Measuring Volatility Persistence for Conventional and Islamic Banks: An FI-EGARCH Approach Fredj JAWADI1,
[email protected] Nejib HACHICHA,
[email protected] Mohamed FAKHFEKH,
[email protected] Nadhem SELMI,
[email protected] Abdoulkarim IDI CHEFFOU,
[email protected]
Abstract This paper studies the volatility dynamics of conventional and Islamic banks from the Gulf Cooperation Council (G.C.C) countries during calm and crisis periods, providing a dual comparison in time and space. In particular, it proposes an empirical measure of volatility persistence using the FIEGARCH (Fractionally Integrated Exponential Generalized Auto-Regressive Conditional Heteroscedasticity) model. This specification is useful for reproducing further asymmetry in volatility dynamics and provides a direct measure of long-term volatility dependence. Our findings point to three interesting findings. First, volatility exhibits asymmetry as bad news has a significantly higher impact on volatility than positive news. Second, bad news affects the volatility of conventional banks more strongly than that of Islamic banks. Third, it seems that following a shock, volatility is more persistent in conventional banks than in Islamic Banks. Accordingly, Islamic banks are more resilient than conventional banks, but the degree of resilience is somewhat heterogeneous and sample dependent. Thus, while this may appear to suggest that we could regulate the conventional bank system using the industry rules of Islamic banks, it is worth noting that Islamic banks in Saudi Arabia tend to provide the most resilient Islamic Bank benchmark model. Key words: Islamic and conventional banks, volatility, persistence, asymmetry, FIEGARCH models. JEL Classification: C20, G10, G21.
1
Corresponding author: University of Evry, 2 Facteur Cheval, 91000, Evry, France. Email:
[email protected] 1
1. Introduction Volatility is the degree of price variability over a certain period, and is often considered as a proxy for financial risk. It is typically measured by the standard deviation of stock returns, but other dynamic measures have also been used in the literature. These measures enable volatility to be time-varying, while taking the hypothesis of conditional heteroscedasticity (Engle, 1982) into account in order to appropriately capture volatility changes. 2 Assessing the dynamics of volatility changes is a key challenge for financial markets as it is crucial to investment decisions, pricing, banking security valuation, risk management and monetary policy. Furthermore, the recent global financial crisis of 2008-2009 that precipitated the world of finance into considerable turmoil resulted in significant volatility excess and change for stocks, bonds, currencies, commodities and derivatives (Shiller, 2008). This makes the volatility modeling issue highly relevant. Furthermore, since 2007, excess volatility appears to be durable, and volatility persistence needs to be investigated to evaluate the propagation of shocks and the subsequent crisis and economic downturn that has affected all financial systems. With foolhardy credit risk-taking and banking policies acknowledged as the major sources of banking volatility and crises, banking services and products have been severely criticized by numerous economists and analysts in recent years. The latter have also pointed to the need to overhaul and regulate finance and banking in order to better control credit, risk management and trading, reduce volatility, and provide a more ethical and moral banking and financial framework. In such a context, Islamic banks could appear as a promising alternative to conventional banks since the way they function is naturally less volatile and features only moderate risk. Indeed, Islamic banks - which are based on Sharia or Islamic Law - prohibit speculation, uncertainty and any form of interest rate on credit (riba) that is assimilated with illicit usury practice. They also advocate sharing profits and losses with investors, and only invest in activity sectors that comply with the Sharia. Accordingly, Islamic banking investments are a priori more likely to reduce financial risk and excess volatility, and to provide a more stable financial industry. However, at least three factors may be a source of risk and foster excess volatility for Islamic banks. First, prohibiting investment in sectors that are not compliant with the Sharia board (i.e. alcohol, pork, etc.) could be seen as source of risk since it means fewer 2
See Scott (1991) for a literature review of financial market volatility and its different measures. 2
diversification opportunities for Islamic banks compared to conventional banks. Second, the prohibition of interest rates reduces financial resources, which means additional constraints and restrictions that may increase the possibility of bankruptcy and collapse for Islamic banks. Third, the increased number of passive and active classes of financial instruments for Islamic banks3 can be a source of price volatility, especially if both parties (investor and banker) interact in an asymmetrical framework with less transparency. Therefore, it is far from certain that financial risk, risk management and volatility are better controlled by Islamic banks than by conventional ones. In addition, Islamic banks may not have escaped the recent financial downturn entirely (Jawadi et al., 2014). To explore these points, this paper models the volatility dynamics for conventional and Islamic banks. We also provide a direct measure of volatility persistence in order to assess whether Islamic banks are more resilient than conventional banks or not. The literature on Islamic finance has grown significantly following the recent financial downturn since there is a widespread view that bank losses and financial risk could be avoided or reduced by adopting Islamic finance rules. Thus, Kantakji (2008) suggests that Islamic finance could provide a robust solution to the current financial crisis. The report by Jouini and Pastré (2008) clearly shows Islamic finance as an interesting and promising option for French conventional finance as it could add 100 billion Euros to the Paris Stock Exchange. Causse (2009, 2010) also assimilates Islamic finance with a risk-free refuge for investors. The investigation of Islamic and conventional price dynamics over the period 1996-2009 by Charles et al. (2011) does not reject the equality hypothesis of their variances however. More recently, Jawadi (2012) tested the impact of the financial crisis on three international capital markets (France, the USA and the World) for conventional and Islamic stock indexes. He showed that while the recent financial crisis affected both markets, the impact was less significant with Islamic indexes. In addition, portfolio simulations based on the Markowitz model by Arouri et al. (2013) indicate that the exposure of Islamic indices to risk was greater than that of conventional indices before the crisis, but that investment in conventional and Islamic funds generates interesting diversification opportunities. Such opportunities have increased since the global financial crisis, reflecting a change in investor’s choices to some 3
We identify different instruments: i) instruments with a passive partnership with an entrepreneur (Mudaraba), ii) instruments with an active partnership with an entrepreneur (Musharaka), iii) Instruments without any partnership with an entrepreneur (Murabaha), defined as the agreed profit from a sales contract, and iv) instruments associated with insurance contracts (Ijarah or Leasing; Salam and Istisnaâ or Traitance contract). Among these instruments, the main source of finance is the Murabaha that accounted for about 66% of the total finance of Islamic banks over the period 1994-1996. It is 8% for Mudaraba, 10% for Musharaka, 4% for Ijarah, Salam and Istisnaâ, and 12% for the other financial models (12%). See Iqbal et al. (1998), Ben Bouheni and Bellalah (2012) for more details of these finance models and their conditions. 3
extent. The comparative investigation of stock index performance proposed by Jawadi et al. (2014) for three regions (France, the USA and the World) through the application of different performance ratios before and after the subprime crisis shows that Islamic indexes have replaced conventional indexes to a significant extent. A recent book edited by Barnett and Jawadi (2013) also presented several interesting findings about Islamic finance. The authors argued that while Islamic finance cannot fully replace conventional finance, traders, policymakers and investors would do well to learn more about it to improve and reform conventional financial systems and try to avoid further crises in the future. More recently, Arshad and Rizvi (2013) analyzed the effects of fundamental changes to Islamic indices using a continuous wavelet approach for the period 1997 to 2011 for Asia Pacific and emerging stock markets. They showed that Islamic indices were less affected by speculative shocks, and concluded that Islamic finance could be a good alternative to the distress of conventional finance. In turn, Hasan and Dridi (2010) looked at the impact of the global financial crisis on conventional and Islamic banking profitability. They showed that while both Islamic and conventional banks were affected by the recent financial downturn, the impact on Islamic banks was less notable. Abdous and Arrabi (2011) focused on the direct and indirect effects of the crisis on conventional and Islamic banks. For the authors, the direct effects are negative and reflect the losses of Islamic banks and institutions due to the financial downturn and the collapse of the economic sector, while the indirect effects are positive, involving a reconsideration of Sharia rules to re-innovate and regulate conventional finance, and to improve risk management controls in Europe and the USA. The number of conferences, books and research activities on Islamic finance has risen significantly in recent years, while some financial institutions (i.e. the French bank Société Générale) have also recently adopted Islamic financial products and/or introduced new financial products that are Sharia compliant. Increased investment in Islamic financial products also reflects the new attitude of investors towards this industry, today considered as a refuge from the global financial crisis. In the same context, Bourkhis and Nabi (2013) examined whether Islamic banks were more resistant than conventional banks during the crisis. Using a non-parametric analysis and a panel data approach, the authors showed that Islamic bank profitability varied across market phases, and that they were more profitable than conventional banks only before the financial crisis. More recently, Tabash and Dhankar (2014) examined the performance ratio of all fully fledged Islamic banks in the Kingdom of Saudi Arabia during the financial crisis. Using performance and stability ratios of Islamic banking for the period 2005 to 2010, their results 4
revealed that Islamic banks were more stable in terms of capital adequacy and liquidity. Ouerghi (2014) examined whether Islamic banks were more resilient than conventional banks during the financial crisis. He found that conventional banks were more profitable, less prone to credit risk and more efficient than Islamic banks during the post-crisis period. The findings also showed that large banks fared worse than small ones, and that conventional banks were more financially stable than Islamic ones. Similar conclusions were also made by Chenguel (2014). Addefiar et al. (2015) conducted a survey on the recent empirical literature in Islamic banking and finance, but identified no major differences between Islamic and conventional banks overall. While Islamic banks on the whole seem less risky than conventional banks thanks to the application of Sharia board rules, existing empirical studies are far from unanimous about Islamic finance. Indeed, previous conclusions appear to be sample and period dependent. Consequently, conventional and Islamic banks’ exposure to market risk remains ambiguous and warrants further investigation. Our paper aims to fill this gap by investigating the volatility dynamics of conventional and Islamic banks not only in the context of the crisis but also during calm periods and for different heterogeneous banks. To this end, we used recent time-series and econometric developments (long-memory-GARCH model class) and recent data for the period 2006-2013, applying structural break tests to check for further changes in volatility dynamics. Next, we developed an appropriate econometric specification based on a Fractionally Integrated EGARCH model to capture long-term volatility dependency. The specification was designed to capture volatility asymmetry and persistence. Investigating the persistence hypothesis is important to determine the duration of volatility shocks for conventional and Islamic banks. This resulted in two interesting findings. First, we showed that conventional banks’ volatility is more persistent than that of Islamic banks. This suggests that Islamic banks escaped the recent financial downturn and thus appear to be more resilient than conventional banks. Second, we highlighted a significant degree of heterogeneous resilience, which varies in Islamic and conventional banks according to the bank under consideration. This heterogeneity may be linked to the complexity associated with the implementation and development of Islamic banking rules across regions. The remainder of the paper is organized as follows. Section 2 presents the econometric methodology. The data and empirical results are discussed in Section 3. The last section provides our concluding remarks.
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2. Econometric methodology First, we present the EGARCH and FIGARCH models that are used to investigate the long-term volatility dependence. Second, we present the Iterative Cumulative Sum of Square (ICSS) method that is applied to check for structural breaks in volatility dynamics.
2.1. EGARCH and FIEGARCH Models The Exponential GARCH model, initially introduced by Engle and Ng (1993), is often used to capture volatility dynamics. It is preferred to the usual GARCH model as it does not require the non-negativity condition of variance equation parameters. In addition, it enables time-variation and further asymmetry in volatility to be reproduced (French et al., 1987; Engle and Ng, 1993). This asymmetric effect is known as the ‘leverage’ effect, referring to the fact that bad news effects are greater than good news effects. Formally, an AR(1)-EGARCH (1,1) model, as in Nelson (1991), is specified as follows: Rt = m0 + m1 Rt-1 + et, with et = st xt , Et-1(xt ) = 0 and s2t-1(xt ) = 1 lo g s
2 t
= w + g
e t -1 s
2 t -1
+ a
e t -1 - E ( e t -1 ) s
2 t -1
+ b lo g s
2 t -1
(1)
Where: m0, m1, α, β, g and w are the model parameters to be estimated. Parameter g measures the asymmetric effect on volatility. The first equation captures the mean equation, while the second refers to the variance. This specification is adapted to reproduce further asymmetry through the introduction of the quantity g
e
t -1
s
2 t -1
. In particular, if g is negative and statistically significant, it implies that
a negative innovation induces an increase in volatility more than a positive one of equal magnitude. This phenomenon is consistent with the leverage effect, which means that a negative innovation induces an increase in the aggregate debt-to-equity ratio and hence produces a higher volatility effect. The quantity
e t -1 - E ( e t -1 ) s t2-1
effect of a positive or negative innovation, while the expression a
measures the magnitude
e t -1 - E ( e t -1 ) s t2-1
allows an
increase (resp. a decrease) in volatility if e t -1 is greater (resp. smaller) than its expected
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value. More specifically, the positive and negative effects of standardised shock on volatility are given by the following equations respectively: ¶ log s t2 ¶ log s t2 ¶ s t2 1 ¶ s t2 1 = g + a and = g a Û = g + a and = g -a ¶ e t+ ¶ e t¶ e t+ s t2 ¶ e t- s t2
(2) S=
We define the asymmetry ratio as:
g -a g + a . We have negative asymmetry if S > 1, perfect
symmetry if S = 1 and positive asymmetry if S < 1. Next, we can estimate the degree of volatility persistence by quantifying the time required for a shock to reduce persistence to one Half Life (HL) with respect to its original level. 4 HL is computed as follows:
HL =
log(0.5) log(b )
(3)
However, for a more accurate measure of persistence, the above EGARCH can be extended to model persistence with the following FIEGARCH model: log s t2 (1 - L)d (1 - b L) = w + g
e t -1 s t2-1
+a
e t -1 - E( e t -1 ) s t2-1
(4)
where d is a parameter to be estimated, measuring the persistence degree of volatility.
In practice, since the standard errors for financial data are often characterized as leptokurtic, asymmetrical and not normally distributed, we estimate the FIEGARCH model under the hypothesis of a skewed-student distribution for a xt model. Accordingly, the FIEGARCH model is estimated by the Maximum Likelihood method.
2.2. Structural break test Determination of structural breaks in unconditional volatility is useful for estimating the long memory parameter (d). Indeed, failure to take further structural breaks into account in the conditional volatility model will lead to an overestimation of the degree of volatility persistence (Andreou and Ghysels, 2002; Mikosh and Starica, 2004; Hillebrand, 2005). Furthermore, Granger and Hyung (2004) and Choi et al. (2010) showed that the presence of structural breaks in volatility can generate a “spurious long memory process’’, leading to inaccurate investment decisions. Accordingly, Choi and Hammoudeh (2009) argue that long
4
For more details, see Bhar and Nikolova (2009) and Bensafta and Semedo (2009). 7
memory parameters are usually overestimated and are lower after taking the structural breaks into account. Thus, the Iterated Cumulative Sum of Squares (ICSS) procedure is required to determine the different dates of the structural breaks in order to adjust the estimation. The ICSS procedure developed by Inclan and Tiao (1994) and extended by Sanso et al. (2004) is based on successive iterations of the CUSUM test. The ICSS methodology assumes that the time series has a stationary variance from a fixed time to a date, characterised by a sudden change in variance. It can be used to determine further structural breakpoints of the unconditional variance induced by an exogenous shock. In practice, we test the null hypothesis for which this unconditional variance is constant against the alternative hypothesis of a break in the unconditional variance of some dates in the sample. If the null hypothesis is rejected, a number of sub-samples will be retained and we can estimate the EGARCH and the FIEGARCH models for all of the sub-samples.
3.
Empirical analysis 3.1 Data and preliminary analysis This study investigates the volatility dynamics of conventional and Islamic banking indices of the Gulf Cooperation Council countries, excluding Oman.5 To do this, we construct two banking indices for the representative Islamic banks and conventional banks respectively of each state in the GCC. Each banking index is defined as a weighted average of the bank price. Formally, the Banking Index is defined as ∑ni=1 (CBi / CBT) × Price, with CBi denoting the stock market capitalization of the bank I, CBT = ∑ni=1 CBi, “Price” denoting the daily observed bank price, and n the number of banks. We used daily data from June 2006 to May 2013. This period enabled us to investigate the effects of the subprime crisis on the volatility of these banking indices. Data for individual banks were obtained from Datastream. For the United Arab Emirates, we considered two conventional and Islamic banking indices for its two largest states: Dubai and Abu Dhabi. Table 1 gives more details on the number of conventional and Islamic banks considered for each country.
5
Data is not available for Oman. Banks are selected with regard to their capitalization size. 8
Table 1: Data Statistics (number of banks) Countries
Islamic banks
Conventional banks
Dubai
1
2
Abu Dhabi
2
6
Saudi Arabia
5
5
Bahrain
5
5
Kuwait
4
5
Qatar
3
4
In a first step, we applied three unit root tests (i.e. Augmented Dickey-Fuller (ADF) test, Philips-Perron test and the KPSS test), showing that the banking indices under consideration are not stationary in level but stationary in the first difference.6 Accordingly, we focus hereafter on the bank returns reported in Figures 1 and 2, with three remarks. First, bank returns show a volatility excess that significantly varies per country under consideration, but is more significant for conventional than for Islamic Banks. Second, this volatility excess seems to have been significantly and differently persistent even after the subprime crisis for both conventional and Islamic banks. Third, bank returns are characterized by significant shifts, suggesting further evidence of breaks in volatility, notably during crisis periods. 7
6
We do not report the results of unit root tests to save space, but they are available upon request. Following a suggestion by one reviewer, before moving on to the volatility analysis, we also compared Islamic bank indexes and conventional bank indexes in terms of returns. Our findings are consistent with Arouri et al. (2013) and Jawadi et al. (2014), and confirm that Islamic banks appear to outperform conventional banks, particularly in periods of crisis. To save space, we do not report the results in this paper, but they are available upon request. 7
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Figure 1: Evolution of the conventional banks’ returns Saudi Arabia 0.05
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Figure 2: Evolution of the Islamic banks’ returns Qatar Evolution of Qatar Islamic banks’ average returns
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In the second step, we attempted to improve the comparative analysis of the volatility dynamics. Accordingly, i) we first checked for the presence of shifts, ii) then tested for further asymmetry in the data, and iii) finally, we developed an appropriate model for volatility persistence.
3.2 Searching for structural breaks in volatility We ran the ICSS procedure in order to test for further shifts and reported the main results in Table 2. Accordingly, we noted that Islamic and conventional banks display the same number of changes in variance and almost on the same dates. We categorized these 11
different breaks in three homogenous sub-periods to simplify the econometric calculation. The first one, called the pre-subprime crisis period, is from 16/06/2006 to 30/06/2007. The second, called subprime and financial crises period, covers the period from 01/07/2007 to 30/06/2009. The third, called the post subprime crisis period, covers the period from 01/07/2009 to 15/05/2013. We then carried out a comparative econometric modelling procedure for Islamic and conventional bank volatility over these three sub-periods. This approach consists of taking further structural breaks in volatility into account, enabling us to improve the modelling and explanation of volatility dynamics and persistence over different sub-samples, at the same time avoiding any conclusion in terms of fallacious persistence degrees (Engle and Ng, 1993). Table 2: ICSS Procedure results ADC BAC DUC KUC QAC SAC ADI BAI DUI KUI QAI SAI
29/06/2007 25/06/2007 12/06/2007 25/09/2006 05/10/2006 27/10/2006 29/06/2007 23/06/2007 25/09/2006 29/06/2007 07/09/2006 02/03/2007
15/10/2007 31/01/2008 19/10/2007 11/05/2007 19/02/2007 26/06/2007 03/01/2008 04/04/2008 02/11/2006 31/07/2007 27/11/2006 03/06/2007
08/11/2007 28/04/2008 03/01/2008 01/07/2007 23/03/2007 04/12/2007 07/02/2008 04/08/2008 20/12/2006 18/10/2007 25/12/2006 05/12/2007
10/01/2008 07/10/2008 18/04/2008 12/09/2007 03/07/2007 29/01/2008 03/04/2008 21/06/2009 01/03/2007 09/09/2008 20/02/2007 15/02/2008
23/01/2008 01/12/2008 25/08/2008 12/09/2008 31/10/2007 03/10/2008 07/08/2008 29/03/2010 25/06/2007 05/03/2009 09/05/2007 03/09/2008
04/08/2008 06/06/2009 20/06/2009 14/06/2009 09/09/2008 23/06/2009 02/09/2008 26/05/2010 24/08/2007 09/08/2011 03/04/2008 23/06/2009
01/06/2009 25/03/2010 06/04/2010 02/05/2012 16/06/2009 15/09/2009 18/06/2009 29/09/2010 13/11/2007
02/07/2009 14/12/2009 27/01/2011 11/03/2011 26/05/2010 24/01/2011 05/08/2011 03/11/2011 01/02/2011 15/02/2012 22/03/2010 02/12/2009 27/11/2009 05/10/2010 11/12/2007
27/05/2010 22/03/2011 24/05/2010 12/01/2011 28/01/2011 22/04/2011 12/04/2012 21/03/2008 09/09/2008 04/06/2009
30/06/2008 28/08/2008 24/06/2009 19/06/2012 27/05/2010 02/09/2010 18/03/2011 15/09/2011 01/02/2012
Note: ADC, BAC, DUC, KUC, QAC, and SAC denote the aggregate index returns of conventional banks in Abu Dhabi, Bahrain, Dubai, Kuwait, Qatar and Saudi Arabia respectively. ADI, BAI, DUI, KUI, QAI, and SAI refer to the aggregate index returns of Islamic banks in Abu Dhabi, Bahrain, Dubai, Kuwait, Qatar and Saudi Arabia respectively. For each bank, we indicate the beginning of the crisis in bold and the end of the financial crisis in italics.
3.2 Assessing for asymmetrical and ARCH effects In the next step, we computed the main descriptive statistics for the three sub-periods and reported them in Table 3. We can note several interesting results. First, bank returns are fairly negative on average except during the post crisis period, suggesting that prices are under correction due to the global financial crisis effect. Second, we noted similar unconditional volatilities (standard deviations) for both conventional and Islamic banks, but that were higher than their returns. Third, the distributions of Islamic and conventional bank returns are leptokurtic, suggesting fat tails in the return distributions of these banks. This leptokurtic excess property indicates the presence of high risk in the extreme tail of the Islamic and conventional bank return distributions. Interestingly, using a hypothesis test to 12
compare leptokurtic excess for conventional and Islamic banks, we observed that risk intensity is virtually the same in both types of bank.8 Fourth, we found negative skewness values for both banking index return distributions during the crisis period, which indicates that these returns are asymmetrical to the left. This result can be explained by the fact that during this period, banking returns were characterized by a significant decrease. Skewness is nonetheless fairly positive before and after the subprime crisis, suggesting asymmetry to the right for the bank return distributions, probably due to the fact that during these two sub-periods, banking index returns were positive. Overall, both Islamic and conventional bank return distributions seem to exhibit more asymmetry, leptokurtic effect and longer tails, either on the right or on the left, than normal distribution patterns. Finally, we carried out a volatility clustering test (Table 3). Using the Ljung-Box test, we checked the auto-correlation of the squared weekly return series and identified persistence of non-linear dependence, which means that there is conditional heteroscedasticity in the returns of both conventional and Islamic banks. The volatility clustering effect is statistically significant in both banks. Interestingly, this ARCH effect is more significant during the crisis period, confirming our analysis of Figures 1 and 2. Thus, in order to model volatility dynamics while capturing the ARCH effect, asymmetry and volatility clustering in banking index returns, we estimated an EGARCH model in the next step.
8
This finding is based on the hypothesis test relative to the equality between the kurtosis of the different bank returns. We do not report the details of this test to save space, but they are available upon request. 13
Table 3 : Descriptive statistics Max 0.0403
Min -0.0392
Std. Dev. 0.0121
Skewness 0.0831
Kurtosis 4.0749
J.B 13.3605
LM1 19.51*
Q2(16)2 73.632**
BACAVC
- 0.0012
0.0306
-0.0216
0.0074
0.6534
4.7195
52.6709
20.49*
98.031*
DUCAVC
-0.0001
0.0470
-0.0370
0.0136
0.2211
3.8600
10.5615
20.02*
84.403**
KUCAVC
0.0010
0.0371
-0.0499
0.0097
0.0985
6.6033
147.0544
57.73***
60.102***
QACAVC
0.0001
0.0657
-0.0617
0.0165
0.0757
5.0718
48.7295
35.84*
30.784**
SACAVC
-0.0016
0.0579
-0.0747
0.0170
-0.3594
6.1502
117.8925
31.24**
22.053***
ADIAVC
-0.0011
0.0661
-0.0867
0.0171
-0.1492
7.1101
191.7583
29.56*
27.996**
BAIAVC
-0.0001
0.0242
-0.0405
0.0088
-0.5465
5.7215
97.1274
18.78*
90.882**
DUIAVC
-0.0001
0.1377
-0.0756
0.0219
0.7693
9.9700
575.3007
37.94**
35.059***
KUIAVC
0.0012
0.0566
-0.0345
0.0133
0.4269
4.1243
22.5075
17.47*
94.884*
QAIAVC
-0.0005
0.0804
-0.0976
0.0204
0.1164
5.7985
89.0466
51.67***
87.794***
SAIAVC
-0.0030
0.0828
-0.0940
0.0223
0.3445
6.1060
114.3019
34.52*
41.243***
ADCC
-0.0008
0.0780
-0.0942
0.0220
-0.2976
5.5156
145.3534
132.95***
399.838***
BACC
-0.0008
0.0317
-0.1105
0.0109
-2.2562
23.4961
9579.895
145.21***
69.4741**
DUCC
-0.0011
0.0930
-0.0513
0.0177
0.1346
5.7081
161.0893
90.49***
136.407***
KUCC
-0.0011
0.0725
-0.1535
0.0157
-1.6472
20.7782
7110.498
105.21***
58.0235*
QACC
-0.0006
0.0860
-0.1002
0.0240
-0.0059
5.6389
151.4760
139.69***
449.180***
SACC
-0.0006
0.0871
-0.1037
0.0228
-0.3321
6.2802
243.6303
100.94***
183.529***
ADIC
-0.0012
0.1658
-0.1725
0.0305
-0.4070
12.2808
1887.817
78.50***
83.612***
BAIC
-0.0006
0.0604
-0.0942
0.0181
-0.4944
6.7565
328.1976
127.9***
301.713***
DUIC
-0.0024
0.1405
-0.1291
0.0337
- 0.3462
7.4015
431.7974
130.43***
235.324***
KUIC
-0.0008
0.0727
-0.0759
0.0221
0.0073
5.2502
110.1417
206.45***
817.127***
QAIC
-0.0003
0.2098
-0.1890
0.0290
- 0.1388
14.3177
2787.687
171.82***
190.776***
SAIC ADCAPC
-0.0002 0.0005
0.0924 0.0776
-0.1013 -0.0840
0.0221 0.0108
-0.2401 0.3337
7.2190 13.1974
392.1819 4399.268
90.78***
151.773***
34.431**
448.292***
BACAPC
-0.0002
0.0268
-0.0281
0.0068
-0.3440
5.9653
390.3714
31.672**
38.788***
DUCAPC
0.0004
0.0806
-0.0726
0.0141
0.2916
8.71144
1388.464
17.464*
122.548***
KUCAPC
0.0001
0.0357
-0.0416
0.0097
-0.1306
4.3510
79.7745
21.905**
93.153***
QACAPC
0.0005
0.0586
-0.0683
0.0119
0.3135
7.5869
902.8708
24.155**
269.77***
SACAPC
4.96E-05
0.0851
-0.0663
0.0112
0.5907
14.5942
5721.495
18.345*
290.675***
ADIAPC
0.0004
0.0938
-0.1035
0.0131
0.2453
14.6302
12.9854
18.817**
342.338***
BAIAPC
-0.0009
0.4786
-0.4711
0.0259
0.2997
224.652
100.4589
45.358**
250.560***
DUIAPC
0.0003
0.1365
-0.1062
0.0188
0.1422
13.8685
49.7941
43.213**
984.178***
KUIAPC
0.0002
0.0434
-0.0580
0.0109
-0.4118
5.73958
12.8541
18.507*
258.194***
QAIAPC
0.0002
0.0932
-0.0689
0.0099
0.8791
18.8635
13.0477
19.658*
79.736***
Islamic banks Conventional banks
Islamic banks Conventional banks
Islamic banks
Crisis Period After crisis period
Conventional banks
Mean -0.0006
Before crisis period
Banking index ADCAVC
8.81E-05 0.0611 -0.0563 0.0099 0.2093 11.6575 3164.753 28.975** 245.339*** SAIAPC Note: This table reports the main descriptive statistics, normality and ARCH tests for the conventional and Islamic banking indices over the three sub-periods. To identify these sub-periods, we add the letters (AVC), (C) and (APC) to the names of banking indices respectively for the pre-subprime crisis period, the crisis period, and the post crisis period. The LM statistic is used for the ARCH test. The Q2(16) is the statistic used to test autocorrelation up through the 36th lag of the squared returns. J-B denotes the Jarque-Bera test. ***, **, * indicate that the estimators are significant at 1%, 5% and 10% level respectively.
3.4 Volatility modeling with an EGARCH model Table 4 shows the main econometric results associated with the estimation of an EGARCH (1, 1) model (equation (1)) over the three sub-periods. First, let’s recall that the number of lags was specified using the information criteria and autocorrelation function of the squared innovation series. Second, we noted that the GARCH effects were present and statistically significant in all the banks, suggesting further evidence of persistence in banking 14
index volatility. More particularly, the b estimates for banks were positive and exhibited values close to 0.90, suggesting that current volatility depends strongly on lagged volatility in each sub-period. Furthermore, the high level of b estimates indicates a significant degree of volatility persistence in both banks, reflecting the extent to which a momentum effect is present in the conditional volatility process. It is worth noting that this GARCH effect is significant for both conventional and Islamic banking indices before, during and after the crisis period, which indicates the presence of a clustering effect, whatever the type of bank or financial market situation.
Table 4: EGARCH estimation results Country AD
BA
DU
KU
QA
SA
AD
BA
DU
KU
QA
SA
Banks ADCAVC ADCC ADCAPC BACAVC BACC BACAPC DUCAVC DUCC DUCAPC KUCAVC KUCC KUCAPC QACAVC QACC QACAPC SACAVC SACC SACAPC ADIAVC ADIC ADIAPC BAIAVC BAIC BAIAPC DUIAVC DUIC DUIAPC KUIAVC KUIC KUIAPC QAIAVC QAIC QAIAPC SAIAVC SAIC SAIAPC
w -3.636711*** -0.848511** -0.506671*** -6.806859** -0.462276* -3.243612*** -5.943467** -1.019357* -0.545546** -3.163316*** -0.753161* -1.227361** -1.267560*** -0.540925** -0.691343* -0.90016*** -0.878335** -0.829331*** -2.049833*** -2.463374** -0.698112** -0.254896* -0.460250*** -5.568468** -0.970939** -0.947123** -0.515307*** -1.485210*** -0.518139*** -0.612972** -1.171876*** -1.167952** -0.373052* -2.66242*** -0.675092** -1.208968***
ARCH(1) 0.440126** 0.445144*** 0.174003** 0.382901** 0.103552*** 0.201247** 0.143356*** 0.293128* 0.218375** 0.651730*** 0.391417* 0.197558** 0.364762* 0.346173*** 0.197333** 0.18370*** 0.283132** 0.229385*** 0.4700375** 0.390346*** 0.2508616** 0.1** 0.1737309*** 0.05555734** 0.1006837* 0.427586** 0.2878410*** 0.172034*** 0.3555813** 0.2409502*** 0.520734** 0.3745536*** 0.2390821** 0.3572412*** 0.2401794*** 0.25379434**
GARCH(1) 0.628104** 0.936038*** 0.958707** 0.830189* 0.958931** 0.688436*** 0.884512** 0.902271*** 0.954121** 0.718239* 0.944181** 0.884484*** 0.879726** 0.963347*** 0.939168** 0.907092** 0.912525* 0.926121*** 0.7953595** 0.698983** 0.9413206*** 0.8999554*** 0.9601205** 0.8845120* 0.8834769* 0.909170*** 0.9628876** 0.843831*** 0.9710359*** 0.9528704** 0.903618*** 0.8766992** 0.9782438*** 0.6917145* 0.9348459*** 0.89015091**
LEV(1) -0.035161** -0.014287*** -0.017852* -0.016271*** -0.087401** -0.035134*** -0.01109*** -0.013828* -0.016050** -0.094640** -0.095997*** -0.021856** -0.046152*** -0.136357** -0.097743* -0.384931** -0.208996*** -0.325006** -0.021701** -0.043119** -0.0196986* -0.0218546* -0.0578777** -0.0221582** -0.03110*** -0.054386* -0.014617** -0.013704*** -0.046508** -0.016602*** -0.014153*** -0.039497** -0.018037*** -0.063336** -0.035213*** -0.024224*
HL 6.4 15.5 9.5 3.7 22.1 13.6 5.6 13.5 7.3 4.1 23.2 14.4 6.8 25.2 11.0 9.5 10.3 9.1 4.3 13.4 9.5 6.5 16.2 10.3 3.5 10.3 6.6 3.3 12.1 11.2 5.4 18.53 8.5 6.1 7.4 5.0
Note: This table reports the estimates of the EGARCH model for the conventional and Islamic banking indices over the three sub-periods. To identify these sub-periods, we add the letters (AVC), (C) and (APC) to the names of banking indices respectively for the pre-subprime crisis period, the crisis period, and the post crisis period. ***, **, * indicate that the estimators are significant at 1%, 5% and 10% level, respectively. LEV(1) expresses the Leverage Effect.
15
Moreover, the estimates of b indicate the stability of volatility and a tendency for the shocks to persist. In fact, the estimation of the Half-Life (HL) parameters during the different sub-periods (also reported in Table 4) gives us significant information on the volatility persistence of returns. Indeed, based on the conventional and Islamic banks’ HL results, Figure 3 shows that the former require more time to reduce volatility persistence by half with respect to its original level.
Figure 3: Estimated half-life for Islamic and conventional banking indices
Half lives in terms of days
30 25 20 15 10
Conventional Banks Islamic Banks
5
after crisis
in crisis
after crisis
before crisis
in crisis
before crisis
after crisis
in crisis
before crisis
after crisis
in crisis
before crisis
after crisis
in crisis
before crisis
after crisis
in crisis
before crisis
0
Abu Dhabi (UAE) Dubai (UAE) Bahrain Kuwait Qatar Saudi Arabia Islamic and conventional Banking indices of the GCC countries before, during and after the crisis
In particular, the half-life is between 3 and 7 days for Islamic banking index returns during the period before the crisis, and between 6 and 9 days for conventional ones. The crisis period required between 7 and 16 days for Islamic banks, while it is bounded at between 7 and 25 days for conventional ones. After the crisis, the HL is between 3 and 12 days for Islamic banks and between 7 and 14 days for conventional ones. It seems that during the post crisis period, HL estimates for conventional and Islamic banks saw a decrease in their values compared to those of the crisis period, indicating the presence of a financial crisis regulation process. Overall, however, this empirical finding indicates that conventional banks take more time than Islamic ones to reduce the return volatility persistence by half with respect to its initial level. In order to better appreciate these persistence properties in the data, in the next step we estimated a FIEGARCH model.
16
3.5 Volatility modeling persistence with a FIEGARCH model We estimated the FIEGARCH model given by equation (4) to better reproduce the persistence effect in our data, proposing a direct measure for this persistence. We reported the main results in Table 5 and noted several interesting results. First, we confirmed our previous analysis and showed that the estimated degrees of persistence for the banks under consideration over the three sub-periods are statistically significant. As previously mentioned and expected, the persistence degree is higher during the crisis period for both banks. In particular, the degree varies between 0.251 and 0.416 for Islamic banking indices, while it is bounded between 0.452 and 0.695 for conventional ones. During the two other periods, the degree of persistence shows significant values but is lower than those estimated during the crisis period for either Islamic or conventional banks. All in all, however, we also confirmed that persistence is far higher for conventional banks than for Islamic ones. Second, we showed that volatility dynamics exhibit asymmetry. Indeed, the leverage effect or asymmetric shock characterizes all banking indices since the estimates of parameter g are negative and statistically significant (also reported in Table 5). The positive sign of a estimators suggests that banking volatility increases when the magnitude of market movement is large. However, we noted that the intensity of the leverage effect is very weak in both conventional and Islamic banks. Comparing the values of column B and C in Table 5, we concluded that during the three sub-periods, the impact of a negative shock (B) is greater in absolute value than that of a positive one (C).9 Thus, for all banking index returns, the asymmetry coefficient S (also reported in Table 5) is greater than one, which indicates that negative past innovations for Islamic and conventional banks result in greater conditional volatility in the current period and vice versa. However, this asymmetric effect is more significant in conventional banks. Furthermore, we noted that before, during and after the financial crisis, leverage effects are greater in conventional banks than in Islamic ones.10
We compute the effects of positive shock by adding the parameters a and g , while the negative effect of innovation is calculated as follows: -a + g . 10 This empirical finding is based on the hypothesis test relative to the equality between the leverage effects of conventional banks and those of Islamic ones. We do not report these results to save space, but they are available upon request. 9
17
Table 5: FIEGARCH estimation results Country Banking index ADCAVC AD ADCC
BA
DU
KU
QA
SA
AD
BA
DU
KU
QA
SA
w -1.560 -0.3938***
ADCAPC
-0.2423***
BAAVC
-2.2566
BACC
-0.5587***
BACAPC
-0.5692***
DUCAVC
-0.8526
DUCC
-0.4384***
DUCAPC
-0.3683***
KUCAVC
-1.7568
KUCC
-0.2942***
KUCAPC
-0.1817***
QACAVC
-1.5336***
QACC
-0.4336***
QACAPC
-0.1733***
SACAVC
-0.2437***
SACC
-0.8261*
SACAPC
-0.3984***
ADIAVC
-1.775
ADIC
-1.024***
ADIAPC
-0.2065***
BAIAVC
0.1613***
BAIC
-0.2228***
BAIAPC
0.01996***
DUIAVC
-2.2882
DUIC
-0.4184***
DUIAVC
-0.3003***
KUIAVC
-0.8427
KUIC
-0.3105***
KUIAPC
-0.2537***
QAIAVC
-0.7173***
QAIC
-1.467***
QAIAPC
-0.1373***
SAIAVC
-1.281
SAIC
-0.505**
SAIAPC
-0.628***
GARCH(1)
0.628104*** 0.936038*** 0.958707*** 0.958931*** 0.958931 0.6884367 0.8845122 0.9022711 0.9541210 0.718239 0.944181 0.88448404 0.879726 0.9633470 0.9391685 0.90709 0.9125254 0.9261212 0.7953595 0.698983 0.9413206 0.8999554 0.9601205 0.8845120 0.8834769 0.909170 0.9628876 0.843831 0.9710359 0.9528704 0.903618 0.8766992 0.9782438 0.6917145 0.9348459 0.89015091
ARCH(1) 0.3254***
LEV(1) -0.0540
d 0.2945*
B
C
S
-0,3794
0,2714
1,39793632
0.3710***
-0.0747***
0.5907***
-0,4457
0,2963
1,90421871
0.2001***
-0.0283***
0.4471***
-0,2284
0,1718
1,32945285
0.4895***
-0.088*
0.2996**
-0,5775
0,4015
1,43835616
0.2591***
-0.0362
0.4526***
-0,2953
0,2229
1,62480933
0.2737***
-0.0579***
0.3502**
-0,3316
0,2158
1,53660797
0.1105***
-0.0204*
0.4632*
-0,1309
0,0901
1,45283019
0.3881***
-0.0024*
0.4828***
-0,3905
0,1857
2,21244491
0.1951***
-0.0319***
0.32145
-0,227
0,1632
1,39093137
0.6801***
-0.0439
0.1860*
-0,724
0,636
1,13800691
0.2754***
-0.0219
0.5293***
-0,2973
0,1535
1,97178106
0.1514***
-0.0537***
0.3530***
-0,2051
0,0977
2,09928352
0.1452*
-0.0217*
0.2919***
-0,1669
0,1235
1,35141710
0.1506*
-0.0994***
0.6958***
-0,2500
0,0512
4,88281250
0.1302***
-0.0195***
0.3971***
-0,1497
0,1107
1,35230352
0.0728***
-0.0464**
0.24782
-0,1192
0,0264
4,51515152
0.2907***
-0.0537**
0.6363***
-0,3444
0,1537
2,45316456
0.1871***
-0.0715***
0.1317*
-0,2586
0,1156
2,23702422
0.3477***
-0.0767***
0.0048
-0,4244
0,271
1,56605166
0.1809***
-0.0470***
0.2931***
-0,2279
0,1409
1,61745919
0.1547***
-0.0412
0.12141
-0,1959
0,1135
1,72599117
0.4533***
-0.0171
0.3141***
-0,4362
0,4704
1,09272959
0.2226***
-0.0183***
0.4160***
-0,2443
02043
1,19579050
0.2854***
-0.0385***
0.3234***
-0,3239
0,2469
1, 31180011
0.1210*
-0.0081*
0.2931**
-0,1291
0,1129
1,14348981
0.4503***
-0.1373***
0.3644***
-0,5876
0,313
1,87731629
0.1589*
-0.1215***
0.2936***
-0,2804
0,0374
7,49732621
0.1708*
-0.0218*
0.0955
-0,1926
0,149
1,29261745
0.3609***
-0.0738***
0.3393***
-0,4347
0,2871
1,51410658
0.2709***
-0.0446***
0.2994***
-0,3155
0,2263
1,39416703
0.2219**
-0.0773***
0.5414***
-0,2992
0,1446
2,06915629
0.4286***
-0.1551***
0.38124
-0,5837
0,2735
2,13418647
0.1486***
-0.0462***
0.4607**
-0,1948
0,1024
1,90234375
0.3031***
-0.0528***
0.1888*
-0,3559
0,2503
1.42189372
0.2267***
-0.3679***
0.25121
-0,5946 0,25001
1,85516373
0.2104***
-0.0684***
0.12141
-0,2788
1,96338028
0,142
This table reports the estimates of the FIEGARCH model for the conventional and Islamic banking indices over the three sub-periods. To identify these sub-periods, we add the letters (AVC), (C) and (APC) to the names of banking indices respectively for the pre-subprime crisis period, the crisis period, and the post crisis period. ***, **, * indicate that the estimators are significant at 1%, 5% and 10% level, respectively. LEV(1) expresses the Leverage Effect (γ). B and C denote the effect of a negative (resp. positive) shock. The ration S measures the asymmetry coefficient. D measures the degree of persistence. Note:
Third, in Figure 3, using FIEGARCH estimates, we illustrated the asymmetry associated with the volatility dynamics, and notably its response to further external shocks. In particular, we considered positive (resp. negative) shock associated with positive (resp. negative) news and computed the reaction of the volatility function for each shock. To this 18
end, we estimated the curves illustrating the relationship between news and volatility (Figure 3). The horizontal axis depicts the news at time t-1 captured by the lagged innovation, and the vertical axis conveys volatility behaviour. In particular, negative values on the horizontal axis denote bad news, whereas positive values refer to good news. Thus, as in Engle and Ng (1993), the dynamics of the new impact curves can improve the analysis of volatility behaviour following good and bad news. This gave us some interesting results. First, we saw that after bad news, the impact curve exhibits a negative slope that is higher than that of a positive news item, especially in conventional banks. This confirmed the asymmetric effect present in the estimated FIEGARCH models. Second, we showed that conventional banking index volatility experienced a significant increase following bad news compared to the volatility dynamics of Islamic banking indices. This can be explained by the highly perverse effect of the financial crisis which affected conventional banks far more than Islamic banks. In particular, we noted that conventional banking index returns in Qatar exhibit the strongest reactions to bad news, while conventional banks in Bahrain show the lowest sensitivity to bad news, suggesting that the crisis effect varies across countries. 11 Third, while both types of bank exhibit lower sensitivity to positive news, this is especially true for conventional banks, where volatility reacts hardly at all to good news.
11
This empirical finding is related to the crisis period. We also noticed that the result is in line with the estimated asymmetric coefficient S in Table 5. 19
Figure 3: News impact curves for Islamic and conventional banks New Impact Curve: the case of KUC and KUI Banks
New Impact Curve: the case of SAC and SAI Banks
1.040
1.06
1.06
1.05
1.05
1.035 1.030 ASYKUI ASYKUC
1.025
1.04
1.020
1.04
ASYSAI ASYSAC
1.03
1.03
1.02
1.02
1.01
1.01
1.015 1.010 1.005 1.000 1985 -0.1
1.06
1990 -0.05
1995 0
2000 0.05
2005 0.1
1.00
2010 0.15
1.00 1985 -0.1
N e w Im p ac t C urve: th e c as e o f D U C an d DU I B an k s
1990 -0.05
1995 0
2000 0.05
2005 0.1
2010 0.15
New Impact Curve: the case of BAC and BAI Banks 1.05
1.05 1.04
AS Y DUI AS Y DUC
1.04
ASYBAI ASYBAC
1.03
1.03 1.02
1.02 1.01
1.01
1.00
1.00 1985 - 0.1
1.06
1990 -0.05
1995 0
2000 0.05
2005 0.1
1985 -0.1
2010 0.15
New Impact Curve: the case of ADC and ADI Banks
1990 -0.05
1995 0
2000 0.05
2005 0.1
2010 0.15
New Impact Curve: the case of QAC and QAI Banks 1. 06
1.05
1. 05
1.04
1. 04 ASYQAI ASYQAC
ASYADI ASYADC
1.03
1. 03
1. 02
1.02
1. 01
1.01
1. 00
1.00 1985 -0.1
1990 -0.05
1995 0
2000 0.05
2005 0.1
1985 -0.1
2010 0.15
1990 -0.05
1995 0
2000 0.05
2005 0.1
2010 0.15
Note: ASYADC, ASYBAC, ASYDUC, ASYKUC, ASYQAC, and ASYSAC denote the News Impact Curves of the conventional banking system in Abu Dhabi, Bahrain, Dubai, Kuwait, Qatar and Saudi Arabia, respectively. ASYADI, ASYBAI, ASYDUI, ASYKUI, ASYQAI, and ASYSAI refer to the News Impact Curves of the Islamic banking system in Abu Dhabi, Bahrain, Dubai, Kuwait, Qatar and Saudi Arabia, respectively.
4. Conclusion This study compares the volatility dynamics for Islamic and conventional banking indices in the G.C.C countries. We developed an appropriate econometric framework to investigate two main properties: persistence and asymmetry. The characterization of these properties allowed us to improve the analysis of volatility and to identify their drivers. To this end, a long-memory-EGARCH model was estimated and applied to recent daily Islamic and conventional banking indexes, enabling us to measure volatility persistence during calm and crisis periods. Accordingly, our study contributes several findings. First, our analysis points to significant clustering volatility and tails, suggesting evidence of extreme financial risk. 20
Second, volatility exhibits significant persistence that is far higher for conventional banks than for Islamic banks, and is also greater during crisis periods as opposed to normal ones. Third, the volatility distribution is significantly asymmetrical, and bad news seems to affect volatility patterns more strongly than positive news. Conventional banks also react more strongly to a negative shock than to positive news. Furthermore, conventional banks are more sensitive to the arrival of bad news than Islamic banks. These different findings suggest that Islamic banks are more resilient than conventional banks, while the heterogeneity in banks’ curve reactions can be explained by the differences in their financial support and rules. In order to improve conventional banks efficiency and better control their volatility persistence and risk management, regulations and changes based on the Islamic bank framework could be used as a useful benchmark. However, it is worth noting that due to limited access to data for Islamic banks, our study was conducted in accordance with available data, so our conclusion are necessarily data and sample dependent.
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