Nexus between commodities and banking sector financial soundness: The role of general macroeconomic setting in Ghana

Figures & data

Figure 1. Plots of prices and returns series.

Table 1. Preliminary statistics

	Mean	Std. Dev.	Skewness	Kurtosis	Jarque-Bera
COCOA	0.0026	0.0584	−0.1193	3.9652	6.7966**
GOLD	0.0061	0.0395	0.6051	5.0830	39.8981***
CRUDE	0.0003	0.1015	−0.9493	7.9834	195.5165***
CAR	0.0017	0.0433	0.6711	5.3914	51.7044***
NPL	0.0041	0.0573	0.2974	8.5965	217.7645***
ROA	−0.0001	0.1798	0.3267	13.0049	691.1092***
ROE	−0.0012	0.2111	−0.0578	12.0847	567.5034***
CLATA	−0.0009	0.0528	0.1562	2.9098	0.7267
INF	−0.0004	0.0516	0.9517	10.7340	436.1320***
EXC	0.0111	0.0316	−0.5829	8.0956	187.8575***
GEPU	0.0081	0.1874	0.3354	3.5139	4.9095*

[*], [**], and [***] indicate significance at 10%, 5% and 1% levels respectively.

Figure 2. Comovements between commodities and the macroeconomic variables.

Figure 2. Continued.

Figure 2. Continued.

Figure 3. Wavelet bivariate correlations matrix.

Figure 3. Continued.

Figure 4. Wavelet multiple correlations among commodities, banking sector financial indicators, and general economic indicators. U-upper limits, L- lower (at 95% confidence interval).

Table 2. Wavelet multiple correlations (WMC)

Scale	WMC “lower”	Correlation	WMC “upper”
1	0.8863	0.9254	0.9513
2	0.8500	0.9177	0.9556
3	0.8145	0.9240	0.9699
4	0.8486	0.9635	0.9916

Figure 5. Wavelet multiple cross-correlations among Commodities, Banking sector financial indicators, and general economic indicators.

Table 3. Wavelet multiple cross correlations (WMCC)

Scale	Localizations	Time Lag (months)	Leading/Lagging variable
1	0.9254	0	ROE
2	0.9177	0	ROA
3	0.9240	0	ROA
4	0.9895	−8	EXC

Data Availability Statement

The data used this study are attached as supplementary files.