Equity return volatility in Africa’s stock markets: A dynamic panel approach

Figures & data

Table 1. Descriptive statistics of the returns and drivers of return volatility

Stock Market	Mean	Max.	Min.	Std. Dev.	Skewness	Kurtosis	J-Bera	ARCH(2)	No. of Obs.
Panel A: Descriptive statistics of the returns
South Africa	0.96	12.35	−15.03	4.40	−0.34	3.95	12.45***	28.19***	221
Egypt	1.24	31.19	−40.33	9.31	−0.41	5.5	63.16***	220***	221
Nigeria	0.51	32.41	−36.59	7.31	−0.43	7.15	165.39***	15.12***	221
Kenya	−0.02	14.41	−25.67	5.59	−0.78	5.72	90.47***	10.05***	221
Ghana	0.99	20.11	−32.35	5.89	−0.35	8.57	290.63***	17.43***	221
Morocco	0.60	18.34	−23.38	4.42	−0.61	8.41	283.12***	221***	221
Botswana	0.52	13.84	−10.7	3.09	0.33	7.01	151.10***	16.04***	221
Mauritius	0.67	15.38	−32.63	4.58	−1.97	18.02	2221.15***	221***	221
US (S&P 500)	0.71	11.94	−18.56	4.14	−0.92	5.57	91.77***	38.85***	221
UK (FTSE 100)	0.26	11.65	−14.86	3.79	−0.77	4.79	51.01***	19.18***	221
Panel B: Descriptive statistics of drivers of return volatility
ASMRV	0.057	1.749	0.009	0.052	20.572	645.584	3054.56***		1768
M2	10.472	12.826	7.482	1.425	−0.033	1.534	158.74***		1768
CPI	127.504	377.8	38.96	63.643	1.531	5.218	1053.37***		1768
TBR	8.06	31.15	0.01	5.257	0.955	3.851	321.64***		1768
ER	45.471	420.64	0.873	72.902	2.689	10.895	6722.01***
WOP	67.428	133.93	16.52	22.860	0.418	2.512	69.062***		1768
SP500RV	0.039	0.125	0.019	0.017	2.137	8.673	464.62***		1768
FTSE100RV	0.036	0.1	0.022	0.013	2.192	9.476	563.25***		1768
Cov19	0.099	1.00	0.00	0.300	2.675	8.156	508.378***		1768

Notes: Panel A displays summary statistics for monthly stock returns of African markets and the advanced markets of US and UK. US and UK market returns are based on S&P 500 index and FTSE 100 index respectively. All returns are calculated as 100 times the difference in the log of prices. ARCH (2) denotes the LaGrange multiplier test for autoregressive conditional heteroscedasticity of order 2. Panel B displays summary statistics of the drivers of return volatility. ASMRV = return volatility in ASM’s, M2 = money supply, CPI = consumer price index, TBR = Treasury bill rate, ER = exchange rate, WOP = world crude oil prices, SP500RV = return volatility of US’s Standard and Poor 500 index, FTSE100RV = return volatility of UK’s Financial Times Stock Exchange 100 index, Cov19 = COVID-19 time dummy coded 1 for months when COVID-19 was present in a sampled African country and 0 otherwise. ASMRV, SP500RV and FTSE100RV are monthly conditional variance estimates computed from either Eq. (1), Eq. (2) or Eq. (3) depending on the goodness of fit of the GARCH model. *** = significance at 1%.

Figure 1. Time series of conditional volatilities.

These graphs show conditional volatilities of all 10 stock markets under consideration in this paper.

Figure 1. (Continued).

Table 2. Estimates of GARCH (1,1) models

	South Africa	Egypt	Nigeria	Kenya	Ghana	Morocco	Botswana	Mauritius	US	UK
	SGARCH	EGARCH	GJR-GARCH	EGARCH	EGARCH	EGARCH	EGARCH	GJR-GARCH	EGARCH	EGARCH
Panel A: Conditional Mean
c	0.012^a (0.001)	0.015^b (0.007)	0.005 (0.007)	0.001 (0.005)	0.002 (0.001)	0.006^a (0.000)	0.003^c (0.002)	0.008^a (0.000)	0.005^a (0.002)	0.002 (0.002)
ɷ1		0.863^a (0.068)	−0.212 (0.164)	0.213^b (0.097)	0.315^a (0.055)	−0.122^a (0.000)	0.681^a (0.109)
ɷ2								−0.797^a (0.000)	−1.027^a (0.000)
ɷ3	−0.272^a (0.000)
Ө1						0.287^a (0.000)	−0.292^c (0.169)
Ө2									1.019^a (0.001)
Ө3	0.144^a (0.000)	0.085^b (0.040)	0.158^b (0.065)					0.155^a (0.000)
Panel B: Conditional Variance
ω	0.000^c (0.000)	−0.925^a (0.420)	0.001^a (0.000)	−0.900^b (0.397)	−1.699^b (0.765)	−0.200^a (0.000)	−0.083 (0.091)	0.0000 (0.002)	−0.842^a (0.096)	−1.884^b (0.924)
α	0.289^a (0.099)	−0.145^b (0.064)	0.005 (0.058)	−0.072 (0.070)	0.461^c (0.253)	0.110^a (0.000)	0.057^c (0.031)	0.156^b (0.078)	−0.279^a (0.065)	−0.346^a (0.116)
β	0.575^a (0.122)	0.810^a (0.087)	0.729^a (0.068)	0.826^a (0.846)	0.654^a (0.145)	0.970^a (0.000)	0.988^a (0.012)	0.818^a (0.139)	0.871^a (0.016)	0.720^a (0.137)
γ		0.097 (0.122)	0.195^b (0.098)	0.351^a (0.118)	0.746^b (0.358)	−0.168^a (0.000)	0.252^a (0.094)	0.050 (0.182)	0.282^a (0.085)	0.250^b (0.119)
Persistence	0.865	0.810	0.832	0.826	0.654	0.970	0.988	0.999	0.871	0.720
Log Like.	408.69	230.14	288.89	339.73	358.3	410.43	530.63	468.18	441.248	431.89

^{a, b and c}are significance levels at 1%, 5% and 10%, respectively. c, ɷ1, ɷ2, ɷ3, Ө1, Ө2 and Ө3 denote the constant, AR(1), AR(2) AR(3), MA(1), MA(2), MA(3) terms. ω, α, and β proxy the constant, ARCH and GARCH terms. γ is coefficient of asymmetric effect in EGARCH and GJR-GARCH. Persistence is α+β for the standard GARCH model, β for the EGARCH models and α+β+(γ/2) for the GJR-GARCH models. All models are estimated using symmetric distributions. Log Like is log-likelihood. Standard errors are in parenthesis.

Table 3. Unit root test results

Variables	Level		First Difference
	Intercept	Intercept and Trend	Intercept	Intercept and Trend
Panel A: Panel Unit Root Test - Levin-Lin-Chu Test
ASMRV	−2.5111***	−3.8532***	−32.5088***	−38.2789***
M2	−5.6820 ***	−2.6346***	−28.7680***	−38.0430***
CPI	−2.1780**	−2.3708***	−17.2478***	−19.8433***
TBR	−1.7348	−3.2356***	−3.5123***	−2.5213***
ER	1.1850	−1.7662**	−25.5679***	−28.1040***
WOP	−3.0579***	−3.5271***	−21.2692***	−24.7549***
SP500RV	−6.0723***	−6.6619***	−6.5247***	−6.0955***
FTSE100RV	−13.6389***	−15.8911***	−20.9922***	−23.8242***
Panel B: Time Series Unit Root Test - Augmented Dickey-Fuller Test
WOP	−3.403**	−3.382	−8.813***	−8.830***
SP500RV	−4.290***	−4.281***	−11.479***	−11.452***
FTSE100RV	−5.312***	−5.330***	−12.652***	−12.622***

Notes: Panel A presents results of panel unit root tests for all variables used in panel analysis. Panel B presents results of time series unit root tests for variables with repeated values across all panels. ASMRV = African stock markets return volatility, M2 = money supply, CPI = consumer price index, TBR = Treasury bill rate, ER = exchange rate, WOP = world crude oil prices, SP500RV = return volatility of US’s Standard and Poor 500 index, FTSE100RV = return volatility of UK’s Financial Times Stock Exchange 100 index. All variables are log transformed. *** and ** represent statistical significance at 1% and 5%, respectively.

Table 4. Correlation between predictive variables

	ASMRV	M2	CPI	TBR	ER	WOP	SP500RV	FTSE100RV
ASMRV	1
M2	0.014	1
CPI	0.289***	−0.134***	1
TBR	0.332***	−0.012	0.657***	1
ER	0.025	−0.079***	0.281***	−0.045*	1
WOP	0.014	0.074***	0.017	0.022	−0.059**	1
SP500RV	0.093***	0.016	0.077***	0.012	−0.002	−0.123***	1
FTSE100RV	0.069***	0.027	0.070***	0.013	0.001	−0.042*	0.796***	1

Notes: In this table, ASMRV = African stock markets return volatility, M2 = money supply, CPI = consumer price index, TBR = Treasury bill rate, ER = exchange rate, WOP = world crude oil prices, SP500RV = return volatility of US’s Standard and Poor 500 index, FTSE100RV = return volatility of UK’s Financial Times Stock Exchange 100 index. All variables are log transformed. ***, ** and * represent statistical significance at 1%, 5% and 10%, respectively.

Table 5. LSDVC estimates: drivers of African stock markets return volatility

	Coefficient	Standard Error	P-value
L1.ASMRV	0.8646***	0.0072	0.000
M2	0.0203***	0.0048	0.000
CPI	−0.0023	0.0043	0.589
TBR	−0.0632**	0.0253	0.013
ER	0.1617***	0.0313	0.000
SP500RV	0.0291***	0.0017	0.000
FTSE100RV	0.0329***	0.0027	0.000
WOP	−0.0521***	0.0158	0.001
Cov19	0.039**	0.0186	0.033
Year Dummies		Yes
Observations		1740
Countries		8

Notes: This table presents the results of estimating Eq. 4. ${\mathrm{Y}}_{\mathrm{i}\mathrm{t}}=\phi {\mathrm{Y}}_{\mathrm{i}\mathrm{t}-1}+{\beta \mathrm{X}}_{\mathrm{i}\mathrm{t}}^{\prime }+\left({\mathrm{n}}_{\mathrm{i}}+\hspace{0.17em}{\epsilon }_{\mathrm{i}\mathrm{t}}\right)$. ${\mathrm{X}}_{\mathrm{i}\mathrm{t}}^{\prime }$ = a set of predictive variables including macroeconomic factors (money supply (M2), consumer price index (CPI), Treasury bill rate (TBR) and nominal exchange rates (ER)), global factors (world crude oil prices (WOP), return volatility of U.S.’ S&P 500 index (SP500RV) and return volatility of UK’s FTSE 100 index (FTSE100RV) and COVID-19 time dummy (Cov19); Y_it = African stock markets return volatility (ASMRV); Y_it-1 is one period (month) lag of ASMRV (L1.ASMRV); with $\phi$ and $\beta$ being parameters to estimate. ${\mathrm{n}}_{\mathrm{i}}$ = the unobserved time-invariant market-specific effects, ${\epsilon }_{\mathrm{i}\mathrm{t}}$ = the idiosyncratic error term. Variables are log transformed. ***, ** and * are significance at 1%, 5% and 10% levels, respectively. Bootstrapped standard errors (50 iterations; see Bun and Kiviet (2003) and Bruno (2005b)) in parentheses.

Table 6. Drivers of African stock markets return volatility before, during and after crisis (GFC and Covid-19)

	Before GFC	During GFC	After GFC	During Covid-19
	(2003–2006)	(2007–2009)	(2010–2021)	(2020–2021)
L1.ASMRV	0.8284***	0.8683***	0.8257***	0.7491***
	(0.0032)	(0.0535)	(0.0301)	(0.0049)
M2	−0.2513	−0.0332**	0.4968	0.3687
	(0.1960)	(0.0151)	(0.7488)	(0.4936)
CPI	−0.0086	0.0015	0.0039***	−0.0053
	(0.0264)	(0.0143)	(0.0003)	(0.0100)
TBR	−0.0235	−0.2465***	−0.0605***	−0.0675*
	(0.1971)	(0.0731)	(0.0110)	(0.0395)
ER	−0.0768	0.1654*	0.5475***	0.9474**
	(0.7036)	(0.0924)	(0.1986)	(0.4171)
SP500RV	−0.0499	0.0927***	0.0298***	0.0563
	(0.3763)	(0.0003)	(0.0018)	(0.2734)
FTSE100RV	0.0667	0.1027***	0.0148	0.2288
	(0.4926)	(0.0384)	(0.0785)	(0.1777)
WOP	0.0389	0.0011	−0.0354***	0.0011
	(0.2773)	(0.0026)	(0.0064)	(0.0012)
Year Dummies	Yes	Yes	Yes	Yes
Observations	368	184	1172	184
Countries	8	8	8	8

Notes: This table presents the results of estimating Eq. 4. ${\mathrm{Y}}_{\mathrm{i}\mathrm{t}}=\phi {\mathrm{Y}}_{\mathrm{i}\mathrm{t}-1}+{\beta \mathrm{X}}_{\mathrm{i}\mathrm{t}}^{\prime }+\left({\mathrm{n}}_{\mathrm{i}}+\hspace{0.17em}{\epsilon }_{\mathrm{i}\mathrm{t}}\right)$. ${\mathrm{X}}_{\mathrm{i}\mathrm{t}}^{\prime }$ = a set of predictive variables including macroeconomic factors (money supply (M2), consumer price index (CPI), Treasury bill rate (TBR) and nominal exchange rates (ER)), global factors (world crude oil prices (WOP), return volatility of U.S.’ S&P 500 index (SP500RV) and return volatility of UK’s FTSE 100 index (FTSE100RV) and COVID-19 time dummy (Cov19); Y_it = African stock markets return volatility (ASMRV); Y_it-1 is one period (month) lag of ASMRV (L1.ASMRV); with $\phi$ and $\beta$ being parameters to estimate. ${\mathrm{n}}_{\mathrm{i}}$ = the unobserved time-invariant market-specific effects, ${\epsilon }_{\mathrm{i}\mathrm{t}}$ = the idiosyncratic error term. Variables are log transformed. ***, ** and * are significance at 1%, 5% and 10% levels, respectively. Bootstrapped standard errors (50 iterations; see Bun and Kiviet (2003) and Bruno (2005b)) in parentheses.

Table 7. Drivers of stock return volatility within African regions

	Southern Africa	North Africa	East Africa	West Africa
L1.ASMRV	0.8380***	0.8823***	0.8621***	0.6710***
	(0.0259)	(0.0365)	(0.0306)	(0.0000)
M2	−0.2890***	0.1835***	−0.0730***	0.2259***
	(0.0614)	(0.0179)	(0.0076)	(0.0555)
CPI	−0.0023	0.0011**	0.0078	0.0083***
	(0.0308)	(0.0005)	(0.0064)	(0.0020)
TBR	−0.0615***	−0.0938***	−0.0706***	−0.029**
	(0.0207)	(0.0367)	(0.0261)	(0.0118)
ER	−0.0407**	0.0536***	0.1579***	−0.0143***
	(0.0178)	(0.0165)	(0.0282)	(0.0020)
SP500RV	0.0334***	−0.0197	0.0086***	0.0355***
	(0.0066)	(0.2320)	(0.0030)	(0.0126)
FTSE100RV	0.1586***	−0.0004	0.0159**	−0.0336
	(0.0287)	(0.5729)	(0.0075)	(0.2143)
WOP	0.0233***	−0.0369***	0.0018	−0.0420***
	(0.0045)	(0.0094)	(0.0021)	(0.0099)
Cov19	0.0871***	0.0581**	0.0156*	0.0325
	(0.0254)	(0.0261)	(0.0091)	(0.0489)
Observations	440	424	440	436
Countries	2	2	2	2

Notes: This table presents the results of estimating Eq. 4. ${\mathrm{Y}}_{\mathrm{i}\mathrm{t}}=\phi {\mathrm{Y}}_{\mathrm{i}\mathrm{t}-1}+{\beta \mathrm{X}}_{\mathrm{i}\mathrm{t}}^{\prime }+\left({\mathrm{n}}_{\mathrm{i}}+\hspace{0.17em}{\epsilon }_{\mathrm{i}\mathrm{t}}\right)$. ${\mathrm{X}}_{\mathrm{i}\mathrm{t}}^{\prime }$ = a set of predictive variables including macroeconomic factors (money supply (M2), consumer price index (CPI), Treasury bill rate (TBR) and nominal exchange rates (ER)), global factors (world crude oil prices (WOP), return volatility of U.S.’ S&P 500 index (SP500RV) and return volatility of UK’s FTSE 100 index (FTSE100RV) and COVID-19 time dummy (Cov19); Y_it = African stock markets return volatility (ASMRV); Y_it-1 is one period (month) lag of ASMRV (L1.ASMRV); with $\phi$ and $\beta$ being parameters to estimate. ${\mathrm{n}}_{\mathrm{i}}$ = the unobserved time-invariant market-specific effects, ${\epsilon }_{\mathrm{i}\mathrm{t}}$ = the idiosyncratic error term. Variables are log transformed. ***, ** and * are significance at 1%, 5% and 10% levels, respectively. Bootstrapped standard errors (50 iterations; see Bun and Kiviet (2003) and Bruno (2005b)) in parentheses.

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