Wen Luo

Jean-Jacques Gaumet
email: jean-jacques.gaumet@univ-lorraine.fr

Pierre Magri

Sébastien Diliberto

Feng Li

Pascal Franchetti

Jaafar Ghanbaja

Liqiang Mai

Fast, green microwave-assisted synthesis of single crystalline Sb2Se3 nanowires towards promising lithium storage

Keywords: Microwave synthesis, Sb2Se3 nanowires, solvent-mediated process, lithium-ion battery, high-performance anode

In this work, a fast (0.5 h), green microwave-assisted synthesis of single crystalline Sb2Se3 nanowires was developed. For the first time we demonstrated a facile solvent-mediated process, whereby intriguing nanostructures including antimony selenide (Sb2Se3) nanowires and selenium (Se) microrods can be achieved by merely varying the volume ratio of ethylene glycol (EG) and H2O free from expensive chemical and additional surfactant. The achieved uniform Sb2Se3 nanowire is single crystalline along [001] growth

Introduction

Main-group V-VI metal chalcogenides semiconductors are of great technological importance because of their typical narrow band gap and size-dependent properties [1]. Among these compounds, antimony selenide (Sb2Se3) is one kind of representative direct band-gap semiconductors (Eg≈1.11 eV) and has held promise in the applications of optoelectronic [2], thermoelectrics [3], photovoltaics [4,5] and electrochemical energy storage [6][7][8][9]. Various novel Sb2Se3 nanostructures exhibit different physical and chemical properties compared with their bulk counterparts. Notably one-dimensional (1D) Sb2Se3 nanostructures demonstrate unique properties due to their anisotropic shape, especially 1D nanowires with high aspect ratio [2,10]. Of particular note that Sb2Se3 has captured great attentions as an anode for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs) due to its high theoretic capacity of 670 mAh g -1 [7,11]. The synthesis and fabrication of high-performance Sb2Se3 anode for LIBs is of great interest to researchers in the field of energy and chemistry.

To date, much effort has been devoted to the fabrication of 1D Sb2Se3 nanostructures.

Various approaches have been developed, such as hydrothermal/solvothermal synthesis [12][13][14][15][16][17][18], sol-gel route [19], microwave-assisted method [20,21], template-assisted approach [22], vapor liquid solid (VLS) method [23] and gas-induced reduction method [24]. However, as far as we know, most of previous studies involve high-toxic reducing agent, time-consuming protocol and exhibit low yield feature.

For instance, the common exploited hydrothermal method usually requires a long-time (6-72 h) high-temperature treatment to promote the complete splitting growth of Sb2Se3 nanowires [2,7]. In addition, some involved surfactant or template reagents are hardly to be totally removed, which is unfavorable for the quality of Sb2Se3 nanowires. Although gas-induced growth of Sb2Se3 nanowires seem to be an effective route to achieve high-crystallinity product, the special condition and complex procedures restrict the large-scale synthesis [24]. Therefore, developing a rapid, green, high-yield synthesis of uniform Sb2Se3 nanowires is still highly desired.

Recently microwave-assisted synthesis exhibits superior advantages towards the fabrication of nanomaterials because the microwave irradiation can lead to fast nucleation and greatly shorten the reaction time [25]. Specifically, in the presence of suitable polyol solvents which can induce a reducing and coordinating environment, a certain degree of size and shape control can be facilely established compared to that in pure aqueous or monofunctional alcohol media [26][27][28]. For example, Zhao et al. employed microwave irradiation to obtain Sb2Se3 nanowires in oleylamine which acts as both the reducing solvent and the medium of microwave heating [20]. Zhou et al. reported the synthesis of Sb2Se3 submicron rods based on the polyol reducing process [29]. In our work, a fast (0.5 h) and green solvent-mediated microwave synthesis of uniform single crystalline Sb2Se3 nanowires was developed. In a simple ethylene glycol (EG)/H2O solvent system free from any further surfactant or template, Sb2Se3 nanowires and Se microrods can be achieved by merely varying the volume ratio of EG and H2O. Impressively, when evaluated as an anode of LIBs, Sb2Se3 nanowires exhibit good cycling stability and outstanding rate performance: it can deliver a high reversible capacity of 650.2 mAh g -1 at 100 mA g -1 and a capacity retention of 63.8% after long-term 1000 cycles at 1000 mA g -1 , as well as superior rate capability (389.5 mAh g -1 at 2000 mA g -1 ). We believe that our facile approach provides a new idea for the design of metal chalcogenide, a better utilization of microwave irradiation to synthesize nanostructures in green solvents, and demonstrates its potential application in energy storage systems.

Experimental

Synthetic procedures

All reactants and solvents were analytical grades and used without further purification.

Microwave irradiation experiments were carried out using a Monowave 300 single-mode microwave reactor from Anton Paar GmbH (Graz, Austria). Microwave synthesis was performed in 30 mL Pyrex vials that were sealed with PEEK snap caps and standard polytetrafluoroethylene (PTFE)-coated silicone septa. The typical synthesis of Sb2Se3 nanowires is as follows: First, 0.114 g SbCl3 (0.5 mmol) and 0.13 g Na2SeO3 (0.75 mmol)

were dissolved in the 20 mL deionized water (H2O) under magnetic stirring for 1 h. Second, 0.1 g NaBH4 was rapidly added into solution with continuous magnetic stirring. Next, the mixed solution was transferred to a 30 mL vial for microwave irradiation. During a typical run, the microwave power was adjusted to heat the sample to 180 o C as fast as possible, and then the temperature was maintained for 0.5 h with a stirring rate of 600 rpm. After the vial was naturally cooled to room temperature, black precipitate was collected by centrifugation and was washed thoroughly with anhydrous ethanol and deionized water for three times respectively. The collected precipitate was dried in vacuum at 70 o C overnight for later characterization. For the preparation of comparison samples, the solvent was replaced by the mixture of EG and H2O with different volume ratios: namely, 0 mL EG+20 mL H2O (the achieved sample denoted as E0); 5 mL EG+15 mL H2O (E5); 10 mL EG+10 mL H2O (E10);

15 mL EG+5 mL H2O (E15); 20 mL EG+0 mL H2O (E20). And other conditions remained same to investigate the solvent-mediated effect.

Characterization

Field emission scanning electron microscopy (FESEM) images were collected with a TescanVega3 microscope equipped with a XFlash detector 410M (Bruker) for energy-dispersive X-ray (EDX) analysis. X-ray diffraction (XRD) patterns were obtained with a Bruker AXS D8 Advance diffractometer using a Cu anode (λ Cu Kα = 1.5406 Å). Radiometer with a combined probe.

Electrochemical measurement

The electrochemical performance was evaluated by assembly of 2016-type coin cells in a glove box filled with pure argon gas. The electrodes were prepared by a mixed slurry with a weight ratio of 70% Sb2Se3 nanowire active material, 20% ketjen black and 10% carboxy methyl cellulose (CMC aqueous solution). The mixed slurry was pasted on copper foil and dried in an oven at 70 o C for 12 h. The average mass loading was approximately 1.1 mg cm -2 . In lithium half cells, lithium metal was used as the counter electrode. 1 M LiPF6 in ethylene carbon (EC)/ dimethyl carbonate (DMC)/ ethyl methyl carbonate (EMC) (1/1/1 vol/vol/vol) was used as electrolyte. A Whatman glass fibre membrane (GF/D) was used as the separator. Galvanostatic charge-discharge measurement was performed using a multi-channel battery testing system (LAND CT2001A). Voltammetry (CV) curves and electrochemical impedance spectrum (EIS) were recorded with an electrochemical workstation at different scan rates (Autolab PGSTAT 302). EIS test was performed with a frequency range of 0.01-10 5 Hz.

Results and discussion

To study the influence of mixture solvent on the morphology and composition, controllable experiments were performed by merely changing the volume ratio of EG/H2O.

SEM images and corresponding XRD patterns of the as-prepared five products with the increasing amount of EG from 0 to 20 mL are depicted in Fig. 1. It is found that if no EG was added in the reaction system, uniform and dispersive nanowires were formed (Fig. 1a).

The corresponding XRD pattern of sample E0 demonstrates that all the diffraction peaks can be well indexed as an orthorhombic phase of Sb2Se3 without any detectable impurity (JCPDS 01-075-1462) (Fig. 1b). If 5 mL EG was introduced in the solvent system, even shorter and less uniform nanowires were also achieved (Fig. 1c). Notably, a large number of irregular nanoparticles and nonuniform nanowires coexist when the amount of EG was increased to 10 mL (Fig. 1e). Interestingly, except diffraction peaks indexed to Sb2Se3, some weak peaks ascribed to hexagonal Se (JCPDS 01-073-0456) are evident in both XRD patterns of sample E5 (Fig. 1d) and sample E10 (Fig. 1f). It should be carefully noted that one small diffraction peak located at 2θ=13.2 o detected in both XRD patterns of sample E5 (Fig. 1d) and sample E10 (Fig. 1f) is associated with a kind of hydrogen antimony oxide hydrate (HSbO3(H2O)0.267, JCPDS: 01-083-1928). These results indicate that the multicomponent mixture of orthorhombic phase Sb2Se3, crystalline Se along with antimony oxide hydrate would somehow be obtained in such reduction environment due to the presence of mixture solvents. Notably here as the volume of EG was increased to 15 mL, a dramatic change in morphology occurred: no nanowire was formed and instead micrometer-sized short bundles and rods were obtained (Fig. 1g). XRD pattern of sample E15 reveals that all the diffraction peaks can be indexed to hexagonal Se (JCPDS 01-073-0456) (Fig. 1h). These results suggest that when the amount of EG excess that of H2O, the chemical environment of reaction system has completely changed, which induce the formation of products in different phases and morphologies. The key linkages between all experimental factors will be discussed in the subsequent text. Still, when the amount of EG was further increased to 20 mL, namely in a total EG solvent reaction system, uniform 1D rods about 2 μm in diameter and tens of micrometers in length were formed (Fig. 1i).

XRD result shows that these microrods products are pure phase hexagonal Se (JCPDS 01-073-0456) (Fig. 1j). The facile synthesis of 1D hexagonal phase Se microstructure also exhibits great potential in related applications, such as Li-Se batteries [30] and Se-based solar cells [31]. More importantly, our method comprises merits of green reducing agent, short reaction time and high-quality nanowires product compared to previous reports on the synthesis of Sb2Se3 via wet chemical approaches (See supporting information, Table S1). composites were determined to be only 0.6 wt% (Fig. S1a), 3 wt% (Fig. S2b) and 5 wt% (Fig. S1c), respectively. These TGA results match well with above XRD analysis, confirming that in H2O-excess systems the increase of EG would favor the formation of more Se colloids. Meanwhile, sample E15 and sample E20 contain 76 wt% Se (Fig. S1d) and 96 wt% Se (Fig. S1e) respectively, which further confirm that the resulted products are Se rods.

Fig. 2 Raman spectra of sample E0, E5, E10, E15 and E20.

Raman spectra of above five samples were provided in Fig. 2. Six intensive sharp peaks were obviously observed for three types of Sb2Se3 composites (E0, E5 and E10). In details, the peaks located at 83 cm -1 are ascribed to the translational vibration of Se-Sb-Se. The bands at 118 and 189 cm -1 are due to Se-Sb-Se bending. The vibrations at 252, 372 and 450 cm -1 originate from Sb-Se stretching. The Raman mode assignments are in accordance with previous reports [32], demonstrating the high quality of as-prepared Sb2Se3 composites. In contrast, no typical Raman mode assigned to Sb2Se3 can be detected in Se composites (E15 and E20). The only notable mode located at 238 cm -1 can be assigned to Se-Se stretching when materials are completely crystallized [33].

To provide further insights into the microstructure of Sb2Se3 nanowires, the detailed morphology and structure characterizations of sample E0 were conducted as follow. The typical SEM images of Sb2Se3 nanowires were shown in Fig. 3(a-c). The low magnification SEM image presented in Fig. 3(a) shows that the products are uniform and well-dispersive nanowires. The enlarged images (Fig. 3b-c) show that these nanowires have smooth surface and possess an average diameter of 100 nm and a length of several tens of micrometers. The EDX spectrum in Fig. 3(d) displays only strong Sb and Se peaks. The quantity analysis reveals that the atomic ratio of Sb:Se is quite close to 2:3, as expected. TEM image in Fig.

3(e) indicates the nanowire-like morphology of Sb2Se3, which agrees well with SEM results.

High-resolution TEM image (Fig. 3f) indicates the single crystalline nature of Sb2Se3 nanowires. Meanwhile Sb2Se3 nanowires are confirmed to be single crystals with the [001] growth direction. The corresponding selected area electron diffraction (SAED, inset of Fig. 3f) taken from this nanowire can be indexed as an orthorhombic Sb2Se3 single-crystal recorded from the [-111] zone axis. The superb crystal quality of Sb2Se3 nanowires is highly anticipated to lead to improved lithium storage performance.

During

Step I, Sb 3+ would hydrolyze in aqueous solution and form into stable antimony complex, which is determinable for the exactly stoichiometric Sb2Se3. If subjected to the higher ratio of EG, as exemplified in sample E15 and E20 (Fig. 1h and Fig. 1g), trace amount of antimony element can be detected in the resulted products. It is supposed that most of Sb-salts would be removed during centrifugation and sample washing process.

During

Step II, the reducing agent NaBH4 plays decisive roles through governing initial nucleation. It has been studied after NaBH4 dissolves in solution, once BH4 -is consumed and functions its ability to reduce metal salts into colloids, the pH values of solution tend to increase [34]. The pH value measurements demonstrated that the mixtures containing Sb-salts and Se-salts in EG-H2O were all acid solutions (Table S2). After the introduction of NaBH4, the pH value of E0, E5 and E10 mixture solutions was measured to be 10.2, 9.3 and 8.4 (i.e., all basic solutions). Whereas E15 and E20 mixture solutions were found to still remain acid suspensions with pH value of 5.7 and 4.8, respectively. The pH value changes suggest that in EG-excess mixture solution, such as E15 and E20 system, NaBH4 is unable to completely function its reducing ability [35]. Therefore in this way, only Se colloids could be obtained instead of Sb2Se3. that crystalline Sb2Se3 is difficult to be achieved at room temperature. However interestingly, XRD patterns of intermediate products synthesized from solution containing higher ratio EG display some diffraction peaks (Fig. S2d and Fig. S2e), suggesting that Se colloids were formed during this process even without microwave heating. SEM images demonstrated intermediate products synthesized from E0, E5 and E10 solution systems contained a lot of nanoparticles (Fig. S3a, S3b and S3c, respectively). Additionally, the product came from E15 solution system consisted of larger size irregular particles (Fig. S3d). Notably, intermediate product collected from pure EG system exhibit 1D rod structure (Fig. S3e), and corresponding point elemental analysis confirmed that the achieved products are almost Se (Fig. S3g), which is in agreement with previous XRD result. These results indicate that even at room temperature without microwave irradiation, hexagonal phase Se rods have already formed in EG-excess systems. Consequently it can be anticipated that excess amount of EG in the mixture solution prohibit NaBH4 completely reduce SeO3 2-into Se colloid neither contribute to form (SbxSey)n high-active colloids.

Lastly during a highly efficient microwave heating (Step III), water exhibits excellent microwave absorbance which would increase in the pressure, as it can be seen that an increase of ratio of H2O in the reaction system would lead to the increase of pressure (Fig. S4). When the solutions containing amorphous nanoparticles were irradiated by microwave, the heat could be easily transferred from the water to the (Sb2Se3)n amorphous nanoparticles due to its excellent heat conductivity [36], which could also induce a rapid temperature increase on the surfaces and contribute to the anisotropic growth of Sb2Se3 nanowires. (d) rate performances at various current densities; (e) long-term cycling performance at a current density of 1000 mA g -1 .

Since Sb2Se3 has been demonstrated to be a promising high-capacity anode for LIBs, the high quality of achieved single crystalline nanowires inspires the investigation of their electrochemical performance. To evaluate the lithium storage properties of Sb2Se3

nanowires, 2016-type coin cells were assembled using Li foil as the counter electrode. Fig. 5(a) displays cyclic voltammograms (CV) of Sb2Se3 nanowires during the first four cycles for LIBs. During the first cathodic scan, one sharp peak located at 1.38 V and a small peak around 1.25 V can be attributed to Li + intercalation into Sb2Se3 host and the conversion from Sb2Se3 to Sb particles, respectively; the peak observed at 0.77 V is resulted from the alloying process between Li + and Sb [7,11]. Upon the first anodic scan, three distinctive peaks located at 1.05, 1.36 and 2.02 V can be observed; specifically, the peak centred at 1.05

V corresponds to the dealloying process from Li3Sb to Sb, while the peak located at 1.36 V relates to the conversion reaction between Li2Se and Sb along with the formation of Lix Sb2Se3, and the peak at 2.02 V is attributed to the Li + extraction and the final formation of Sb2Se3. In the following three cathodic scans, three reduction peaks at 1.64, 1.38 and 0.84 V can be detected, and the voltage difference between the first cycle and the following cycles due to the formation of solid electrolyte interphase (SEI) film [37]. From the second scan onward, the reduction and oxidation peaks are almost unchanged, suggesting a highly reversible nature of alloying and conversion reactions during the charge/discharge and thus the outstanding reversibility of Sb2Se3 nanowires electrode. (based on 3 rd reversible capacity) at 100 mA g -1 , which is quite close to its theoretical capacity of 670 mAh g -1 . Notably, Sb2Se3 nanowire electrode is capable to display a capacity retention of 91.8% after 50 cycles, revealing its good stability and high reversibility.

The rate performance is then evaluated by charging/discharging at various current densities, as demonstrated in Fig. 5(d). The average reversible capacities of Sb2Se3 electrode are 638.2, 611.5, 543.6 and 472.3 mAh g -1 , at current densities of 0.1, 0.2, 0.5 and 1.0 A g -1 , respectively. Impressively, even a high capacity of 389.5 mAh g -1 can be still achieved when a current density of 2.0 A g -1 is applied. The rate capability is even competitive to the reported Sb2Se3 anode [7] including some carbonaceous material or doping modified Sb2Se3 electrodes [38][39][40]. Additionally, the discharge capacity can be recovered to 574.1 mAh g -1 when the current density comes back at 0.1 A g -1 , which indicates a good reversibility of as-prepared Sb2Se3 electrode. In order to disclose the intrinsic mechanism of good cycling performance of Sb2Se3 nanowire electrode, EIS measurement was carried out. As shown in Fig. 6, the EIS plot of Sb2Se3 nanowire electrode is composed with an inclined line at low frequency and two depressed semicircles at high frequency region. The inclined line is associated to the diffusion of Li ions in the bulk of the electrode material, and the diffusion coefficient value (D) of the Li ions can be calculated based on the equation: D=0.5(RT/An 2 F 2 σwC) 2 , where R is the gas constant, T is the temperature, A stands for the area of the electrode surface, n is the number of electrons transferred per mole of the active material involved in the electrochemical reaction, F is the Faradic constant, σw is the Warburg coefficient, and C is the molar concentration of Li ions [41,42]. The apparent Li ion diffusion coefficient for Sb2Se3 nanowires electrode is calculated to be 1.03×10 -10 cm 2 s -1 , suggesting the fast and efficient lithium diffusion process within the electrode. The first one observed in the medium-to-high frequency corresponds to the interfacial resistance between the electrode and electrolyte (Rf). The second semicircle appearing in the low frequency is due to the charge transfer resistance (Rct). And the bulk resistance (Rs) of Li-Sb2Se3 battery is the intercept at the real axis [43,44]. After simulation, the values of Rs, Rf and Rct were determined to be 1.4, 8.6 and 31.6 Ω, which confirm the excellent electrical conductivity and the good interfacial contact of SEI film formed in the cell of Li-Sb2Se3, thus resulting in its long-term cycling stability and good rate performance.

Conclusions

In summary, this work offers a rapid, green, inexpensive and highly reproducible approach for the synthesis of uniform single crystalline Sb2Se3 nanowires. The achieved uniform Sb2Se3 nanowires are single crystalline along [001] growth direction with a diameter of 100 nm and a length up to tens of micrometers. Through merely varying the volume ratio of solvents in a simple EG/H2O solvent system, Sb2Se3 nanowires and Se microrods can be easily achieved. Significantly, when evaluated as an anode of LIBs, Sb2Se3 nanowires can deliver a high reversible capacity of 650.2 mAh g -1 at 100 mA g -1 and a capacity retention of 63.8% after long-term 1000 cycles at 1000 mA g -1 , as well as superior rate capability (389.5 mAh g -1 at 2000 mA g -1 ), which show great potential as a promising candidate for high-performance LIBs. At last we believe our facile strategy can be easily extended towards the preparation of other nanoscale advanced nanomaterials for energy storage and conversion.

Fig. 1

 1 Fig. 1 SEM images of (a) sample E0, (c) sample E5, (e) sample E10, (g) sample E15, (i) sample E20 and the corresponding XRD patterns of (b) sample E0, (d) sample E5, (f) sample E10, (h) sample E15, (j) sample E20. DSC and TGA measurements were carried out to identify the composition of as-prepared samples. As clearly demonstrated in Fig. S1, samples E0, E5 and E10 mainly contain large portions of Sb2Se3. The amounts of Se colloids in the E0, E5 and E10

Fig. 3

 3 Fig. 3 SEM images (a-c), EDX spectrum and quantity analysis (d), TEM images (e, f) and select area electron diffraction (SAED) result (inset of Fig. 3f) of Sb2Se3 nanowires.

Fig. 4

 4 Fig. 4 Schematic illustration of synthesis procedure and formation mechanism of Sb2Se3 nanowire via solvent-mediated microwave irradiation.

Fig. 5

 5 Fig. 5 Lithium storage performances of Sb2Se3 nanowires electrode: (a) Cyclic voltammogram curves at a scan rate of 0.1 mV s -1 ; (b) first three charge-dischaege profiles at a current density of 100 mA g -1 ; (c) cycling performance at a current density of 100 mA g -1 ;

Fig. 5 (

 5 Fig. 5(b) shows the typical charge-discharge voltage profiles of Sb2Se3 nanowires for the first three cycles at a current density of 100 mA g -1 . The electrode provides a discharge and charge capacity of 846.5 and 632.2 mAh g -1 , respectively, corresponding to an initial Coulombic efficiency (CE) of 74.7%. Besides, the discharge profiles are characterized by a inclined plateau around 1.65 V (Li + intercalation) and two flat plateaus at 1.40 V (conversion reaction) and 0.84 V (alloying reaction), which are well in accordance with the above CV results. Fig. 5(c) demonstrates the cycling performance of Sb2Se3 nanowires at a current density of 100 mA g -1 . Sb2Se3 electrode delivers a reversible capacity of 672 mAh g -1

Fig. 5 (

 5 e) shows the long-term cyclability of Sb2Se3 nanowires at a relative high current density of 1000 mA g -1 . Notably, the Sb2Se3 electrode exhibits remarkable ultra-long cycling stability. It is to be pointed out that the as-fabricated Sb2Se3 nanowire electrode is lack of any carbon buffer layer modification, thus it is reasonable to observe the slight capacity fade during long-term cycle which is due to the surface reconstruction of Sb2Se3 nanowire and SEI film. The CE gradually increases during initial several cycles and then stabilizes around 99% in the upcoming long-term cycles. After 1000 cycles, the Sb2Se3 electrode can still deliver a reversible capacity of 260.8 mAh g -1 . The above electrochemical tests reveal that the as-synthesized high-quality single-crystalline Sb2Se3 nanowires exhibit great promise towards lithium storage.

Fig. 6

 6 Fig. 6 EIS plot and the corresponding fitting curve of Sb2Se3 electrode (inset are the equivalent circuit and a summary of simulation results of resistance value).

Acknowledgments

This work was supported by the National Key Research and Development Program of China (2016YFA0202603), the National Basic Research Program of China (2013CB934103), the National Natural Science Foundation of China (51521001, 51602239), the National Natural Science Fund for Distinguished Young Scholars (51425204), Yellow Crane Talent (Science & Technology) Program of Wuhan City and the Fundamental Research Funds for the Central Universities (WUT: 2016III001, 2016III003, 2016IVA090) and the Programme of Introducing Talents of Discipline to Universities (B17034). We acknowledge the funding support from the Lorraine Region (now part of Grand Est Region) Cooperation Research Lorraine/Hubei Program 2015/2017.

Graphical abstract

Single-crystalline Sb2Se3 nanowire or Se rod were produced during 0.5 h solvent-mediated microwave synthesis. As-prepared Sb2Se3 nanowire exhibits long-life stability and superior rate ability as an anode for lithium-ion battery.


Ilyes Abid

Khaled Guesmi

Stéphane Goutte

Christian Urom

Julien Chevallier
email: julien.chevallier@ipag.fr

Commodities risk premia and regional integration in gas-exporting countries

Keywords:

This study examines the fundamental driving forces of stock market integration with particular emphasis on major Gas-Exporting Countries (hereafter known as GECs), namely the United Arab Emirates, Qatar, Venezuela, Saudi Arabia, Russia, and Kuwait, over the period from June 2003 to November 2017. The novelty of our study stems from the fact that we examine a dynamic process of international, regional and national stock markets integration using a set of local, regional, global and commodities as driving forces of integration. Particularly, we measure market integration using gas price as a common source of risk in addition to the world, regional and domestic sources of risk based on a conditional version of the International Capital Asset Pricing Model (ICAPM). Our study also differs from past ones in that we investigate the integration of stock markets into the international market as well as in the GECs countries. Our results show that the level of market integration of the major gas-exporting countries varies widely over time and depends on the interest rate spread, the level of market openness and market volatility. It also seems to be still significantly segmented from both the global and GECs markets. Gas risk represents a small part of the global risk in all the countries considered in this study.

Introduction

There is strong evidence that the international economic landscape has changed since the 1980s due to the liberalization of financial markets. We can enumerate a long list of financial crises, such as the crash of the U.S. equity market in 1987, the loan collapse and credit crisis in the end of 1990, the financial crisis in Asia region in 1997, the Enron incident in 2002, the 2007 sub-prime crisis in the U.S. and the 2008 sub-prime crisis. Notably, the sub-prime crisis of mid-2006 was followed by an upsurge of crises and a sharp decline in housing prices in other economies and caused the global financial crisis in 2007. The global financial crisis initiated with the bursting of the U.S. housing crises and has caused some new questions about probable susceptibility to contagion effects. The U.S. tremors and shocks play a better role, as its economy is the largest in the world, and its propagation throughout the world. It seems that the global financial crisis has spread to other parts of the world. There seems to be a significant degree of co-movement between different and distant countries. These almost parallel developments provide evidence in favor of a significant correlation across national markets and approve that the U.S. market continues to retain its place as the world's first leading economy.

This paper mainly examines the dynamics of gas-exporting countries' stock market integration into the world market by developing an International Capital Asset Pricing Model (ICAPM), while taking into account gas prices as a potential source of risk. More precisely, we consider an augmented asset-pricing model to analyze the dynamics of equity market integration from 2003 to 2017 conditioned by domestic, regional and global information variables. This issue is of paramount importance because modeling the risk exposure of equity market only by its interdependence with the international market portfolio in the context of an ICAPM might not be free of bias. The main reason is that domestic equity markets are not fully integrated into the regional international markets and that the level of market integration is continuing and may be reversed, and more importantly, its determinants are not known in advance. In what follows, we examine the dynamics of gas-exporting countries' stock market integration into the world market. This study departs from that of [START_REF] Bekaert | Time-varying world market integration[END_REF] and [START_REF] Carrieri | Characterizing world market integration through time[END_REF] by developing an international capital asset pricing framework which takes into account gas price as a potential source of risk.

We take the stance to consider gas prices as a potential source of risk, by arguing that the natural gas price has a potential impact on financial market integration. Following the North American shale gas boom around 2005, the influence of natural gas on the global economy has become indeed very substantial. Ji et al. (2018a,b,c,d,e, forthcoming-a,f) suggest that the importance of natural gas has also proliferated in the recent past given the increasing concern for energy security and the desire for cleaner energy sources due to climate change. However, as the number of its main importers such as Japan and China is rising, the socio-economic, as well as political realities in the main exporters such as USA and Russia, have created uncertainties in the international energy market [START_REF] Zhang | Further evidence on the debate of oil-gas price decoupling: a long memory approach[END_REF]. These uncertainties which mainly revolves around gas pricing as an essential market signal has attracted a growing body of empirical studies seeking to unravel what drives natural gas prices as well as the interaction between natural gas and international asset prices (see e.g. [START_REF] Acaravci | Natural gas prices and stock prices: evidence from EU-15 countries[END_REF]Ji and Zhang, forthcoming;Ji et al., 2018a,b,c,d,e, forthcoming-a,f).

The Gas Exporting Countries Forum (GECF) was created in 2001 with headquarters in Doha and currently has twelve members (as listed in the comparative table with OPEC in the Appendix). The forum meets the same objectives as OPEC, i.e., strengthening the trading of gas in order to defend the interests of natural gas exporters globally. The GECF allows its members to anticipate the evolution of the natural gas market collectively and develop the exploitation of natural gas in an economically and environmentally cost-efficient way. However, unlike the OPEC, the GECF does not consider itself a cartel, given that it does not enforce production quotas on its members. At a glance, we notice that this forum gathers two-thirds of proven reserves of natural gas, including the three leading producers of natural gas in the world (Iran, Russia, and Qatar). As Liquefied Natural Gas (LNG) producers, the USA is not a member of the forum while seven other countries 1 are given observatory roles in the forum. Given the intrinsic characteristics of the GECF region, it appears interesting to investigate its dynamics.

The remainder of the article is organized as follows. Section 2 contains the literature review and our contribution. Section 3 presents the empirical approach we use to identify the determinants of financial integration and examine the dynamics of regional financial integration for our sample markets. Section 4 describes the data and their statistical properties. Section 5 reports and discusses the obtained results. Section 6 provides some concluding remarks.

Related literature

Prior empirical studies agree that the level of stock market integration varies over time (see, e.g., [START_REF] Bekaert | Time-varying world market integration[END_REF][START_REF] Carrieri | Characterizing world market integration through time[END_REF]Guesmi andNguyen, 2011, 2014). Empirical results are, however, divergent regarding the identification procedure of determinant factors of integration. (Bekaert andHarvey (1995, 1997), Adler and[START_REF] Adler | Mexico's integration into the North American capital market[END_REF][START_REF] Hardouvelis | EMU and European stock market integration[END_REF] arbitrary selected financial and macroeconomic variables to model the logistic function of market integration, while after the estimation of the degree of 1 Namely: Angola, Azerbaijan, Iraq, Kazakhstan, Norway, Oman, and Peru. stock market integration, [START_REF] Carrieri | Characterizing world market integration through time[END_REF] identify 'ex-post' the determinant factors of financial integration. More recently [START_REF] Guesmi | Time-varying regional integration of stock markets in Southeast Europe[END_REF] identify the financial integration determinants in four major markets in Southeast Europe by introducing the candidate factors of market integration one by one in the logistic function. As [START_REF] Bhattacharya | The world price of insider trading[END_REF], they impose the estimation results from the multivariate GARCH to guarantee, for all individual markets, the same prices of risk related to regional market and exchange rates. The method proposed by [START_REF] Guesmi | Time-varying regional integration of stock markets in Southeast Europe[END_REF] may be subject to criticism because it introduces information variables to assess financial integration one by one. This procedure leads to some loss of efficiency because the process does not consider the interaction of all variables considered.

More so, whereas the influence of oil price on stock markets of regional markets through different channels including returns and volatility has been well documented in recent times (see e.g. [START_REF] Sadorsky | Risk factors in stock returns of Canadian oil and gas companies[END_REF][START_REF] El-Sharif | Evidence on the nature and extent of the relationship between oil prices and equity values in the UK[END_REF][START_REF] Basher | Oil price risk and emerging stock markets[END_REF][START_REF] Park | Oil price shocks and stock markets in the U.S. and 13 European countries[END_REF][START_REF] Apergis | Do structural oil-market shocks affect stock prices?[END_REF][START_REF] Chen | Do higher oil prices push the stock market into bear territory?[END_REF][START_REF] Arouri | Return and volatility transmission between world oil prices and stock markets of the GCC countries[END_REF][START_REF] Arouri | On the relationship between world oil prices and GCC stock markets[END_REF][START_REF] Zhang | Oil shocks and stock markets revisited: measuring connectedness from a global perspective[END_REF][START_REF] Gomez-Gonzalez | Dynamic relations between oil and stock markets: volatility spillovers, networks, and causality[END_REF]Ji et al., 2018a,b,c,d,e, forthcoming-a,f). Other studies have examined the relationship between energy prices by linking oil, coal and natural gas prices with the stock market (e.g. [START_REF] Wei | Energy, the stock market, and the putty-clay investment model[END_REF][START_REF] Scholtens | Stocks and energy shocks: the impact of energy accidents on stock market value[END_REF][START_REF] Gatfaoui | Linking the gas and oil markets with the stock market: investigating the US relationship[END_REF]Ji et al., 2018a,b,c,d,e, forthcoming-a,f) and with emphasis on the stock prices of energy companies (e.g. Sardorsky, 2001;[START_REF] Oberndorfer | Energy prices, volatility, and the stock market: evidence from the Eurozone[END_REF]Ji and Zhang, forthcoming). Attention has also been given to the connectedness and spillover between oil and natural gas prices (see e.g. [START_REF] Ewing | Volatility transmission in the oil and natural gas markets[END_REF][START_REF] Brown | What drives natural gas prices?[END_REF][START_REF] Hartley | The relationship of natural gas to oil prices[END_REF][START_REF] Ramberg | The weak tie between natural gas and oil prices[END_REF][START_REF] Atil | Asymmetric and nonlinear pass-through of crude oil prices to gasoline and natural gas prices[END_REF][START_REF] Zhang | Further evidence on the debate of oil-gas price decoupling: a long memory approach[END_REF]Zhang et al., 2018).

However, despite the growing influence of natural gas on the international energy market, to our best knowledge, only the study by [START_REF] Acaravci | Natural gas prices and stock prices: evidence from EU-15 countries[END_REF] has examined the long run relationship between gas prices and stock prices for 15 European Union countries from the first quarter of 1990 to the first quarter of 2008. Their empirical findings show a long-term relationship between natural gas prices, industrial production, and stock prices in Austria, Denmark, Finland, Germany, and Luxembourg. However, no relationship was found between these variables for Belgium, France, Greece, Ireland, Italy, Netherlands, Portugal, Spain, Sweden, and the UK. The Granger Causality test indicates an inverse relationship between the two variables. The focus on natural gas prices is also particularly desirable given that gas markets are primarily regional and a good understanding of its linkages with the stock markets will guide policymakers' decisions and could have a profound influence on the process of regional financial integration.

Our study contributes to the expanding literature on the interaction between energy and the financial markets by examining the time-varying stock market integration of major Gas-Exporting Countries (hereafter known as GECs), as well as their determinants in the context of a dynamically integrated ICAPM. We seek to find the level of market integration using a set of global and local information variables. To do this, we integrate the price of gas as part of the global risk premium, and we decompose the total risk premium to dictate the contributions of gas risk to the total risk premium. Our study also contributes to the literature on energy finance by examining the dynamic level of international and regional market integration among GECs. Unlike previous studies, we first attempt to identify ex-ante the driving forces behind the integration process of national stock markets using a set of local, regional, global variables and commodities in a dynamic factor model. We also consider in addition to the systematic risks associated with regional and local markets, currency risk which according to theories of international asset pricing models is a relevant factor in pricing equity markets [START_REF] Adler | International portfolio selection and corporation finance: a synthesis[END_REF][START_REF] Pavlova | Asset prices and exchange rates[END_REF][START_REF] Carrieri | Characterizing world market integration through time[END_REF][START_REF] Tai | Market integration and contagion: evidence from Asian emerging stock and foreign exchange markets[END_REF], Ji et al., 2018a,b,c,d,e, forthcoming-a,f).

Empirical strategy

Our model creation stems from an augmented version of the asset price model by [START_REF] Bekaert | Time-varying world market integration[END_REF] where the excess return E t-1 (R i,t ) issued in country i, conditionally on a set of information available to investors up to time t -1 is given by:

E t-1 (R i,t ) =y w t-1 d w t-1 Cov (R i,t , R w,t | Y t-1) +d p t-1 Cov (R i,t , R P,t | Y t-1) +d G t-1 Cov (R i,t , R G,t | Y t-1) + L k =1 d k,t-1 Cov (R i,t , R k,t | Y t-1) ⎤ ⎦ + (1 -y w t-1 )d i,t-1 Var t-1 (R i,t | Y t-1)
(1)

Let:

y W t-1 = exp (-| a 0 + a i F i,t-1 |) d W t-1 = exp(k w F w,t-1 ), d k,t-1 = exp(k k F w,t-1 ), d P t-1 = exp(k P F w,t-1 ), d G t-1 = exp(k G F w,t-1 ), d i,t-1 = exp(k i F i,t-1 ). E t-1 (R i,t
) is the excess return in country i, conditionally on a set of information Y t-1 available to investors up to time t -1. Cov t-1 is the conditional covariance. y w t-1 is the conditional probability of transition between segregation and integration states, which falls within the interval of [0,1]. In other words, this is a conditional measure of integration of market i respectively into the world market.

This conditional probability depends on integration factors. If y W i,t-1 = 0, the unique source of symmetric risk is the variance, and the pricing relationship in a strictly segmented market applies. If y W i,t-1 = 1, only the world covariance risk is priced. So the strict world segmentation hypotheses are rejected.

The loading variables d W t-1 refers to the conditionally expected price of covariance risk and d i,t-1 refers to the conditionally expected local price.

d P t-1 Cov(R i,t, R p,t /Y t-1
) takes into account the conditional covariance between the security r return and the rewards for GECF region, that is R p,t .

d G t-1 Cov(R i,t, R G,t /Y t-1
) takes into account similar to the previous one between the security r return and the rewards for Gas prices risk, that is R G,t . These three loading variables depend on respectively the vector of international information (F W,t-1 , the vector of domestic information (F i,t-1 , at the time (t -1).

Given that the natural gas market which facilitates the trading of natural gas as a commodity has region-specific characteristics and that the deregulation of the intermediaries and spot markets makes it even more complex, we demonstrate the suitability of our model by linking the local stock markets with the price of gas. Gas price risks are managed through the trading of gas contracts in the financial gas market. Also, since the region is composed of the countries that export gas, our model also incorporates the covariance of the stock markets with the international market, as well as the covariance of the region with the international market. Therefore, we argue that gas markets can have a broader international appeal, rather than a purely regional approach.

The subindex k denotes the currencies of GECF countries. This term includes the covariance with the return on the exchange rate of the currency of country k, denoted by the variance R k,t , against the currency of the reference country. d k,t-1 expresses the expected price of the exchange rate risk for currency k, conditionally on the information available up to t -1. L is respectively the number of GECF markets included. We consider the U.S. as the reference country. Finally, V ar t-1 (R i,t ) is the conditional variance of the national market return.

The fundamental assumptions of this model, as in [START_REF] Carrieri | Characterizing world market integration through time[END_REF], are that the continually changing world market structure (i.e., from the state of strictly segmented markets to that state of full integration), as well as the increasing availability of substitute assets, is well captured. The model also assumes the existence of cross-border risk preferences, as well as costs/barriers on the free flow of capital, that is widely accepted as a significant contributor to imperfectly integrated global capital markets. Lastly, it also assumes consistency with the well-known phenomenon of home asset bias.

Eq. ( 1) can be written as a risk premium decomposition as follows:

RP T i,t = RP W i,t + RP L i,t + RP G i,t + RP P i,t + RP K i,t (2)
where RP w i,t and RP L i,t are the world and local premium respectively. RP P i,t and RP G i,t are the GECF region risk and Gas premium respectively. Under the hypothesis of rational expectations, the econometric specification of Eqs. ( 1)-( 2) can be further characterized by the following system of Eqs. ( 3) to ( 6). More precisely, the econometric specification of the model to be estimated, i.e., Eqs. ( 3), (4), and ( 5), is characterized by the following system of equations:

rW,t = d W t-1 h W,W,t + d p t-1 h i,W,t + d G t-1 h i,G,t + L k=1 d k,t-1 h W,k,t + e W,t (3) rk,t = L k =1 d i,k,t-1 h i,k,t + e k,t (4) ri,t =y W t-1 d W t-1 h i,W,t + d p t-1 h i,W,t + d G t-1 h i,G,t + L k=1 d k,t-1 h i,k,t + 1 -y W t-1 d i,t-1 h i,i,t + e i,t (5)
e t | Y t-1 ∼ N(0, H t );
where e t = [e w,t , e k,t , e i,t ] .

(6) rt refers to the vector of excess returns which are assumed to be normally distributed. Also, e t is a vector of unexpected excess returns given the set of information, and H t is a conditional variancecovariance matrix of excess returns following a Multivariate DCC-GARCH process with H t = D t R t D t .R t is the symmetric matrix of dynamic conditional correlations. D t is a diagonal matrix of conditional standard deviations for each of the return series, obtained from estimating a univariate GARCH process.

At the empirical level, we consider N exchange rate specifications. Since we consider the U.S. as the reference country, we use bilateral real exchange rates to measure the exchange value of local currencies against the U.S. dollar. This specification, being the baseline, permits us to compare our results with those of existing studies. We then use the bilateral real exchange rates expressed in the currency of the largest country in the region regarding GDP. Finally, the real effective exchange rate indices are employed. These different exchange rate specifications enable us to check the robustness of the findings and to justify our choice of reference currency.

The estimation of the vector of unknown parameters is carried out by the Quasi-Maximum Likelihood Estimation (QMLE) method as proposed by [START_REF] Bollerslev | Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances[END_REF] to avoid the problem of non-normality in excess returns. Given the highly nonlinear structure of the model and the large unknown parameters number, the simultaneous estimation of the model is not feasible. So, we follow the prevailing literature, i.e., [START_REF] Hardouvelis | EMU and European stock market integration[END_REF], and [START_REF] Guesmi | How strong is the global integration of emerging market regions? An empirical assessment[END_REF] to estimate the system from Eqs. ( 4) to (6) in two steps. In the first stage, we estimate a subsystem of equations for excess returns on regional and individual markets, and exchange rates plus the relevant variance-covariance elements. This step allows us to obtain the conditional variances of the regional market and real exchange rate, their conditional covariances as well as the prices of the regional market and exchange rate risks. In the second stage, we estimate the price of local market risk and the time-varying level of integration for each market.

Data

The dataset includes six stock market indices (the United Arab Emirates, Qatar, Venezuela, Saudi Arabia, Russia, and Kuwait), exchange rates indices and the Gas index. The data are collected over the period from June 2003 to November 2017. All the data are extracted from Thomson Datastream International. We use monthly stock returns in excess of the one-month Eurodollar interest rate, which is considered as a risk-free rate. Monthly stock returns are calculated from stock market indices with dividends reinvested. We resort to monthly industrial production for the six countries (as a GDP proxy). Real exchange rates represent the value of the local currency against the U.S. dollar and are extracted from the IMF's International Financial Statistics (IFS) and the U.S. Federal Reserve databases. The real effective exchange rate index is the geometric average of bilateral real exchange rates among the countries under consideration. Our choice of the local, regional and global instrumental variables set of information variables is centered upon empirical studies which pioneered the model used for this study. Our model is pioneered in [START_REF] Bekaert | Time-varying world market integration[END_REF] and [START_REF] Carrieri | Characterizing world market integration through time[END_REF]. These studies used the following set of information local, regional and global information variables such as the U.S. term structure, U.S. treasury bills, world dividend yield, change in exchange rate, local equity return and the risk-free rate, Euro-dollar rate and the ratio of equity market capitalization to GDP. Following these previous studies, we select the following instrumental variables to explain changes in the prices of risks:

• Regional instrumental variables: the dividend yield of the GECF region in excess of the 30-day Eurodollar interest rate (DYR), the return of the GECF market index (RMI). • Financial market integration factors: A set of candidate factors that cause the movements in the degree of regional financial integration is chosen based on the findings of previous studies (e.g., Bekaert andHarvey, 1997, 2000;[START_REF] Bhattacharya | The world price of insider trading[END_REF]. Previous empirical results (e.g., [START_REF] Errunza | Further evidence on the benefits of portfolio investments in emerging markets[END_REF][START_REF] Bailey | Benefits of international diversification: the case of Pacific Basin stock markets[END_REF][START_REF] Errunza | Tests of integration, mild segmentation and segmentation hypotheses[END_REF][START_REF] Divecha | Emerging markets: a quantitative perspective[END_REF][START_REF] Bekaert | Diversification, integration and emerging market closed-end funds[END_REF][START_REF] Phylaktis | Stock market linkages in emerging markets: implications for international portfolio diversification[END_REF]) show that, whatever the specification of exchange rate risk, the interest rate spread (SWAY), the level of market openness (OPEN) in each region and The market volatility (denoted VIX) are commonly selected as the relevant factors affecting the regional integration process of the markets under consideration.

Table 1 reports the main statistics of return series for the stock market, real exchange rate and gas for all the countries in the sample. All the series depart from normality conditions and conditional heteroscedasticity. The skewness coefficients are significantly different from zero for almost all markets, indicating the presence of asymmetry in the return distribution. Besides, all the return series are characterized by a kurtosis coefficient statistically significant and greater than 3, and thus have fatter tails than those of a normal distribution. [START_REF] Engle | Autoregressive conditional heteroskedasticity with estimates of the variance of U.K Inflation[END_REF] test for the 1st order of conditional heteroscedasticity is also performed, and we cannot reject the hypothesis of no ARCH effects for all return series considered. This result motivates our choice of GARCH modeling approach for conditional variance processes.

Results and discussion

Empirical estimates

As discussed above, we first estimate the system composed of Eqs. ( 1) to ( 6) for excess returns on the regional and international markets, gas, and returns on real exchange rates.

In Table 2, Panels A and B present the estimated parameters for the price of gas and international market risk respectively. The price of gas risk is positively correlated with IRENT and positively with RSP (both coefficients being significant at the 1% level), confirming the co-movement between oil and stock markets, on one side, and diversification opportunities, on the other. The price of international market risk also presents a positive relationship both with IRENT and RSP (in the two cases, the coefficient are significant at 1% level).

Turning out to the analysis of gas-exporting countries (Panel C), the coefficients associated with the world market portfolio, the S&P 500 index, and the coefficients associated with the U.S. term premium has a significant effect on the evolution of the price of the exchange rate.

Panel D shows that the coefficients associated with the dividend yield of the GECF region in excess of the 30-day Eurodollar interest rate (DYR) and the return of GECF market index (RMI) are significant at the 1% level. These variables have a significant positive effect on the evolution of the price of GECF market risk.

In order to examine the relevance of the risk price in the valuation of financial assets issued by the GECF markets, we use the robust Wald test (Table 3) to check the nullity of the coefficients associated with the information variables. Consequently, the results from the Wald test reject the hypotheses according to which the risk prices are individually equal to zero. In parallel, the assumptions of the constant world, gas, exchange, and regional risk prices are rejected for the considered markets. These findings are consistent with those of previous studies, including that of [START_REF] Carrieri | Characterizing world market integration through time[END_REF], [START_REF] Tai | Market integration and contagion: evidence from Asian emerging stock and foreign exchange markets[END_REF], and Guesmi and Nguyen (1995), in the sense that the local risk is a relevant source of risk in the valuation of financial assets issued by GECF markets.

There is a common perception that gas prices, world market, and exchange rate risks influence asset pricing in emerging markets and that they hold profound economic implications in these economies including the GECS countries. For instance, it has become widely accepted that rising gas prices and its volatility (just like oil prices) suppress economic activity and reduce asset prices. [START_REF] Sauter | Oil price volatility and economic activity: a survey and literature review[END_REF] argue that energy price shocks reflect a risky obligation for energy consumers to the extent that its prices correlate negatively with economic activity such that peoples' income and asset value decline following a rise in energy prices. On the other hand, gas price risks present severe macroeconomic outcomes for gas exporters like the GECF countries. The price risks of gas impacts on the fiscal and monetary measures of these economies through volatility in government earnings and cost-push inflationary pressure.

Further, there are several channels through which exposure to the world market and exchange rate risks impact on the economic outcome of emerging markets. Increasing integration with the global financial markets implies a more rapid international transmission of risks resulting in changes in cash flows and current account imbalances. For instance, [START_REF] Granger | A bivariate causality between stock prices and exchange rates: evidence from recent Asian flu[END_REF] note that as stock markets become increasingly integrated with the global market, changes in stock prices result in both capital movements across markets and current account imbalances. On the other hand, exchange rate risks directly influence the international competitiveness of firms given their impact on input and output prices [START_REF] Nathan | Modelling the impacts of interest rate and exchange rate changes on UK stock returns[END_REF]. Thus, note that exchange rate volatility entails real economic costs, affects prices, firms' profitability as well as the general financial stability [START_REF] Benita | Policy factors and exchange rate volatility[END_REF], holding both positive and negative implications in both export-dominant and import-dominant economies [START_REF] Ma | On exchange rate changes and stock price reactions[END_REF].

We, therefore, decide to use these factors to estimate the degree of regional integration. This restriction allows us to avoid the numerical convergence problem when the number of explanatory variables for integration is above two. In practice, we have attempted to estimate our model with different numbers of market integration determinants, but the specification with two factors (trade openness and local market development) does not face convergence problem and provides the best fit to the data as witnessed by the AIC, BIC and Log-likelihood criteria.

Table 4 reports the obtained results as well as the basic integration measure statistics estimated for the six countries. Overall, the results confirm the identification of driving factors for integration as its dynamics is significantly explained by the selected variables whatever the market is.

Further, Table 4 shows our empirical estimation of the degree of integration using interest rates spread, level of market openness and market volatility show that these factors are relevant in the process of regional integration of the GECS. The result shows that these factors are significant across all the countries except the measure of market openness for the United Arab Emirates and Qatar. This result holds some economic implications for these economies. First, the degree of market openness indicates that the stock markets are increasingly becoming open within the region such that stocks and equity capital can smoothly flow within the markets. However, this also implies that idiosyncratic risks can easily be transmitted to the regional market, thus influencing macroeconomic stability in the region. For instance, stock prices have been found to relate to key macroeconomic factors including consumer prices, growth, and industrial production. For instance, [START_REF] Gilchrist | Credit market shocks and economic fluctuations: evidence from corporate bond and stock markets[END_REF] found that shocks emanating from the bond markets influence key economic variables such as inflation, interest rate, and other financial indicators, causing the significant and persistent contraction to economic activity.

Secondly, the market volatility and interest rate spread are significant across all the economies considered. This shows that these are influential factors that account for the degree of market integration in the GECS region. This implies that these economies face a high risk of macroeconomic volatility following the transmission of shocks both from global and regional sources through their capital markets. Volatility has been variously documented to influence growth negatively, reducing economic welfare, especially, in emerging economies. [START_REF] Loayza | Macroeconomic volatility and welfare in developing countries: an introduction[END_REF] opine that high macroeconomic instability in emerging economies is caused by several factors, notably large external shocks, and that volatility represents a direct welfare cost for risk-averse individuals and an indirect cost through its negative influence on income growth and development.

Lastly, interest rate spread also influences the process of regional integration and is significant across all the economies. Interest rate spread represents the difference between the interest paid by banks to depositors and the interest paid to the bank by the borrowers. The spread largely influences the process of regional financial market integration and impacts upon domestic economies within the region. For instance, although it may not imply utterly perfect mobility of capital within a region, competition lowers the interest differentials and improves access to capital to the extent that domestic monetary policy becomes less effective in the management of interest rates. [START_REF] Were | What factors drive interest rate spread of commercial banks? Empirical evidence from Kenya[END_REF] note that as a benefit of financial integration, narrowing interest rate spread reflects critical domestic issues including the efficiency of the financial intermediaries, profitability, monetary policy impacts and the overall level of financial development.

Fig. 1 presents the time-path of our measure for the level of financial market integration (estimates and Hodrick-Prescott filtered values) among the gas-exporting countries in our sample over the period from 2003 to 2017. The trend suggests that the degree of international integration varies over time and differs from one country to another, depicting the divergence in monetary and economic policies of these countries. The degree of market integration was at the highest level in Saudi Arabia, the United Arab Emirates and Qatar (0.4) followed by Kuwait (above 0.3) in 2015. It was highest in Venezuela (0.15) in 2011 and Russia (0.25) in 2004-06. In about all the countries, a downward trend is discernable following a sharp fall in the level of market integration in 2007, but this was followed almost instantly by a quick rise throughout 2008 to 2009. This pattern can also be observed in most of the countries between 2013 and 2015. Lastly, the degree of market integration displays a downward trend since 2016 in all countries. The values found for gas-exporting (e.g., GEC) countries in this study are typically lower than that documented by [START_REF] Créti | International CAPM and Oil price: evidence from selected OPEC countries[END_REF] regarding the integration of oil-exporting (e.g., OPEC) countries into world markets. 2Table 5 reports average values of the total, the global, regional, gas and local risk premiums. The two-sided Student-t test indicates that different risk premiums are significantly different from zero at the 1% level for all the markets considered. Saudi Arabia has the highest total risk premium (13.365%), followed by Venezuela (10.952%), Kuwait (9.485%), the United Arab Emirates (8.464%) and Qatar (6,547%). International risk premiums are on average higher than local and regional premiums for all markets. The share of local risk premium over the total risk premium ranges from 10% for Russia to 25% for the United Arab Emirates. Gas risk represents a small part of the global risk for all countries. Gas risk represents however only 10% of total risk.

The risk premium represents an incremental return required by stockholders for choosing risky stocks instead of risk-free or average risky securities [START_REF] Dimson | Global evidence on the equity risk premium[END_REF]. [START_REF] Harris | The market risk premium: expectational estimates using analysts' forecasts[END_REF] note that it is a crucial factor in asset allocation, helping portfolio managers in the determination of the optimal portfolio mix of debt and equity and asset pricing. The fundamental assumption in financial analysis suggesting that as long as there is sufficient quality data, extrapolation based on historical risk premium always gives an unbiased estimate of future premium makes the understanding of risk premium very critical in portfolio management. The decomposition of total risk premium in our result above suggest that the risks premium of the GECF have high significant global and regional influence. The implication is that stock markets in these economies are becoming increasingly integrated, both within the region and with the global markets. Hence, their stocks are being traded in the regional capital markets, and therefore belong to internationally integrated markets.

Policy implications

To summarize the gist of our results, we document that the level of market integration among the major gas-exporting countries depends on the interest rate spread, the level of market openness and market volatility. Also, gas-exporting countries seem still significantly segmented from both the global and GECs markets. Overall, the specific gas risk represents a small part of the global risk in all the countries considered in this study.

Risk premium analysis is particularly relevant in the GECF and other emerging markets for several reasons including that it is the driver of equity returns and it determines the cost of capital. This is because the process of market integration often occurs with lower expected returns, thus reducing the cost of capital [START_REF] Bekaert | Time-varying world market integration[END_REF]. Interestingly, increasing market liberalization in emerging economies implies that portfolio managers are faced with improved risk-sharing opportunities. Given that risk drives investment returns, a portfolio mix of debts and stocks from the regional and global capital markets could improve portfolio performance. This corroborates with the critical factor behind portfolio management that diversification with foreign and regional securities that contain assets that have low correlation substantially reduces portfolio risk. A low-risk tolerant portfolio mix would, however, include more debt and equity from the domestic financial markets with similar returns, thus reducing the exposure of the portfolio to regional and global risks. This is because the portfolio theory argues that investors are risk-averse. Our empirical results reveal that the level of market integration of significant gas-exporting countries varies widely over time and depends on the interest rate spread, the level of market openness and market volatility. Besides, we find that gas risk constitutes a small part of the global risk in all countries. The implication is that gas-exporting countries are still primarily segmented from the world and the regional markets.

We would express optimism that our findings have profound implication for policy making in these countries as well as in the process of regional integration. Particularly, given the lack of information about institutional reforms or data that accurately captures its effects especially for countries like Venezuela and Russia as at the time of writing this paper, we would recommend that future research should take into account the impact of institutional reforms aimed at lowering barriers to portfolio flows within the region.

For investors, understanding the interaction between gas prices and the stock market is particularly important as it would guide market decisions concerning the trading of financial assets especially gas contracts. Besides, given the risk associated with the increasing financialization of commodities, these findings would be helpful in risk management and portfolio diversification strategies using gas commodity and its derivatives.

For regional policy makers in GECF, these findings are significant in the pricing of natural gas given that the GECF influences the market for gas. Our findings would also be valuable in the process of policy formulation towards regional integration of gas exporters. Generally, our research offers an innovative and universal perspective for considering the integration process and the transmission mechanisms of risks within the region and across the global market. However, given the lack of information about institutional reforms or data that accurately captures its effects especially for countries like Venezuela and Russia as at the time of writing this paper, we would recommend that future research should take into account the impact of institutional reforms aimed at lowering barriers to portfolio flows within the region.

Conclusion

In this paper, given the growing influence of natural gas price in the energy market and its implications for the stock markets of significant gas exporters and the potential impact on financial market integration, we examined the dynamics of gas-exporting countries' stock market integration into the world market. We underline the predominant role played by the natural gas price in energy markets, especially since the shale gas revolutionary technique has been widely adopted in the U.S. and other regions of the world (e.g., Argentina, Canada, China, Poland, see for instance the IEA website3 ).

It has been widely documented that the degree of integration of many countries varies over time. Whereas it is expected that market integration continuously increases over time, the reality is that the level of integration could halt or even reverse depending on certain variables including market volatility, currency exchange rates and barriers to portfolio flows. The empirical estimation of the degree of market integration is however tricky, prompting most previous studies to make the three key assumptions of strict segmentation, full integration, and partial integration -where integration is assumed to remain constant over time [START_REF] Bekaert | Time-varying world market integration[END_REF].

However, following [START_REF] Bekaert | Time-varying world market integration[END_REF] which offers an innovative econometric approach in the estimation of the evolution of market integration in emerging markets in the context of a regime-switching methodology, a growing body of empirical studies have emerged in this subject. The international asset pricing models adopted in these studies have been severally modified to account for different local and international information variables that affect the degree of market integration. For instance, [START_REF] Carrieri | Characterizing world market integration through time[END_REF] incorporated measures of financial market development and liberalization policies and found that there are important factors that affect the degree of market integration in emerging markets.

This study departs from that of [START_REF] Bekaert | Time-varying world market integration[END_REF] and [START_REF] Carrieri | Characterizing world market integration through time[END_REF] by developing an international capital asset pricing framework which takes into account gas prices as a potential source of risk. In its fully functional form, the model allows the market integration measure as well as global, regional, local and gas risk premiums to vary over time. Following this framework, we also examine the time-variations in expected returns on stock market indices.

Our empirical results reveal that the level of market integration of important gas-exporting countries varies widely over time and depends on the interest rate spread, the level of market openness and market volatility. Also, we find that gas risk constitutes a small part of the global risk in all countries. The implication is that gasexporting countries are still primarily segmented from the world and the regional market. We would express optimism that our findings have profound implication for policy making in these countries as well as in the process of regional integration. Notably, we would recommend that future research should take into account the impact of institutional reforms aimed at lowering trade barriers which affect capital movements.

Fig. 1 .

 1 Fig. 1. Level of market integration among gas-exporting countries.

•

  Global instrumental variables: they are used to explain changes in the prices of international markets, Brent crude oil index and foreign exchange risk. We choose the following variables: the world market portfolio (Morgan Stanley Capital International-MSCI World index) in excess of the 30-day Eurodollar interest rate which is denoted by (IRENT), the variation in the U.S. term premium (USTP) and the return on the S&P 500 stock market index (RSP). • Local instrumental variables: They are used to infer the changes in the local price of risk, include the return on the local stock market index in excess of the 30-day Eurodollar interest rate (LRENT), and the variation in the trade-weighted average local inflation rate (INFRT).

Table 1

 1 Return series descriptive statistics. We report the basic statistics of sample data over the period from June 2003 to November. The associated probabilities of the LM ARCH test and Jarque-Bera (J.B) test are reported in brackets.

	LB-Q (10)	48.7595	[0.0000005]**	16.8893	[0.0768500]	17.7285	[0.0597193]	5.83389	[0.8290205]	24.5459	[0.0062760]**	7.93646	[0.6350436]	10.4529	[0.4017004]	23.8838	[0.0079148]**
	LB-Q (5)	46.7581	[0.0000000]**	11.5110	[0.0421385]*	16.6247	[0.0052695]**	2.78442	[0.7331800]	21.5961	[0.0006247]**	3.92882	[0.5597085]	3.09740	[0.6849720]	11.4976	[0.0423599]*
	ARCH-LM test (10)	4.0330	[0.0001]**	7.6737	[0.0000]**	2.6188	[0.0058]**	5.5657	[0.0000]**	5.1339	[0.0000]**	2.7909	[0.0034]**	1.2018	[0.2940]	6.5794	[0.0000]**
	ARCH-LM test (5)	7.8408	[0.0000]**	12.607	[0.0000]**	3.5298	[0.0047]**	9.7713	[0.0000]**	1.6393	[0.1524]	4.7212	[0.0005]**	1.2339	[0.2956]	12.610	[0.0000]**
	Jarque-Bera	19.887	[0.0000]**	159.68	[0.0000]**	356.55	[0.0000]**	30.424	[0.0000]**	402.71	[0.0000]**	95.543	[0.0000]**	7.3320	[0.0255]*	14.514	[0.0007]**
	Excess kurtosis	1.5457		4.4013		6.4433		1.9938		7.4309		3.1762		0.21218		0.74970
	Skewness	0.29742		0.81432		-1.3840		-0.23517		-0.28637		-0.87893		0.49150		-0.59996
	Std.dev	0.1007		0.09092		0.059839		0.085904		0.18191		0.1021		0.12679		0.087705
	Mean	0.013303		0.008693		0.0010294		0.0069681		0.037206		0.0092844		0.0038243		0.0078256
	Variable	The United Arab Emirates		Qatar		Kuwait		Saudi Arabia		Venezuela		Russia		Gas Prices		Oil Prices	Note:

Table 2

 2 Estimation of price risks.

		Constant	IRENT	USTP	RSP
	Panel A: Price of Gas risk
		0.260***	0.150***	0.130***	0.190***
		(1.065)	(0.001)	(0.015)	(0.033)
	Panel B: Price of World market risk
		0.120***	0.112***	0.023	0.788***
		(0.011)	(0.078)	(0.022)	(0.065)
	Panel C: Price of Exchange Rate risk
	The	0.920	0.247	0.920	0.247
	United	(0.598)	(0.689)	(0.592)	(0.680)
	Arab Qatar	0.564***	0.247***	0.567***	0.331
	Emi-	(0.056)	(0.089)	(0.027)	(0.089)
	rates Kuwait	0.033***	0.585*	0.290***	0.753***
		(0.008)	(0.072)	(0.024)	(0.020)
	Saudi	0.0774***	0.037***	0.046***	0.873***
	Ara-	(0.0084)	(0.002)	(0.002)	(0.006)
	bia Venezuela	-1.600***	0.946***	0.361***	0.699***
		(0.076)	(0.082)	(0.029)	(0.167)
	Russia	-1.600***	0.946***	0.361***	0.528***
		(0.076)	(0.082)	(0.029)	(0.088)
		Constant	DYR	RMI
	Panel D: Price of Regional risk
		0.121***	0.089***	0.291***
		(0.122)	(0.001)	(0.015)

Note: This table shows the determinants of exchange rate, world and regional market risk prices. * , * * , and * * * indicate significance at the 10%, 5% and 1% rates respectively.

Table 4

 4 Dynamics of stock market integration.

		The United Arab Emirates	Qatar	Kuwait	Saudi Arabia	Venezuela	Russia
	Constant	0.289***	0.367***	0.197***	0.189***	1.067***	0.463***
		(-0.022)	(-0.101)	(-0.089)	(-0.011)	(-0.009)	(-0.029)
	OPEN	0.112	-0.102***	-0.053***	-0.056***	0.218***	0.055***
		(-0.125)	(-0.213)	(-0.01)	(-0.028)	(-0.005)	(-0.008)
	SWAY	-0.020***	-0.067***	-0.033***	-0.001	0.0774***	0.037***
		(0.011)	(0.003)	(0.002)	(0.011)	(0.0084)	(0.002)
	VIX	-0.122***	-0.348***	0.290***	-0.141***	-1.600***	0.946***
		(0.028)	(0.000)	(0.000)	(0.002)	(0.076)	(0.082)
	Y mean	0.268 +++	0.230 +++	0.197 +++	0.358 +++	0.061 +++	0.137 +++
	Y max	0.397	0.362	0.373	0.467	0.152	0.245
	Y min	0.071	0.059	0.030	0.166	0.001	0.099
	Std. dev.	0.083	0.084	0.081	0.066	0.001	0.031

Note: Y max, Y min, and Y mean indicate the maximum, minimum and average values of market integration measure. *, **, and *** indicate that the coefficients are significant at the 10%, 5% and 1% levels respectively. +++ indicates that the average degree of integration is significantly different from zero at the 1% level with respect to the two-sided Student-t test.

Table 5

 5 Decomposition of the total risk premium.

	RP T i,t	RP G i,t	RP L i,t	RP K i,t	RP W i,t	RP P i,t

The authors display integration measures average between 0.712 (Venezuela) and 0.847 (Kuwait).

https://www.iea.org/ugforum/ugd/shalegas/.

Acknowledgements

For constructive comments and feedback, we wish to thank Dayong Zhang, Qiang Ji, anonymous reviewers, as well as seminar participants at the 2018 International Conference on Energy Finance: Frontiers and Future Development, Institutes of Science and Development (Chinese Academy of Sciences, Beijing, China), and the 2018 International Symposium on Environment and Energy Finance (IPAG Business School, France).

Appendix A Table 6

Comparative list of membership in Oil & Gas cartels.

OPEC GECF

Middle-East

Appendix B. Supplementary data

Supplementary data to this article can be found online at https:// doi.org/10.1016/j.eneco.2018.12.027.