Bank Risk in an Islamic Financial System

Journal of Administrative Management, Education and Training (JAMET) ISSN: 1823-6049 Volume (12), Issue (4), 2016, 558-567 Available online at http://www.jamet-my.org

Citation: A.Toussi, N.Trad, R.Rostamiyan, Bank Risk in an Islamic Financial System-Inter-temporal Comparative Study, Journal of Administrative Management, Education and Training, Volume (12), Issue (4), 2016, pp. 558-567

Journal of Administrative Management, Education and Training (JAMET)

Bank Risk in an Islamic Financial System: Inter-temporal Comparative Study A.Toussi, N.Trad, R.Rostamiyan ABSTRACT In order to determine whether the financial system without interest could really be an alternative to the financial system based on the interest, we have completed a study of 78 Islamic banks in 12 countries, where such institutions have been considered to be more active during the period 2004-2013. By performing an inter-temporal comparative analysis (pre-crisis period, period of crisis, post-crisis period), we have combined a series of microeconomic variables with other macroeconomic that depend on risk which is hereby defined as insolvency risk measured by Zscore and credit risk measured by EQL. Using the method of the generalized method of moment (GMM system), the results show that the financial crisis positively affected the risk of Islamic banks. Generally, the Islamic banks are more stable and less exposed to credit risk before and after the crisis than during the crisis period. Keywords: Islamic Banking, Global Financial Crisis, Bank Risk, GMM System

Introduction Islamic finance is one of the most dynamic sectors, and while it has been ignored in some countries, it has in recent years gained more importance and a significant presence almost everywhere in the world. Currently, according to the information provided by the Ernst & Young 2014-15 study on the competitiveness of Islamic banks (henceforth IBs), entitled "Participation Banking 2.0", the value of Islamic finance increased from $ 700 billion on the global market in 2008 to 1.3 trillion in 2011 to 1540 billion in 2012, and assets of these institutions had annual compound growth rate of around 17% from 2009 to 2013. Similarly, in Saudi Arabia, Kuwait and Bahrain, IBs accounted respectively for over 48.9%, 44.6% and 27.7% of the market share. Furthermore, in Indonesia, Turkey and in Pakistan, these banks market grew at an annual rate of growth composed of 43.5%, 18.7% and 22.0% respectively between 2009 and 2013. Generally, almost all major multinational financial institutions offer Islamic financial services. Two main factors explain the rapid development of this financial system in recent years. First, there is a high demand for Sharia compliant products by pious people as well as a high demand for diversification by traditional people, and the industry has been able to develop a number of financial instruments that meet most of the needs of both institutional and individual investors (Hasan & Dridi 2010). Second, the proliferation of banking crises caused a collapse of

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major banks around the world and therefore an economic paralysis, since the bank is considered to be "the brains of economies". These adverse events have proved that conventional finance remains vulnerable and unable to survive on its own. This has encouraged economists to find an alternative model to a system that continues to present difficulties and to call into question its strength and ability to absorb financial turmoil. Thus, Islamic finance has been a focal point of attention and has been considered as a reliable substitute to traditional banks. In particular, IBs have been considered more stable and relevant than traditional banks (henceforth CBs) during the crisis (Zarrouk, 2012). At this level, the debate on the relationship between Islamic finance and stability during the subprime crisis has produced two conflicting lines. Indeed, several financial analysts believe that Islamic finance, by its nature, is more stable than conventional finance (Bouslama 2008) because in the latter, the uncertainty residing in contracts, securitization, speculation, fixing of interest rates by the central bank, and expansion of unrequited credits, which are absent, in principle, in an Islamic financial system, constituted the main sources of financial instability. Thus, the non-presence of these devices in islamic finance, and the sharing of profits and losses have contributed to create a more stable financial system, which remains able to promote the creation and growth of stable employment (Derbel & al. 2011). However, this does not necessarily mean that IBs are more stable and more resilient to the crisis than CBs. Some financial experts, including Choong & Liu (2006) argue that Islamic banking services and those of the traditional system are not very divergent, and the rapid development in the islamic banking sector is mainly driven by the revival of Islam throughout the world rather than the benefits principle of sharing profits and losses. They also add that IBs should be regulated in the same way as their conventional counterparts. Toussi (2010) and Toumi (2011) also add that IBs could lead, besides the specific risk nature, to many of the risks incurred by the CBs namely the liquidity risk, the operational risk and the legal risk, etc. Theoretical analysis of the relationship between Islamic finance and financial stability does not in itself resolve this issue. It is therefore necessary to supplement it with empirical analysis. Similarly, some papers do not discuss the risk of islamic institutions more than in a theoretical framework, while empirical studies have focused on issues related to performance (Choong & al.2012, Zeitun, 2012, Babatunde & Olaitan 2013, etc) and efficiency (Ftiti & al. 2013, Rosman & al. 2013, Abdul Rahim & al.2013) and a comparison with the CBs. For this reason, and in order to reach our goal and check whether or not the banks opting for Islamic finance have been relatively affected by the crisis, we will carry, unlike previous studies, an empirical study of 78 IBs in 12 countries where these institutions have had a significant presence (Table 1) over the period 2004-2013, which we have divided into three distinct periods (before the crisis 2004-2006, during the crisis 2007-2009 and after the crisis 2010-2013). To this end, this paper is organized as follows: Section 2 discusses the relationship between Islamic finance and financial stability by referring to some previous research. In the section 3, we will conduct an empirical study. Finally, Section 4 concludes the paper. A Brief Overview of the Literature The objective of some studies is to evaluate the soundness of Islamic financial institutions during the period of the crisis and to compare them with the classical ones in order to know if this financial system constitutes an alternative to the conventional system or if it is only a supplement. On this point, several studies have been developed. Indeed, in a comparative study

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on the effectiveness of banks conducted by the University of Economic Sciences in Tehran in 2012, the assessment of 132 banks (54 IBs and 78 of CBs Middle - East) by the model of CCR in 2008 showed that only 11 IBs, provided an efficient response to the financial crisis, unlike CBs. A similar study conducted on 134 banks for 2009 in the Middle – East, of which 54 are IBs shows that only 8 banks are efficient effective, among which only bank is of the conventional type. So the IBs are better placed compared to CBs while facing the crises of these years of 2007 and 2009 (Sara, 2012). The inter-temporal and interbank evaluation developed by Samad & Hassan (2000) showed that only one Islamic bank, 'Bank Islam Malaysia Berhad', was relatively more liquid and less risky than a group of eight CBs. Idriss & al. (2011) conducted a study of nine foreign and local IBs in Malaysia during the period of 2007-2009. The result revealed that the size of banks is the only important factor in determining profitability with a positive relationship. Bech & al. (2013) add in a study of 510 banks across 22 countries, of which 88 are IBs over the period 1995-2009, and a sample of 209 listed banks in 21 countries for the period 2005-2009, that IBs are less profitable, but have a better asset quality and are better capitalized during crises. Using the VAR model including a sample of 4 stock market indices: France (CAC 40), the US (NASDAQ), Indonesia (JAKISLM) and Saudi Arabia (Tadawul) from 16/07/1997 to 15/12/2009, Derbel & al. (2011) found that Islamic finance is more stable than conventional finance given that the shock effect on the US market during the crisis period is transmitted negatively to all other markets, but with a small-scale market, applying Islamic financing method. In order to know which bank flow is more efficient than the other, Fayed (2013) conducted a sample of three IBs and six BCs BIs in Egypt during the period 2008 - 2010. The results show the superiority of the CBs on IBs in terms of profitability, liquidity, credit risk management and solvency. In the same context, Parashar (2010) showed that the IBs suffered more than CBs during the recent global financial crisis in terms of capital ratios, leverage and return on average equity, while CBs have suffered more than BIs in terms of average return on assets and liquidity. During the period of 2006-2009, BIs performed better than CBs. Moreover, Hasan & Dridi (2010) conducted a study to establish more reliable conclusions concerning financial stability and the resilience of the Islamic banking sector. They evaluated the performance of two groups of banks at the country level to control the heterogeneity between countries in terms of regulatory frameworks, macroeconomic shocks and political answers. Their study suggests that IBs were affected differently from CBs, and the credit risk and asset growth of IBs have performed better in 2008-09 compared to CBs, which contributed to the financial and economic stability. Unlike previous studies that are based on interbank comparisons Zarrouk (2012) has established an inter-temporal comparative study (before and during the crisis) on the profitability, liquidity, credit risk, and effectiveness of 20 IBs both before and after the financial crises in the GCC countries over the period ranging from 2005 to 2009. The results show that the financial crisis negatively affected the performance of IBs. However, when the crisis had affected real economic activity in 2009, a sharp decline in performance was noted. Profitability and liquidity of IBs declined after the crisis in Kuwait, Bahrain and the United Arab Emirates States. IBs in the UAE were less efficient and took excessive risks during and after the crisis compared to other countries. In the light of these arguments, it seems that there is no consensus

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on the stability and sustainability of IBs in times of crisis, all studies have led to conflicting results. We therefore test the hypothesis that the risk of IBs was affected during the crisis. Empirical Evidence Methodology In order to determine whether Islamic finance is able to absorb shocks in the context of a very disturbed global economy by the global financial crisis, we will conduct an empirical study based on an inter-temporal comparison (before 2004-2006, during 2007-2009, and after the crisis 20102013), using a dynamic panel (GMM in system). Sample and Data In order to achieve our goal, we'll have a sample of 78 IBs in 12 countries where this type of institutions has a significant market share. Similarly, our choice is guided by the availability of data that extend from 2004 to 2013. It is a large enough sample to provide reliable conclusions. We will take risk, as a dependent variable. Specifically, we have chosen credit risk and insolvency risk as the two main indicators of the stability of the banking sector. As Bourkhis and al. (2013), in order to measure the latter, we will use the Z-SCORE. A bank is called solvable if the total value of its assets exceeds its responsibility, and becomes risky when it is insolvent. A superior Zscore means that banks are less risky. And to measure credit risk, we will use the EQL ratio that was already taken by Fayed (2013) as an indicator. As independent variables, we will combine in our study specific internal determinants bank including the size of the bank, its capitalization, its liquidity and asset quality. We will also apply external factors specific to countries using the growth of gross domestic product and the rate of inflation. The choice of these ratios is for the purpose of finding a tool to provide information on the IBs risk. To find these indicators, the data is imported from the base BankScope, except for macroeconomic determinants, which are extracted from the World Bank base. All variables, except two external variables that are defined in the Table (2), are converted in million US Dollars (mil usd). Models Estimate The question is to compare the level of IBs risk before, during and after the global financial crisis. Specifically, the purpose is to test the hypothesis according to which the world financial crisis has affected the IBs risk. To test this hypothesis, we specify the following econometric model:

RISQUE j ,i ,t    1  MI jit   2  MA jit   jit

Considering that risk is a dependent variable, risk of insolvency is measured by Zscore and the credit is measured by EQL, they are the two models that examine the association between micro and macroeconomic indicators, and bank risk. Insolvency Risk

Zscore j ,i ,t    1  MI jit   2  MA jit   jit

Credit Risk

Eql j ,i ,t    1  MI jit   2  MA jit   jit

Where; indices "i", "j" and "t" indicate successively banks (i = 1, 2, 3,..., 78), the countries (j = 1,2,3, ..., 12), and the period (t = 2004,2005,..., 2013).  Indicates settings model to estimate.

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 MI jit is a vector of micro-economic variables,  MA jit is a vector macroeconomic variables,

 jit is the random term or error. Econometric Results and Interpretations Descriptive Statistics and Correlation Matrix Table 3 shows the average increase in size bq ratio throughout the period of our study but is not strong; in 7.057 before the crisis, in 7.567 during the crisis to reach 7.919 after the crisis. Similarly, we found the average change in the ratio of capital assets between the three subperiods. Indeed, this ratio increased from 1.6337 before the crisis to 2.15 during the crisis, then fell to 1.3624 after the crisis. Bourkhis & al. (2013) concluded that this ratio experienced a constant increase during the three sub-periods. While, in terms of liquidity, there was a progressive decrease of 10% for the ratio Lqata during the entire period. Before the crisis, this ratio registered 31.14% to fall to 24.7% during the period of the crisis and reached 21.34% after the crisis. The same comment applies to the ratio of net loans total assets (netlta), which recorded a value of 48.85% before the crisis, and then declined to 46.36% during the crisis and rised up again to 45.29% after the crisis. This analysis shows that during the period of financial stability, IBs were able to provide more funding. That is not the case during and after the financial crisis. According to Table 4, we see that the estimate of multiple linear models in the three periods requires the absence of multi-collinearity among variables. This problem refers to a situation in which two or more explanatory variables are highly correlated. A multi-collinearity problem arises when two independent variables are highly correlated. Kervin (1992) estimated that a serious multi-collinearity problem arises from the limit of 0.7. According to Kerwin (1992), the results show that all correlation coefficients are lower than 0.7. We therefore conclude the absence of multi-collinearity for all previously defined models. Results Estimates and Comments According to the results shown in Table 5, we find that the relationship between bank size and stability measured by Zscore is negative during all periods. This result implies that a bank with low Zscore causes a lower stability. As concluded by Cihák & Hesse (2008), credit risk was positively influenced by the size of the banks during the crisis. Our result means that the more the size of IBs increases, the more difficulty they have to adjust their credit risk monitoring systems. Concerning the capitalization of the bank, it seems it has no significant negative effect on the stability of banks in all years, but it is significant during and after the crisis. This negative relationship indicates the instability of IBs, in accordance with the results found by Parashar (2010) which indicate that IBs suffered more than CBs during the recent global financial crisis in terms of capital adequacy ratio. Furthermore, the instability of these institutions is illustrated when considering the variable Eql as credit risk measurement, which has been positively affected during the three sub periods. On this point we see that IBs were affected by the crisis. Identically to the findings of El-Said & Ziemba (2009), IBs avoided early exposure to the crisis, since the impact of liquidity ratio on the risk of insolvency is positive before and during the crisis This demonstrates that IBs have sufficient liquid assets, and thereby results in financial stability. This is consistent with the general view that IBs suffer from an excess of liquidity. Nevertheless, the

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long duration of this event affected Islamic financial institutions after the crisis, given that the relationship became negative and significant at the 5% level. Zarrouk (2012) also found that the liquidity of IBs decreased after the crisis in some countries. Depending on the credit risk, the relationship between Lqta and EQL is positive in the three sub-periods. These signs indicate that banks have not been unscathed from the crisis. In an identical manner to the results shown by Bech & al. (2013), IBs have a better asset quality during and after the crisis because the relationship between netlta and Zscore is significantly positive, but it is negative when it comes to credit risk. This causes superior stability and credit risk reduction. Therefore, a better assets quality of Banks is an index of banking stability, in particular, and economy in general since the bank is considered to be the core of economies. When considering the strength of the banks, it is also imperative to control macroeconomic outlook (GDP growth rate and the inflation rate). Indeed, the result of economic growth has a positive and significant effect on Zscore before and after the crisis. This indicates that increasing economic growth will increase the Zscore and therefore will cause a banking stability. The higher the GDP, the more stable will the IBs be. According to Shayegani & Arani (2012), real GDP is one of the macroeconomic variables contributing to financial stability. Our result is in line with previous findings, which claim that in times of economic prosperity, higher real GDP produced a higher Zscore and therefore significant stability. However, a significant change in relationship level was observed during the crisis, indicating that the stability of IBs was affected during this event. This is illustrated by the relationship between credit risk and GDP growth where the sign is always positive. Nevertheless, this conclusion is not always verified. Indeed depending on inflation, IBs kept an important level of stability during the three sub-periods. This stability appears as the positive relationship between inflation and insolvency risk, and is illustrated by the negative impact on credit risk. In accordance with the findings of Zehri & al. (2012), our main empirical results clearly show that IBs are stable and immune against the 2007-2009 crises, because the variable inflation rate (INF), has had a positive impact on insolvency risk and a negative impact on credit risk, which therefore explains a significant level of stability and low credit risk. Conclusion The purpose of this article was to evaluate the effect of the recent global financial crisis on the IBs risk, and hence, whether the financial system without interest could be an alternative to the traditional system or whether it only represents a financial supplement with certain limitations. To empirically illustrate this relationship, we combined a series of internal variables specific to banks and other external variables specific to countries. According to Zscore as a measure of insolvency risk and EQL as an indicator of credit risk, we used an econometric model, especially a dynamic panel model (GMM System) to the case of 78 IBs in 12 countries where these industries have a very important market share. The results show the extent to which the quality of assets of IBs and the inflation rate were the main determinants of the stability of IBs and the reduction of its credit risk. However, the variables forming the growth of GDP, the size of the bank, its capital and liquidity, indicate both instability of IBs and increased credit risk during the period of the crisis. In light of this work, it is clear that the current financial crisis was transmitted negatively to the Islamic financial market but is of a lower magnitude. This allows us to predict that despite the fact that the Islamic financial system still has a long way to go, Islamic finance could be an effective

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complementary funding system alongside conventional finance. Anyway, in empirical studies, the relationship between the stability of the Islamic system and the effect of the crisis is still a matter for debate. It requires comparative studies in which the two banking systems are compared in similar socio-economic situations and by comparable economic and financial criteria. For future studies, it is recommended to have a wider reach where other Islamic financial institutions and influencing factors can be taken into account. Table 1. List of Islamic Banks in Different Countries Name of Countries Brahain Kuwait United Arabe Emirates Qatar Iraq Saudi Arabia Yemen Pakistan Sudan Jordan Iran Turkey Total

Numbre of Islamic Banks 19 7 10 4 5 3 3 4 4 3 12 4 78

Table 2. Definition of Dependents and Independents Variables Dependent Variables Insolvency Risk : Credit Risk :

Definitions

ZSCORE EQL

Independent Variables Internal indicators: size of the bank Capitalization Liquidity Asset quality External indicators: Croissance du Produit intérieur brut Inflation

Asset returns + Capital Ratio clean) / standard deviation of return on assets Total Equity/Net Loans Definitions Natural logarithm of total assets for each bank Capital / T assets Liquid assets / Total assets Net Loans / Total assets

SIZBQ CAPTL LQATA NetLTA

GDP growth Inflation rate (%)

GGDP INFLT

Table 3 : Statistical Summary for Islamic Banks Before Crises Variable zscore sizebq cta lqata netlta gdpg inf

Obs 152 152 152 152 152 234 234

Insolvency Risk Std. Dev. 2.431954 2.304119 0.537216 0.422802 0.551218 0.075855 0.106837

Min -17.89 -0.609 0 0.0002 0 0.0317 0.0061

Max 8.0302 15.4093 3.6829 4.7161 6.4848 0.5416 0.6483

Insolvency Risk Mean Std. Dev. 2.15 4.537285 7.567 2.178969 0.249 0.364001 0.247 0.19044 0.464 0.44928 0.046532 -0.0708 0.062054 -0.0487

Min -5.2593 2.4932 0.0001 0.0003 0 0.18 0.22

Max 48 16.8 4.27 0.97 5.9 0.046532 0.062054

Mean 1.6337 7.057 0.3883 0.3114 0.4885 0.0859 0.0918

Variable eql sizebq cta lqata netlta gdpg inf

Credit Risk Mean 197.725 7.05702 0.38828 0.31138 0.48846 0.08588 0.09178

Obs 152 152 152 152 152 234 234

Std. Dev. 2162.846 2.304119 0.537216 0.422802 0.551218 0.075855 0.106837

Min -0.091 -0.609 0 0.0002 0 0.0317 0.0061

Max 26546 15.409 3.6829 4.7161 6.4848 0.5416 0.6483

During Crises Variable zscore sizebq cta lqata netlta gdpg inf

Obs 219 219 219 219 219 234 234

Variable zscore sizebq cta lqata netlta gdpg inf

Obs 261 261 261 261 261 312 312

Variable eql sSizebq cta lqata netlta gdpg inf

Obs 219 219 219 219 219 234 234

Mean 340.05 7.5665 0.249 0.247 0.4636 0.0433 0.0797

Credit Risk Std. Dev. 4912.76 2.17897 0.364 0.19044 0.44928 0.04653 0.06205

Min 0 2.49 0 0 0 -0.1 -0

Max 72708 16.78 4.267 0.968 5.901 0.18 0.225

After Crises Insolvency Risk Mean Std. Dev. 1.3624 4.94142 7.9197 2.32275 0.218 0.24814 0.2134 0.18888 0.4529 0.25137 0.032 0.0446 0.0933 0.11175

Min -63.46 2.5257 0 0.0006 0.0012 -0.151 0.0018

Max 16.066 17.821 1.5176 2.0105 0.9639 0.1673 0.4444

Variable eql sizebq cta lqata netlta gdpg inf

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Obs 261 261 261 261 261 312 312

Mean 4.1684 7.9197 0.218 0.2134 0.4529 0.032 0.0933

Credit Risk Std. Dev. 13.6517 2.32275 0.24814 0.18888 0.25137 0.0446 0.11175

Min 0.0002 2.5257 0 0.0006 0.0012 -0.1509 0.0018

Max 139.1 17.82 1.518 2.011 0.964 0.167 0.444

Table 4. The Correlation Coefficients between Independent Variables for Islamic Banks Before crises zscore 1 -0.183 0.2555 -0.045 0.009 0.0507 -0.213

zscore sizebq cta lqata netlta gdpg inf

Zscore 1 -0.1 0.34 0.3 0.25 -0.1 0.06

Zscore sizebq cta lqata netlta gdpg inf

zscore 1 0.02 -0.1 0.016 0.077 0.048 -0.01

Zscore sizebq cta lqata netlta gdpg inf

sizebq 1 -0.516 -0.29 0.006 -0.011 0.125

sizebq

Insolvency Risk cta

1 0.3589 0.3835 0.0916 -0.12

lqata

netlta

1 0.644 -0.05 0.034

Insolvency Risk cta

gdpg

1 -0.045 -0.015

lqata

inf eql sizebq cta lqata netlta gdpg 1 inf During Crises

1 0.183

netlta

gdpg

inf

Eql 1

Eql 1 -0.37 -0.29 0.113 -0.1 0.305

sizebq 1 -0.39 -0.18 0.513 0.102 0.372

Sizebq 1 0.25 0.57 0.06 -0.1

1 0 0.13 -0.1

Insolvency Risk cta

1 -0.017 -0.223 -0.008 -0.255

1 -0.1 0.23

lqata

1 0.166

netlta

1 -0.3 0.014 -0.11

gdpg

1 0.078 0.181

1 -0.4

eql 1 -0.1071 -0.0528 0.1276 -0.0813 0.0339 0.5465

sizebq

Crédit Risk cta

lqata

netlta

gdpg

inf

1 -0.5162 -0.2901 0.0057 -0.0113 0.1245

1 0.359 0.384 0.092 -0.12

1 0.644 -0.05 0.034

1 -0.045 -0.015

1 0.183

1

sizebq

Crédit Risk cta

lqata

netlta

gdpg

inf

1 -0.4 -0.3 0.11 -0.1 0.3

1 0.25 0.57 0.06 -0.1

1 0 0.13 -0.1

1 -0.05 0.231

1 0.166

1

sizebq

Crédit Risk cta

lqata

netlta

Gdpg

inf

1 -0.386 -0.176 0.5132 0.1022 0.3722

1 -0.02 -0.22 -0.01 -0.26

1 -0.3 0.014 -0.11

1 0.078 0.181

1 -0.4

1

-0.09 cta lqata netlta gdpg 1 inf After Crises inf Eql sizebq cta lqata netlta gdpg inf

1

-0.04 0.132 -0.07 -0.04 -0.04

Eql 1 -0.279 0.483 0.048 -0.46 0.005 -0.18

Table 5. Estimation GMM en System Before Crises Insolvency Risk Lag of depand Variable sizebq cta

-0.0089924

(0.894)

During Crises Credit Risk

9.673693***

Insolvency Risk (0)

After Crises Credit Risk

Insolvency Risk

Credit Risk

0.0145716

(0.460)

0.0001018

(0.4450)

0.131489***

(0)

-0.026***

(0)

-0.652**

(0.009)

-0.2550994* (0.068)

-19.4418

(0.126)

-0.0343091

(0.870)

0.2013082

(0.8970)

-0.350484***

-0.1747352

(0.794)

15.27427

(0.86)

-2.98143*

(0.067)

4.76112

(0.6960)

-5.98268**

(0.076)

4.722*

(0.098)

(0.03)

0.248

(0.826)

lqata

0.9793668

(0.405)

360.4172*** (0.01)

11.0569***

(0.000)

3.0302

(0.8430)

-4.197841**

(0.035)

netlta

-0.220901

(0.814)

446.9732***

4.803333***

(0.000)

-7.3291

(0.3820)

4.17 2606**

(0.02)

gdpg

0.7577915

(0.745)

287.0203

(0.345)

-5.533776

(0.363)

22.1905

(0.4670)

8.777363**

(0.011)

1.732

(0.464)

inf

0.9211352

(0.481)

-78.0932

(0.534)

0.1650833

(0.460)

-5.3825

(0.6930)

0.785

(0.738)

-0.525

(0.809)

3.16742** 92

(0.018)

-197.5509 ** (0.037) 92

-2.1910

(0.224)

3.1054

(0.8060)

3.617

(0.257)

_cons Nbre Obsr

(0)

201

-14.04 ***

(0)

13.49***

(0)

201

185

185

Hansen Test

8.78

9.46

12.74

6.99

21.88

16.88

P-value of the Hansen test

0.845

0.8

0.542

0.935

0.406

0.718

Sargan Test

121.55

34.56

21.73

39.64

34.46

34.63

P-value of the Sargan Test

0

0.002

0.284

0

0.032

0.031

------

------

-----

-----

-1.38

1.76

------

-----

----

0.169

0.078

Test AR (1) Arellano & Bond P-value of AR (1)

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ALI TOUSSI, lecturer at the University of Kharazmi of Tehran, Iran, and University of Lumière Lyon II, France. Email: [email protected] / [email protected] NAAMA TRAD, Researcher and doctoral student in economics at the University of Lyon II, Lyon, F-69007, France; CNRS, GATE Lyon Saint-Etienne, Ecully, F-69130, France. Email: [email protected]/ [email protected] RASHED ROSTAMIYAN, Bachelor student in Insurance Management at the University of Kharazmi, Tehran, Iran. Email: [email protected] JEL Classification: G21

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