1 Volatility and risk spillovers between oil, gold, and Islamic and conventional GCC banks Walid Mensi a,b , Shawkat Hammoudeh c,d , Idries Mohammad Wanas Al-Jarrah e , Khamis Hamed Al-Yahyaee b* , Sang Hoon Kang a Department of Finance and Accounting, University of Tunis El Manar, Tunis, Tunisia b Department of Economics and Finance, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman Email: [email protected]Email: [email protected]c Lebow College of Business, Drexel University, Philadelphia, United States d Energy and Sustainable Development (ESD), Montpellier Business School, Montpellier, France Email address: [email protected]e College of Business and Economic, Qatar University, Qatar Email: [email protected]f Department of Business Administration, Pusan National University, Busan 609-735, Republic of Korea Email: [email protected]Abstract This paper examines time-varying risk spillovers and hedging effectiveness between two major commodity markets (oil and gold) and both the Islamic and conventional bank stock indices for five GCC countries (Bahrain, Kuwait, Qatar, Saudi Arabia and UAE), using the DECO- FIGARCH model and the spillover index of Diebold and Yilmaz (2012). The results of the DECO-FIGARCH model show evidence of a weak average conditional correlation between all the GCC bank stock indices and the two commodity markets. Moreover, we find significant risk spillovers between these Islamic and conventional GCC bank stock indices and the commodity markets. The spillovers rise considerably during the 2008-2009 global financial crisis and the 2014-2015 oil price plunge periods Further, oil, gold, and the conventional bank stock index of Saudi Arabia, Kuwait and Qatar are net sources of volatility spillovers into the other markets, while all the Islamic banks and conventional banks of UAE and Bahrain are net volatility recipients of volatility spillovers. Finally, we provide evidence asserting that including gold and oil in a GCC portfolio offers better but different diversification benefits and hedging effectiveness for the GCC banks. Keywords: GCC, Islamic banking, Commodity markets, Risk spillovers, Hedging effectiveness JEL classification codes: G14; G15
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1
Volatility and risk spillovers between oil, gold, and Islamic and
conventional GCC banks
Walid Mensia,b
, Shawkat Hammoudehc,d
, Idries Mohammad Wanas Al-Jarrahe, Khamis
Hamed Al-Yahyaeeb*
, Sang Hoon Kang
a Department of Finance and Accounting, University of Tunis El Manar, Tunis, Tunisia
b Department of Economics and Finance, College of Economics and Political Science, Sultan Qaboos
Notes: SAI, SAC, QAI, QAC, UAI, UAC, BAI, BAC, KUI and KUC are respectively the Saudi Arabia Islamic bank index, Saudi Arabia conventional bank index, UAE Islamic bank index,
UAE conventional bank index, Bahrain Islamic bank index, Bahrain conventional bank index, Kuwait Islamic bank index and Kuwait conventional bank index. J-B denotes the empirical
statistics of the Jarque-Bera test for normality. The Ljung-Box Q(30) and Q2(30)
tests for no autocorrelation of residuals and square residuals, respectively. ADF, PP and KPSS are the
empirical statistics of the Augmented Dickey and Fuller (1979), and the Phillips and Perron (1988) unit root tests and the Kwiatkowski et al. (1992) stationarity test, respectively. The ARCH-
LM(10) test of Engle (1982) checks the presence of ARCH effects. The asterisk *** denotes the rejection of the null hypotheses of normality, no autocorrelation, unit root, stationarity, and
conditional homoscedasticity at the 1% significance level.
21
Table 2. Results of the long memory tests for the returns of oil price, gold price, and the Islamic and conventional GCC banking stock indexes
(0.069) Notes: The critical values of the Hurst-Mandelbrot R/S test and Lo’s modified R/S analysis are 1.862 and 2.098 at the 5% and 1% significance levels, respectively. The numbers in the
parentheses are the standard deviations of the estimates. “ q ” in Lo’s modified R/S test is the number of lags of autocorrelation. m denotes the bandwidth for the GSP and GPH tests. ** and ***
indicate significance at the 5% and 1% levels, respectively.
22
Table 3. Results of the long memory tests for the squared returns of oil price, gold price, and the Islamic and conventional GCC banking stock indexes
Gold WTI SAI SAC QAI QAC UAI UAC BAI BAC KUI KUC
Panel A: Hurst-Mandelbrot R/S test
R/S statistic 3.190***
4.459***
4.566***
2.433***
4.938***
5.019***
4.466***
2.799***
3.422***
4.343***
5.894***
5.189***
Panel B: Lo’s modified R/S test
( 1)q 3.093***
4.038***
4.247***
2.232***
4.542***
4.690***
3.871***
2.515***
3.300***
4.018***
5.250***
4.753***
( 5)q 3.190***
3.011***
3.654***
2.120***
3.617***
3.665***
2.818***
2.265***
2.933***
3.478***
3.937***
3.626***
Panel C: GSP test
4/Tmd 0.234***
(0.021)
0.374***
(0.021)
0.206***
(0.021)
0.100***
(0.021)
0.266***
(0.021)
0.296***
(0.021)
0.373***
(0.021)
0.115***
(0.021)
0.181***
(0.021)
0.201***
(0.021)
0.344***
(0.021)
0.308***
(0.021)
16/Tmd 0.415***
(0.042)
0.417***
(0.042)
0.353***
(0.042)
0.175***
(0.042)
0.438***
(0.042)
0.481***
(0.042)
0.378***
(0.042)
0.196***
(0.042)
0.238***
(0.042)
0.355***
(0.042)
0.625***
(0.042)
0.431***
(0.042)
32/Tmd 0.541***
(0.063)
0.830***
(0.063)
0.268***
(0.063)
0.257***
(0.063)
0.402***
(0.063)
0.390***
(0.063)
0.399***
(0.063)
0.327***
(0.063)
0.282***
(0.063)
0.454***
(0.063)
0.768***
(0.063)
0.558***
(0.063)
64/Tmd 0.555***
(0.084)
0.574***
(0.084)
0.587***
(0.084)
0.275***
(0.084)
0.494***
(0.084)
0.474***
(0.084)
0.074***
(0.084)
0.431***
(0.084)
0.448***
(0.084)
0.413***
(0.084)
0.527***
(0.084)
0.566***
(0.084)
Panel D: GPH test
45.0Tmd 0.583
***
(0.134)
0.576***
(0.134)
0.727***
(0.134)
0.307***
(0.134)
0.496***
(0.134)
0.605***
(0.134)
0.358***
(0.134)
0.501***
(0.134)
0.557***
(0.134)
0.371***
(0.134)
0.493***
(0.134)
0.585***
(0.134)
5.0Tmd 0.607
***
(0.106)
0.828***
(0.106)
0.592***
(0.106)
0.276***
(0.106)
0.425***
(0.106)
0.475***
(0.106)
0.435***
(0.106)
0.417***
(0.106)
0.475***
(0.106)
0.508***
(0.106)
0.689***
(0.106)
0.579***
(0.106)
55.0Tmd 0.603
***
(0.085)
0.871***
(0.085)
0.339***
(0.085)
0.279***
(0.085)
0.423***
(0.085)
0.489***
(0.085)
0.461***
(0.085)
0.374***
(0.085)
0.375***
(0.085)
0.448***
(0.085)
0.845***
(0.085)
0.642***
(0.085)
6.0Tmd 0.549
***
(0.069)
0.529***
(0.069)
0.413***
(0.069)
0.230***
(0.069)
0.506***
(0.069)
0.442***
(0.069)
0.470***
(0.069)
0.256***
(0.069)
0.340***
(0.069)
0.384***
(0.069)
0.828***
(0.069)
0.573***
(0.069)
Notes: See the notes of Table 2.
23
4. Empirical results
4.1. Estimation of marginal model
To avoid the non-synchronous trading effect in the world’s financial markets, we have
followed Forbes and Rigobon (2002) to employ two-day rolling returns based on each
aggregated market index. Furthermore, we have applied the two-day rolling returns to the
multivariate DECO-FIGARCH model for modelling all stylized facts for stock returns such as
volatility clustering, volatility persistence (long memory), and time-variations in conditional
volatility and correlation.
The results of the estimation of the multivariate DECO-FIGARCH (1, d, 1) model
between the commodity (oil and gold) and GCC bank stock (Islamic and conventional)
markets are summarized in Table 4. Panels A, B and C summarize the mean and variance
equations, and the average correlation for each pair and the diagnostic tests, respectively.5
Looking at Panel A, we find that the autoregressive (AR(1)) parameter of the mean equation
is positive and statistically significant at the 1% level for all cases (except for the
conventional bank stock index of UAE). This result indicates that the past returns are
instantaneously and rapidly included in the current returns for these banks. Moreover, the
fractional integrated coefficient (d) is highly significant for all the return series, suggesting a
high level of persistence. Among all series, the Islamic bank stock index for Saudi Arabia
exhibits the highest parameter, while the conventional bank stock index of UAE presents the
lowest long memory parameter. We also note that the Islamic bank stock indices for Saudi
Arabia, Qatar and Kuwait are more persistent than their conventional counterparts.
Panel B shows that the DECOa coefficient is positive and statistically significant at the
conventional level, underlying the importance of shocks between the commodity and both the
5 One should note that the lag order (1, d, 1) is selected by using the Akaike Information Criteria (AIC) and the
Schwarz Information Criteria (SIC). The results are not reported here to honor space but they are available upon
request.
24
Islamic and conventional bank GCC stock index return. The DECOb parameter is significant
and very close to one for all pairs. This result corroborates the results of higher persistence of
volatility across the considered markets as determined by the variance equation, particularly
the GARCH parameter and the d-FIGARCH parameter.
We show that the average correlation is close to zero (0.083) but is statistically
significant at the 1% level. This result exhibits the presence of diversification investment
opportunities between the markets. Additionally, the evidence on the degrees of freedom of
the Student-t distribution (df) indicates that fat-tailedness characterizes the distribution of all
return series. Taking together, the significance of the parameters DECOa , DECOb and df
demonstrates the appropriateness of using the DECO-FIGARCH model with Student-t
distributions. The results of the diagnostic tests using the Ljung–Box tests for serial
correlation in the standardized residuals and the squared standardized residuals results do not
reject the null hypothesis of no serial correlation in all pairs. This finding provides no evidence
of misspecification in our model.
Fig. 3 plots the dynamic equicorrelation for the group of the commodity and GCC
bank markets. From this figure, we can draw several interesting findings. First, we observe
time-varying correlations over the sample period, suggesting that institutional investors do or
should frequently change their portfolio structure. Second, the correlation is positive and
weak for all sample period, with a higher level of correlation during the GFC with a value
equals 0.35. This rise in correlation between the markets decreases the potential of
diversification benefits during crises. Finally, the correlation level increases during the more
recent period 2014–2015, which corresponds to the oil price collapse. This result supports the
hypothesis of financial contagion between the commodity and GCC stock returns under
consideration. Thus, the trajectories of the time-varying conditional correlations show that the
share prices of the Islamic and conventional GCC banks are not immune against international
25
factors and oil price shocks. Following the recent oil price decline, the conditional
correlations exhibit a gradual decrease, indicating a gradual recovery of these markets.
Fig. 2. Dynamic equicorrelation among the commodities and GCC banks
Note: The dynamic equicorrelation between Gold, WTI and the five GCC bank indices is estimated from the
multivariate ARMA-FIGARCH(1,d,1)-DECO model.
26
Table 4. Estimation of the multivariate ARMA-FIGARCH(1,d,1)-DECO model
Saudi Arabia Qatar UAE Bahrain Kuwait
Gold WTI SAI SAC QAI QAC UAI UAC BAI BAC KUI KUC
Panel A: Estimates of ARMA-FIGARCH(1,d,1) model
Const. 0.0304
(0.0264)
0.0365
(0.0426)
0.0243
(0.0290)
-0.0701
(0.0770)
0.0127
(0.0253)
0.0373
(0.0255)
0.0106
(0.0299)
-0.0361
(0.0235)
-0.0274***
(0.0081)
0.0163
(0.0169)
0.0008
(0.0255)
-0.0080
(0.0152)
AR(1) -0.0181
(0.0208)
-0.0175
(0.0244)
0.0849***
(0.0263)
0.1009**
(0.0503)
0.0858***
(0.0303)
0.0819***
(0.0268)
0.0475
(0.0254)
0.0069
(0.0548)
-0.0140***
(0.0293)
-0.0274
(0.0266)
-0.1242***
(0.0247)
-0.0547**
(0.0241)
MA(1) 0.9812***
(0.0032)
0.9773***
(0.0036)
0.9647***
(0.0133)
0.9636***
(0.0158)
0.9478***
(0.0088)
0.9543***
(0.0079)
0.9571***
(0.0055)
0.9685***
(0.0059)
0.9182***
(0.0094)
0.9862***
(0.0025)
0.9800***
(0.0030)
0.9806***
(0.0043)
Const. 1.1145
(0.8869)
1.7433**
(0.7276)
2.0059
(1.5310)
0.1318
(0.0854)
3.7552
(3.7946)
0.0070
(0.0036)
6.6453
(4.3303)
0.4249***
(0.1303)
4.0441
(3.2239)
0.2806**
(0.1188)
0.7906**
(0.3862)
0.3539**
(0.1627)
d-FIGARCH 0.4823***
(0.1324)
0.4971***
(0.0683)
0.7240***
(0.0752)
0.3189**
(0.1623)
0.6628***
(0.0908)
0.5266***
(0.0700)
0.6126***
(0.0609)
0.1859***
(0.0706)
0.7097***
(0.0924)
0.3615***
(0.0894)
0.4542***
(0.0662)
0.4339***
(0.0652)
ARCH 0.2442***
(0.0990)
0.2956***
(0.0771)
0.0072
(0.0894)
0.2331***
(0.2497)
0.2956***
(0.0982)
0.3354***
(0.0683)
0.0546
(0.0915)
0.8563***
(0.0981)
0.3686**
(0.1676)
0.4126***
0.0801)
0.4296***
(0.0761)
0.1818
(0.1076)
GARCH 0.6718***
(0.1265)
0.6844***
(0.0902)
0.6116***
(0.0847)
0.1291***
(0.1818)
0.7155***
(0.0981)
0.7197***
(0.0596)
0.5268***
(0.0836)
0.8851***
(0.0831)
0.7183***
(0.1279)
0.6749***
(0.0868)
0.6735***
(0.0759)
0.4409***
(0.1223)
Panel B: Estimates of the DCC model DECO
t 0.10059***
(0.0098)
DECOa 0.0584***
(0.0155)
DECOb 0.8929***
(0.0327)
df 6.1278***
(0.1884)
AIC 27.86028
SIC
28.02476
Panel C: Diagnostic tests
Q(30) 25.804
[0.6850]
19.099
[0.9378]
38.392
[0.1399]
37.093
[0.1744]
31.792
[0.3772]
20.926
[0.8901]
40.661
[0.0926]
32.991
[0.3229]
18.941
[0.9412]
36.962
[0.1782]
35.309
[0.2315]
28.963
[0.5195]
Q2(30)
19.458
[0.9298]
19.963
[0.9174]
7.0039
[0.9999]
8.4525
[0.9999]
27.581
[0.5925]
11.093
[0.9993]
23.626
[0.6235]
12.126
[0.9984]
0.2375
[1.0000]
20.394
[0.9058]
35.922
[0.2106]
29.802
[0.4758]
Notes: Q(30) and Q2(30) are the Ljung-Box test statistic applied to the standard residuals and the squared standardized residuals, respectively. The P-values are reported in brackets [.]. The
standard error values are reported in parentheses (.). The asterisks ** and *** indicate significance at the 5% and 1% levels, respectively.
27
4.2. Total volatility spillover index and rolling-sample spillover analysis
Table 5 summarizes the estimated results of the total volatility spillover matrix. We
note that the (i, j)th entry in each panel is the estimated contribution to the forecast-error
variance of variable i coming from innovations to market j. The row sums excluding the main
diagonal elements (termed ‘From others’) and the column sums (termed ‘To others’) report
the total spillovers to (received by) and from (transmitted by) each volatility.
The total volatility spillovers value is 29%, indicating diversification gains. Let us first
focus on the directional spillovers transmitted ‘To others’. Gold has a lower impact on the all
Islamic and conventional GCC bank stock indices except for the bank stock index of Saudi
Arabia than oil. In contrast, the WTI crude oil price contributes more significantly to the
conventional banks than the Islamic counterparts for Bahrain, Saudi Arabia and Qatar. The
crude oil acts as the price discovery tool for the conventional bank of these three countries.
Further, the risk spillovers between the GCC bank markets themselves are low because those
banks are isolated according to country lines and heavily involved with the national
governments. For instance, the Islamic Saudi banks contribute 3.8%, 2.3%, 0% and 0.2% to
the forecasting variance of the Islamic banks of Qatar, UAE, Bahrain and Kuwait,
respectively. Moreover, the volatility transmission from the Islamic and conventional GCC
bank to oil and the yellow metal is weak. Taking for example the gold market, the risk
spillover coefficient is close to zero from the Bahrain Islamic and UAE conventional banks to
the shiny metal, and is less than 1% for the spillovers from Saudi Arabia and Qatar. The
Kuwait Islamic (conventional) bank stock index contributes to 0.98% (2.4%) to the
forecasting variance of gold.
28
Table 5. Total volatility spillovers for the commodity and GCC bank.
To (i) From(j)
Gold WTI SAI SAC QAI QAC UAI UAC BAI BAC KUI KUC From others
Notes: The underlying variance decomposition is based on a daily VAR of order 4 (as determined by the Schwarz information criterion) using the generalized VAR spillover index
of Diebold and Yilmaz (2012). The (i,j)th element of the table shows the estimated contribution to the variance of the 10-step-ahead forecast error of i coming from innovation
shocks to variable j. The diagonal elements (i=j) are the own variance share estimates, which show the fraction of the forecast error variance of market i that is due to its own shocks.
The last column “From others” shows the total spillovers received by a particular market from all other markets, while the row “To others” shows the spillover effect directed by a
particular market to all other markets. The lower right corner “Total” indicates the level of total spillovers.
29
Similar results can be seen for the crude oil market. This result demonstrates the role
played by the precious metal market as a refuge asset during the market turmoil, and this
result is in line with previous studies including, among others, Baur and Lucey (2010), Baur
and McDermott (2010), Bredin et al. (2015), Mensi et al. (2015a).
For the graphical evidence, we plot the time-varying volatility spillover index in Fig.
3. A close inspection of this figure, we show that the volatility spillovers attain their
maximum level during 2008–2009 and 2015–2016, which as indicated earlier corresponds to
the GFC and the oil price plunge periods. Moreover, we see the commodity-bank linkages are
highly influenced by the political and economic events as illustrated in Figure 3. The 2007–
2008 commodity crisis, the 2011 Arab spring revolution and the changes of rates by the U.S.
Federal Reserve between 2009-2013 and the Chinese stock markets crash in summer 2015
increase the spillovers between these markets, which reduces investment diversification
opportunities for the considered markets. It is worth noting that in July 2015 the Shanghai
stock market had fallen 30% over three weeks, as more than half of the listed companies filed
for a trading halt in an attempt to prevent further losses. Again, the Shanghai index fell in
August by 8.48%, which is the largest fall since 2007.
30
Fig. 3. The dynamics of the total volatility spillover index
Notes: The dynamics of total volatility spillovers are calculated from the forecast error variance decompositions
of 10-step-ahead forecasts with 200-day rolling windows.
4.3. Net volatility spillover
We deepen our analysis by examining the time-varying behavior of the volatility
spillovers. More specifically, we study the net pairwise volatility spillovers, which provide a
fruitful information about the directional volatility spillovers among the bank-commodity
futures markets. We divide the total volatility spillover index into two directional spillovers: i)
the receivers of volatility spillovers, termed directionally as ‘from’, and ii) the transmitters of
volatility spillovers, termed directionally as ‘to’. The net dynamic volatility spillover index is
then computed by subtracting the directional ‘to’ spillovers from the directional ‘from’
spillovers. The positive (negative) values indicate a source (a recipient) of return and
volatility to (from) others.
Table 6 reports the net directional pairwise index to identify the main net recipients an
d contributors to the volatility spillovers. Regarding the directional spillovers received ‘From
others’, the results exhibit that the WTI oil receives more shocks from the rest of the markets t
31
han gold. Also, the Islamic GCC bank stock indices receive more risk than the conventional b
ank indices for all the GCC markets. In fact, the Qatar Islamic bank stock index receives more
risk from the remaining markets (commodities and the rest of banks) than any of the other Isl
amic bank stock indices, while the Islamic bank stock index of Bahrain receives less risk than
the other markets. The Qatar conventional bank index receives 6.73% and 47.3% of the risk s
pillovers from gold, oil and other Islamic and conventional GCC bank stock indexes.
From this table, we can conclude that both the gold and oil markets are net
contributors of risk. More precisely, gold receives risk spillovers from the Saudi Islamic and
conventional bank index, the UAE conventional bank index, the Bahrain Islamic bank index
and the Kuwait conventional bank index, while gold is a net contributor to the remaining
markets. In fact, gold contributes to the error forecast variance of the conventional bank index
of Qatar a meager of 2.88%, the conventional bank index of the Bahrain 0.04%, and the
Islamic bank index for Kuwait 1.15%. Again, this result is in line with previous works (see
Baur and Lucey, 2010; Mensi et al., 2015b; Mensi et al., 2016) on the ability of gold to be a
good hedge and/or a safe haven asset, not only for the stock markets but also for the bank
markets of the GCC economies.
The WTI crude oil receives risk spillovers from gold (3.26%), the Islamic banks of
Bahrain (0.1%) and the conventional banks of Kuwait (0.6%) On the other hand, oil
contributes to the other markets with risk spillovers ranging from 0.1% for the UAE
conventional banks to 7.21% for the Islamic banks of Kuwait. On the other hand, we find that
the risk spillovers between GCC bank stock markets is weak since these markets are well
capitalized, strongly supervised by their respective central banks and domestically isolated
from other GCC banks. The GCC governments also deposit their oil revenues in those banks
and borrow from them, and thus there is no room for contagion to take place among them.
32
To sum up, we conclude that the gold and oil markets as well as the conventional
banks of Saudi Arabia, Qatar and Kuwait are net-contributors of risk to the other markets. In
contrast, all Islamic banks of the five GCC economies and both the Bahrain and UAE
conventional banks are net-recipients of risk from the other markets. A large percentage of
investors in the Bahrain and UAE stock markets come from Saudi Arabia and the other GCC
markets. The UAE market may reflect the heavy borrowing by Dubai from international
markets.
Fig. 4 depicts the time-varying evolution of the net volatility spillover index for each
market. As shown in this figure, we can identify the source or the recipient of the net
volatility spillovers, despite the net volatility spillover oscillates in either the negative or the
positive direction and that their magnitudes have often changed over time. We observe that
gold and oil are net sources of risk spillovers. This result indicates that gold and oil exposure
poses a systemic risk to both Islamic and conventional banking sector in GCC countries. It is
worth noting that oil has become a net recipient of risk spillovers after 2015 in the light of
shocks from the U.S. shale oil shocks and Saudi Arab’s changing oil policy objectives. That
is, this result is due to the significant plunge in oil prices owing to the increase in the U.S.
shale oil production and the Saudi Arabian policy of maximizing oil market share. Further, we
see that the Islamic banks for all GCC banks as well as the conventional banks (stock indices)
of Bahrain and UAE are net recipients of risk spillovers. This result is due to market openness
to the foreign investors. Thus, these banks become integrated with international markets. The
rest of the conventional GCC banks are sources of risk spillovers. We note that the Islamic
banks of Saudi Arabia are a net recipient of risk spillovers between 2011-2012 which
corresponds to the ESDC. Also, the Islamic bank stock index of UAE is a net recipient of the
risk spillovers between 2015-2016 which again corresponds to the recent drop in oil prices.
33
On the whole, the huge exposure of GCC banks to the oil sector coupled with the low
price of crude oil in the international market continues to cause concerns for GCC central
banks. The Islamic banks are also more risky than conventional banks which can also be a
source of additional concern. This result is due to fact that Islamic banks have a smaller
universe than conventional banks. In addition, Islamic finance restricts hedging against risk
contrary to what conventional model does, thus it is difficult for Islamic banks to manage
stock markets and foreign exchange risks when foreign options and futures are not allowed.
Islamic banks face a legal risk as the legal systems in their countries do not have specific laws
or statutes that support the unique features of Islamic financial instruments. Islamic banks
face concertation risk as 60% of the financing provided by those banks is by the way of
Murabiha (cost plus). This graphical analysis is in line with the results reported in Table 5.
34
Table 6. Net directional pairwise indices spillovers
Gold WTI SAI SAC QAI QAC UAI UAC BAI BAC KUI KUC Net Conclusion
Notes: The net directional pairwise spillovers, obtained as the difference between the contribution from variable i and the contribution from variable j in Table 5. The column “Net” indicates the
total sum of the net directional pairwise spillovers, expressed as a negative value (net-recipient) and a positive value (net-contributor), respectively.
35
36
Fig. 4. Time-varying net volatility spillover indices
Notes: The time-varying net volatility spillover indices are calculated by subtracting the directional ‘to’
spillovers from the directional ‘from’ spillovers. The positive (negative) values of the spillovers indicate that the
variable is a net contributor (recipient) of the spillovers.
4.4. Robustness tests
For robustness, we conduct two statistical tests to examine the sensitivity of our
spillover results. To start, we check the choice of the order of the VAR and compute the
spillover index for orders 2 to 6 and plot the minimum, maximum and the median values in
Fig. 5(a). Next, we plot the spillover index for the forecast horizons ranging from five to ten
days in Fig. 5(b). Both Figs. 5(a) and 5(b) reveal the spillover indices that appear to follow
similar patterns. This finding indicates that the total spillover plot is not sensitive to the choice
of the order of the VAR or the choice of the forecast horizon. Similar alternative values as
robustness tests are also adopted by previous studies in the literature (Diebold and Yilmaz,
2009, 2012, 2014; Chau and Deesomsak, 2014; Antonakais and Kizys, 2015 among others).
37
Fig. 5. Robustness tests
Note: (a) Sensitivity of the index to the VAR lag structure (max, min, and median values of the index for VAR
orders 2–6); (b) Sensitivity of the index to forecast horizon (max, min, and median values over 5- to 10-day
horizons).
5. Portfolio design and hedging strategy analysis
The above empirical results provide evidence of risk spillovers across the commodity
and GCC bank markets under consideration. These results have important implications for
having efficient diversified portfolios and conducting risk management. Practically, building
an optimal portfolio based on risk management and portfolio allocation decisions requires a
preliminary and accurate estimation of the temporal covariance matrix. To manage the
commodity-bank more efficiently, we use the estimated results of the DECO-FIGARCH
model, which allows investors to make optimal portfolio allocation decisions by constructing
dynamic risk-minimizing hedge ratios. Thus, we quantify the optimal portfolio weights and
the hedge ratios for designing optimal hedging strategies.
To minimize risk without reducing expected returns, we consider a portfolio
construction of commodity and bank assets. To do this, we assume an investor is holding a set
of bank assets and wishes to hedge her position against unfavorable effects with commodity
assets. Specifically, we follow Kroner and Ng (1998) to define the portfolio weight of the
holdings of commodity (gold or oil) assets by:
38
B
t
BC
t
C
t
BC
t
B
tC
thhh
hhw
,
,
2, with
11
10
00
C
t
C
t
C
t
C
t
C
t
w
ww
w
w , (20)
where C
th , B
th and BC
th , are the conditional volatility of the commodity markets, the
conditional volatility of the stock market and the conditional covariance between the
commodity and the bank asset at time t, respectively. From the budget constraint, the optimal
weight of the bank asset is equal to (1 )Ctw . For each commodity-stock pair, all information
needed to compute the weight Ctw is obtained from the DECO-FIGARCH model.
Following Kroner and Sultan (1993), we apply the beta hedge approach in order to
minimize the risk of this bank-commodity portfolio. We thus measure how much a long
position (buy) of one dollar in the commodity asset (gold or oil) should be hedged by a short
position (sell) of C
t dollar in the GCC bank shares, that is:
C
t
BC
tC
th
h ,
, (21)
The hedging effectiveness of the constructed portfolios can be assessed by comparing
the realized hedging errors (Ku et al., 2007), which are defined by Eq. (22)
unhedged
hedged
Var
VarHE 1 , (22)
where the variance of the hedge portfolio ( hedgedVar ) is the variance of the returns of the
weighted portfolio of a commodity and a bank stock (PF II), whereas the variance of the
unhedged portfolio (unhedgedVar ) is the variance of the returns of the benchmark portfolio (PF
I). A higher HE ratio implies a greater hedging effectiveness measured in terms of the
portfolio’s variance reduction, which thus implies that the associated investment policy can be
deemed a better hedging strategy.
Table 7 presents the values of the optimal portfolio weights, the hedge ratios and the
39
hedging effectiveness. A glance at the coefficients of the optimal weights for commodities, we
find that the GCC banks include holding more oil than gold. On the other hand, the other result
exhibits that institutional investors should hold on average larger weights of commodity assets
than bank assets. By taking Kuwait, for instance, we observe that the optimal weight is 67.27%
(38.55%) for gold while the rest of the wealth should be invested in the conventional (Islamic)
banks. For the oil, the optimal weights are 86.79% (65.16%) and the remainder of the wealth
is invested in the conventional (Islamic) banks of Kuwait. On the whole, the optimal allocation
for gold in a one-dollar conventional bank portfolio ranges from 22.06% to 67.27%, while the
Islamic bank portfolio varies from 38.55% to 68.97%. For oil, the optimal weights for all bank
markets are above 64% and less than 87% respectively to GCC banks. Among the bank
markets, the UAE Islamic (Kuwait conventional) banks should hold a higher proportion of the
gold (oil) commodity asset.
The average optimal hedge ratios show that all the ratios are weak for gold (less than
15%) or close to zero for oil. Looking first at the gold market, the largest ratio reaches 0.1506
for the SAI-gold pair, meaning that a one-dollar long position in the gold should be shorted by
15.06 cents of the Islamic banks of the Saudi Arabia. The lowest ratio is equal to 0.0401 for the
UAE Islamic bank index, indicating that a one-dollar long position in gold should be shorted
by 4.01%. The hedge ratio results for oil can be explained by the fact that these economies are
oil-dependent. More interestingly, the optimal hedge ratios vary slightly across the GCC
banking markets. This result means that investors should hold more of the yellow metal than
GCC bank stocks to minimize risk for investors with bank stock holdings in that region.
Finally, we can conclude that oil is the cheapest hedge for the Islamic bank index of
Saudi Arabia, whereas the most expensive hedge is for the Islamic bank index of UAE. As for
the yellow metal, the cheapest (most expensive) hedge is for the Islamic bank index of UAE
(the Islamic bank index of Saudi Arabia. Note that for oil (gold), the hedge ratio is higher for
40
Islamic banks than their conventional counterparts for all cases, except for Saudi Arabia (UAE).
The time-varying optimal hedge ratios from the estimates of the DECO-FIGARCH
model for the bank-gold pairs are plotted in Figure 6.6 The graphical evidence is in line with
the results reported in Table 7. This plot exhibits a higher variability of the estimated hedge
ratio for the bank stock markets of UAE and Saudi Arabia which demonstrate significant
changes during 2008 and 2014, which is due to the shocks of the onset of the global financial
crisis as well as the recent oil price plunge. The remaining pairs show similar patterns but with
different magnitudes, and for this reason, we will not interpret them. During oil price shocks
and GFC, investors tend to hold more long positions in god and short positions in GCC bank
shares.
Finally, we analyze the hedging effectiveness by actually running portfolio simulations
with our optimal portfolio designs and hedging ratios. More concretely, we build two
portfolios: a portfolio which is composed of only bank stock indexes (PF I) and a weighted
portfolio contains a precious metal (or oil) and a bank stock index with the optimal portfolio
weights calculated above (PF II). The results in Table 7 show that hedging strategies
involving the GCC bank stock and commodity markets make it possible to reduce portfolio
risks. It is worth noting that the hedging effectiveness value for all pairs is positive and high,
suggesting that a significant risk reduction can be realized and that the hedged portfolio is
able to decrease the risk exposure. For the UAE conventional (Islamic) banks, the variance
reduction ranges from 99.11% (99.6%) for gold to 79.7% (93.51%) for oil. More interestingly,
gold offers the best hedging effectiveness for UAE, Qatar, Bahrain and Saudi Arabia while oil
provides the highest hedging effectiveness for Bahrain followed by Qatar, Kuwait and Saudi
Arabia. In sum, gold (oil) provides the best hedging for the conventional (Islamic) banks of
Bahrain.
6 The plots of the dynamic hedge ratios for the bank-oil portfolio are available upon request.
41
Table 7. Optimal portfolios’ weights, hedge ratios and hedging effectiveness
Portfolio C
tw C
t HE (%)
SAI/ GOLD 0.3927 0.1506 97.38
SAC/ GOLD 0.3886 0.1487 99.36
QAI/ GOLD 0.4545 0.1377 98.87
QAC/ GOLD 0.2206 0.1194 96.34
UAI/ GOLD 0.6897 0.0401 99.11
UAC/ GOLD 0.5390 0.1008 99.60
BAI/ GOLD 0.4602 0.1173 97.44
BAC/ GOLD 0.6642 0.0769 99.62
KUI/ GOLD 0.3855 0.1471 90.43
KUC/ GOLD 0.6727 0.0802 97.79
SAI/ WTI 0.8554 0.0211 86.98
SAC/ WTI 0.6443 0.0822 98.98
QAI/ WTI 0.7007 0.0748 99.03
QAC/ WTI 0.7382 0.0659 99.41
UAI/ WTI 0.6418 0.0862 79.70
UAC/ WTI 0.7602 0.0578 93.51
BAI/ WTI 0.7016 0.0669 99.39
BAC/ WTI 0.8548 0.0430 99.42
KUI/ WTI 0.6516 0.0790 98.49
KUC/ WTI 0.8679 0.0431 96.75
Notes: The numbers in bold identify the hedged portfolio which has the highest variance reductions. PFI is a
portfolio of 100% GCC bank stocks, while PFII is the weighted stock and precious metal portfolio in which the
weights are given by the optimal weights.
42
Fig. 6. Time-varying hedging ratios between the GCC bank stock indices and gold.
43
6. Conclusion
Risk spillovers are of great importance for institutional investors, particularly
conventional and Islamic banks in the GCC region, because of high geopolitical risks and
dependency on the highly volatile oil prices. However, counterparty credit risk management
and financial stability requires monitoring and quantifying the risk spillovers of these
financial institutions.
This paper’s objective is to explore the risk spillovers and examine hedging
effectiveness between the two major commodity markets (gold and crude oil), and the Islamic
and conventional banks for five GCC countries ((Bahrain, Kuwait, Qatar, Saudi Arabia and
UAE). For this purpose, we use the dynamic equicorrelation FIGARCH model (DECO-
FIGARCH) and the risk spillovers index developed by Diebold and Yilmaz (2012).
The results show that the average conditional correlations between the commodity and
GCC bank markets are weak due to the fact that most GCC banks are segmented from each
other and from the major international banks, suggesting an ample room for diversification
opportunities for international investors. They also show that gold has a lower impact on the
Islamic and conventional GCC bank than on the oil market. In fact, oil prices increase the risk
spillovers to the conventional GCC banks more than to their Islamic counterparts as measured
by the bank stock index for Bahrain, Saudi Arabia and Qatar. On the other hand, the volatility
spillovers from the Islamic and conventional GCC banks to the commodity markets are weak.
Oil and gold are strategic and global commodities, while most of the GCC banks are
segmented from major international financial markets. Furthermore, oil, gold, and the
conventional banks of Saudi Arabia, Kuwait and Qatar are a net source of volatility spillovers
to the remainder of the markets. In contrast, all the Islamic banks and the conventional banks
of UAE and Bahrain are a net receipt of volatility spillovers. A good portions of investors in
the stock markets of those countries come from Saudi Arabia.
44
By having a close inspection of the coefficients of the optimal weights for the two
commodities, the result reveals that the GCC bank stock indices should hold more oil than
gold. The average optimal hedge ratios show that all the ratios are weak for gold and close to
zero for oil. Finally, gold offers the best hedging effectiveness for UAE, Qatar and Saudi
Arabia which are major oil exporters, while oil provides the highest hedging effectiveness for
Bahrain which is a minor oil producer.
These results have several important insights for policy makers. Given that both
Islamic and conventional GCC banks are less exposed to the risk of gold, banks in oil-
exporting countries should seek diversification benefits in holding gold. For Bahrain, this
country is a minor oil producer and can find diversification benefits in holding oil. Having
more investors from Saudi Arabia and other GCC countries, buying stocks in Bahrain, Dubai
and Abu Dhabi makes the latter countries net receivers for volatility spillover from the former
countries. In this case investors in Bahrain, Dubai and Abu Dhabi should make sure they
include diversifiers from outside the GCC region. Investors dealing with Islamic GCC bank
stocks should be cognizant they need more hedges and safe havens than those who invest in
conventional GCC bank stocks. Islamic GCC banks should also hold collaterals as
conventional GCC banks do to safeguard depositors’ base. Islamic banks should have a higher
risk-weighted capital asset requirements than conventional banks because they get involved in
more risk activities. The Islamic GCC banks depend on more real estate investments than
conventional GCC banks. Moreover, the GCC governments deposit their oil revenues and
borrow money from the conventional banks than from the Islamic banks. Finally, GCC
central banks do not have an elaborate supervision of Islamic banks as they do for the
conventional banks.
45
References
Aielli, G. P. 2013. Dynamic conditional correlation: on properties and estimation. Journal of Business
and Economic Statistics 31, 282–299.
Antonakakis, N., Kizys, R., 2015. Dynamic spillovers between commodity and currency markets.
International Review of Financial Analysis 41, 303–319.