000 |

D(LIQR) | 0.059 | 0.014 | 4.086 | 0.002 | |

Cointegration Eq(−1) × | −1.056 | 0.098 | −10.792 | 0.000 | |

Adjusted R^{2} | 0.880 | ||||

F-stat. | 63.471 | ||||

Prob. of (F-stat.) | 0.000 | ||||

Model 2 | C | 16.590 | 2.474 | 6.704 | 0.000 |

D(ROE) | −0.185 | 0.057 | −3.232 | 0.008 | |

Cointegration Eq(−1) * | −0.771 | 0.115 | −6.676 | 0.000 | |

Adjusted R^{2} | 0.735 | ||||

F-stat. | 24.591 | ||||

Prob. of (F-stat.) | 0.000 | ||||

Model 3 | C | −11.4450 | 1.233 | −9.281 | 0.000 |

D(CTA) | 1.116 | 0.124 | 8.998 | 0.000 | |

D(LATD) | 0.316 | 0.028 | 11.271 | 0.000 | |

Cointegration Eq(−1) * | −1.422 | 0.1501 | −9.436 | 0.000 | |

Adjusted R^{2} | 0.935 | ||||

F-stat. | 82.271 | ||||

Prob. of (F-stat.) | 0.000 |

Model | Null Hypothesis: | F-Statistic | Prob. |
---|---|---|---|

Model 1 | CTD does not Granger cause CAR | 1.279 | 0.276 |

INFL does not Granger cause CAR | 0.877 | 0.364 | |

LEVRR does not Granger cause CAR | 0.500 | 0.490 | |

LIQR does not Granger cause CAR | 2.518 | 0.133 | |

Model 2 | NIM does not Granger cause CAR | 0.753 | 0.399 |

OHC does not Granger cause CAR | 0.146 | 0.708 | |

M2 does not Granger cause CAR | 3.443 | 0.083 * | |

ROE does not Granger cause CAR | 10.970 | 0.004 ** | |

Model 3 | CTA does not Granger cause CAR | 1.262 | 0.279 |

GDPG does not Granger cause CAR | 3.699 | 0.074 * | |

LATD does not Granger cause CAR | 1.821 | 0.197 | |

COVR does not Granger cause CAR | 0.001 | 0.977 |

Model 1 | F-statistic | 0.890 | Prob. F(1,10) | 0.368 |

Obs*R^{2} | 1.472 | Prob. | 0.2251 | |

Model 2 | F-statistic | 0.974 | Prob. | 0.347 |

Obs*R^{2} | 1.598 | Prob. | 0.206 | |

Model 3 | F-statistic | 0.297 | Prob. | 0.599 |

Obs*R^{2} | 0.575 | Prob. | 0.448 |

Model 1 | F-statistic | 0.533 | Prob. | 0.773 |

Obs*R^{2} | 4.057 | Prob. Chi-Square | 0.669 | |

Scaled explained SS | 0.653 | Prob. Chi-Square | 0.995 | |

Model 2 | F-statistic | 0.311 | Prob. | 0.918 |

Obs*R^{2} | 2.607 | Prob. Chi-Square | 0.856 | |

Scaled explained SS | 0.977 | Prob. Chi-Square | 0.987 | |

Model 3 | F-statistic | 0.532 | Prob. | 0.792 |

Obs*R^{2} | 4.886 | Prob. Chi-Square | 0.674 | |

Scaled explained SS | 1.271 | Prob. Chi-Square | 0.989 |

Model (1) | F-statistic | 0.103 | Prob. | 0.753 |

Obs*R^{2} | 0.116 | Prob. | 0.734 | |

Model (2) | F-statistic | 0.564 | Prob. | 0.464 |

Obs* R^{2} | 0.616 | Prob | 0.432 | |

Model (3) | F-statistic | 0.052 | Prob. | 0.823 |

Obs* R^{2} | 0.059 | Prob. | 0.809 |

Model 1 | Model 2 | Model 3 | |
---|---|---|---|

Mean | −7.71 × 10^{−15} | 7.96 × 10^{−16} | −6.13 × 10^{15} |

Median | −0.008 | 0.127 | 0.082 |

Maximum | 0.574 | 1.282 | 0.393 |

Minimum | −0.774 | −1.444 | −0.727 |

Std. dev | 0.443 | 0.659 | 0.316 |

Skewness | −0.334 | −0.363 | −0.818 |

Kurtosis | 1.863 | 3.008 | 2.686 |

Jarque–Bera | 1.304 | 0.394 | 2.079 |

Probability | 0.521 | 0.821 | 0.354 |

Value | df | Probability | ||
---|---|---|---|---|

Model 1 | t-value | 2.157 | 10 | 0.056 |

F-value | 4.654 | (1, 10) | 0.056 | |

Specification: CAR CAR(−1) CTD INFL LEVRR LIQR LIQR(−1) C | ||||

R^{2} | 0.900 | |||

F-statistic | 12.875 | |||

Prob(F-statistic) | 0.000 | |||

Model 2 | t-value | 0.915 | 10 | 0.382 |

F-value | 0.837 | (1, 10) | 0.382 | |

Specification: CAR CAR(−1) NIM OHC M2 ROE ROE(−1) C | ||||

R^{2} | 0.701 | |||

F-statistic | 3.354 | |||

Prob(F-statistic) | 0.041 | |||

Model 3 | t-value | 0.049 | 9 | 0.962 |

F-value | 0.002 | (1, 9) | 0.962 | |

Specification: CAR CAR(−1) CTA CTA(−1) GDPG LATD LATD(−1) COVR C | ||||

R^{2} | 0.926 | |||

F-statistic | 14.01 | |||

Prob(F-statistic) | 0.000 |

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## Share and Cite

**MDPI and ACS Style**

Gharaibeh, A.M.O.
The Determinants of Capital Adequacy in the Jordanian Banking Sector: An Autoregressive Distributed Lag-Bound Testing Approach. *Int. J. Financial Stud.* **2023**, *11*, 75.
https://doi.org/10.3390/ijfs11020075

**AMA Style**

Gharaibeh AMO.
The Determinants of Capital Adequacy in the Jordanian Banking Sector: An Autoregressive Distributed Lag-Bound Testing Approach. *International Journal of Financial Studies*. 2023; 11(2):75.
https://doi.org/10.3390/ijfs11020075

**Chicago/Turabian Style**

Gharaibeh, Ahmad Mohammad Obeid.
2023. "The Determinants of Capital Adequacy in the Jordanian Banking Sector: An Autoregressive Distributed Lag-Bound Testing Approach" *International Journal of Financial Studies* 11, no. 2: 75.
https://doi.org/10.3390/ijfs11020075