Are Non-conventional Banks More Resilient than Conventional Ones to Financial Crisis?

Keywords: GCC countries, Islamic Banks, Resilience, BEKK model

 and Chapra (2008). A diagonal BEKK model is used to examine the impact of the global crisis on conditional beta of the selected banks. Results show that Islamic and conventional banks have been largely affected by the global crisis, except for few banks. They reveal also that small banks have been less affected than larger banks. These results are in line with the other studies which have found that Islamic banks are not more resilient than conventional ones.

Introduction

The resilience of Islamic banks remains ambiguous from both an empirical and theoretical perspective. [START_REF] Khan | Islamic Interest-Free Banking: A Theoretical Analysis[END_REF], [START_REF] Khan | The Framework and Practice of Islamic Banking[END_REF] and [START_REF] Chapra | The global financial crisis: Can Islamic finance help minimize the severity and frequency of such a crisis in the future?[END_REF] claimed that the presence of Islamic banks may enhance the resilience of the financial system. These authors among others [START_REF] Iqbal | Islamic banking[END_REF][START_REF] Al-Omar | Islamic Banking: Theory, Practice and Challenges[END_REF][START_REF] Sugema | Interest versus profit-loss Sharing credit contract: Efficiency and welfare implications[END_REF]Gudarzi Farahani and Dastan, 2013), have recommended the generalization of the Islamic finance principles. Conversely, [START_REF] Kuran | Islamic economics and the Islamic subeconomy[END_REF], among few others, argue that Islamic financial operations differ only "cosmetically" from conventional operations in the Banking services.

In this paper, we show that Islamic banks are more resilient than conventional banks, acting even as a stabilizer of the global financial system. This research is based on the previous works of [START_REF] Khan | Islamic Interest-Free Banking: A Theoretical Analysis[END_REF], [START_REF] Khan | The Framework and Practice of Islamic Banking[END_REF] and [START_REF] Chapra | The global financial crisis: Can Islamic finance help minimize the severity and frequency of such a crisis in the future?[END_REF]. The first author considers that, owing to the absence of the interest rate from the Islamic finance universe, Islamic banks are more stable than conventional ones. In this context, the author assimilates depositors in Islamic banks to shareholders: they hold shares whose value may increase or decrease depending on the bank's performance. Therefore, depositors will bear a portion of the losses when they occur a . Based on this hypothesis, [START_REF] Khan | Islamic Interest-Free Banking: A Theoretical Analysis[END_REF] demonstrates that the flexibility of the value of those shares allows the Islamic banks to adapt and regulate imbalances on capital market b . As for [START_REF] Khan | The Framework and Practice of Islamic Banking[END_REF], they argue that, compared to the interest-based system, the system based on PLS (i.e. profit and loss sharing) conduct to amortization in a more rapid and flexible manner of banking and financial crises, since the nominal value of investment deposits is not guaranteed, but depends on the performance of the bank. In fact, any shock in the real economy will be absorbed by the funds deposited in investment accounts (i.e. PSIA accounts). [START_REF] Chapra | The global financial crisis: Can Islamic finance help minimize the severity and frequency of such a crisis in the future?[END_REF] reminds that Islamic finance is perfectly correlated with the real economy, as any financial transaction must be backed by a real asset rather than by an imaginary or notional one c . He indicates that the obligation of backing by a real asset helps to eliminate most unproductive and speculative transactions that involve gharar (high uncertainty) and qimar (bets), which, in turn, favors the accumulation of physical capital, hence stimulating the economic activity.

Although the estimation of the impact of the resilience of Islamic banks has been the subject of few empirical studies, the results found are not stable. In the light of the specific Tunisian experience over the period [START_REF] Gurley | Does Islamic Bank Financing Contribute to Economic Growth? The Malaysian Case[END_REF]-1984[START_REF] Darrat | The Islamic interest-free banking system: some empirical evidence[END_REF] has examined the hypothesis stipulating that the financial and banking systems become more stable as the interest is paralyzed. He has a This means that the depositors accept the change in the value of their deposits. b Similarly, Al-Omar and Abdel-Haq (1996:9) think that the prohibition of interest by the Shari'a is a way to establish justice between investors who provide the funds and entrepreneurs who provides labor to grow the investors' funds: in an interest-based system, the income earned by the fund provider is disconnected from both the risks incurred by the entrepreneur and the real income that arises from the use of the funds provided. c This principle is of paramount importance for the structuring of most Islamic finance products. [START_REF] Sugema | Interest versus profit-loss Sharing credit contract: Efficiency and welfare implications[END_REF] show, through a theoretical model, that the products offered by Islamic finance, based on the PLS principle, improve the efficiency of the system and ensure equity between creditors and debtors. More recently, [START_REF] Boukhris | Islamic and Conventional Banks' Soundness During the 2007-2008 Financial Crisis[END_REF] have studied the resilience of Islamic banks with respect to conventional banks before, durind and after the recent financial crisis. They have found that: 1/ Islamic banks were more profitable than conventional banks before the crisis; 2/ Conventional banks become more profitable than Islamic banks after the crisis; 3/ Islamic banks were less resistant to the crises than Islamic banks. come to the conclusion that the interest-free monetary system allows a better stability of the money velocity compared to the interest-based one. Furthermore, he has shown that the demand for money is structurally more stable in the absence of interest. He has also concluded that only the monetary and financial assets which do not bear interests may be used by the Tunisian monetary authorities as an appropriate intermediate target to reach its medium-term objectives. [START_REF] Yousefi | Monetary stability and interest-free banking: the case of Iran[END_REF] replicate the study by [START_REF] Darrat | The Islamic interest-free banking system: some empirical evidence[END_REF] to the Iranian case. Their empirical findings partially confirm the results of [START_REF] Darrat | The Islamic interest-free banking system: some empirical evidence[END_REF] and, do not allow the validation of the superiority of the Islamic banks. [START_REF] Charles | Risk and ethical investment: Empirical evidence from Dow Jones Islamic Indexes[END_REF] tested the hypothesis stating that the Islamic finance is more resilient to shocks than the conventional finance. They examined the assumption whether the Islamic stock Indices are more or less affected by the changes in the volatility regimes than the conventional stock indices. They found that the Islamic and the conventional indices were affected to the same degree by the volatility changes, and discovered no empirical evidence allowing to validate the hypothesis of the Islamic finance superiority. [START_REF] Arouri | Are Islamic finance innovations enough for investors to escape from a financial downturn? Further evidence from portfolio simulations[END_REF] have found that Islamic finance helps save investors from financial crises by comparing some Islamic stock indices to conventional ones.

Using a sample of 120 Islamic and conventional banks, Dridi and [START_REF] Hasan | The effects of the global crisis on Islamic and Conventional banks: A comparative study[END_REF] found that Islamic banks were more resilient than conventional banks during the crisis. But, by 2009, the profitability of Islamic banks declined sharply compared to conventional banks. [START_REF] Ouerghi | Are Islamic Banks More Resilient to Global Financial Crisis than Conventional Banks?[END_REF] found that Islamic banks are less profitable, more prone to credit and less efficient than conventional banks. She concluded that Islamic banks are less stable from a financial perspective than conventional banks, and that large Islamic banks perform better than large conventional banks.

This paper provides an empirical evidence of the resilience of Islamic banks with respect to the conventional banks in three GCC countries before, during and after the recent global financial crises. This paper is organized as follows. Section 2 provides a short overview of the specifities of Islamic banking from a financial stability perspective. Section 3 describes the procedure used for sample selection. Section 4 presents the econometric methodology. Section 5 outlines the econometric results and their economic and statistical interpretation. Finally, section 6 highlights the major conclusions to be drawn and further directions for future research.

Islamic banks and financial stability: an overview

Hachicha and Ben [START_REF] Amar | The Effectiveness of Monetary Policy Transmission Channels in the Presence of Islamic Banks: The Case of Saudi Arabia[END_REF] show that although the Islamic bank ensures a better capital allocation (i.e. channeling the deposits to the entrepreneurs that have financial needs), it does not allow the qualitative transformation of the liabilities à la [START_REF] Gurley | Does Islamic Bank Financing Contribute to Economic Growth? The Malaysian Case[END_REF]. Theoretically, the Islamic banks' liabilities are, contractually, less liquid than those of the traditional banks; the depositors in Profit Sharing Investment Account (PSIA) know that their deposits do not represent a stock of money stricto sensu. Their deposits are effectively correlated with the bank assets since the Islamic bank investments are theoretically backed by real assets. Any shock occurring at the level of the bank assets will be automatically transmitted to the liabilities, as indicated by [START_REF] Darrat | The Islamic interest-free banking system: some empirical evidence[END_REF] and Yousefi et al. (1997) d .

The share of profit and losses between parties in financial transactions is the Islamic alternative to the borrower-lender relationship brought by conventional banks. For several reasons, this alternative may strengthen financial stability. First, the structure of risk allocation implied by the PLS principle makes the providers of funds more enticed to collect, ex ante, information about the project and about the agent in need of finance, and to exercise, ex post, monitoring of the projects they fund [START_REF] Khan | Islamic finance: growth and prospects in Singapore[END_REF]Hachicha and Ben Amar , 2015). Second, while conventional credits are granted based on the assessment of borrowers' creditworthiness and not necessarily to the most intelligent or meritocratic [START_REF] Thurow | The Zero-Sum Society: Distribution and the Possibilities for Economic Change[END_REF], some products offered by the Islamic finance are not based on the entrepreneur's creditworthiness but rather, on the project's economic viability and on the debtor's entrepreneurial abilities. Third, Islamic finance reduces speculation and encourages transaction backed by real assets [START_REF] Chapra | The global financial crisis: Can Islamic finance help minimize the severity and frequency of such a crisis in the future?[END_REF]. In fact, according to [START_REF] Ahmed | Incentive Compatible Profit-Sharing Contracts: A Theoretical Treatment[END_REF], the prohibition of riba and the narrow link with the real economy could enhance the stability of the financial system. Fourth, Islamic banks do not grant financing exclusively to Muslims, but also to all the economic agents who have a high potential to contribute to the value added creation (Hachicha and Ben Amar, 2015). Finally, the prohibition of selling/buying debt by Islamic banks has limited their access to toxic assets [START_REF] Chapra | The global financial crisis: Can Islamic finance help minimize the severity and frequency of such a crisis in the future?[END_REF][START_REF] Ouerghi | Are Islamic Banks More Resilient to Global Financial Crisis than Conventional Banks?[END_REF].

Sample Selection

Our sample is based on six GCC countries (Saudi Arabia, Bahrain, U.A.E, Kuwait, Oman and Qatar e ). We have chosen the GCC countries for four reasons. First, the GCC countries have very similar economic structures. Second, their financial systems are mixed, being characterized by the simultaneous presence of Islamic and non-Islamic financial Institutions. This allows us to compare Islamic banks to non-Islamic ones. Third, according to ICD Thomson Reuters, they hold about 48% of global assets of Islamic banks in 2013. Fourth, the GCC countries are among the pioneered countries in Islamic finance. This enables us to solve the problem of data unavailability characterizing the developing countries.

The second step is to list all the commercial banks (Islamic and non-Islamic) for each of the six GCC countries. The third step is to rule out the banks whose data do not go back to 31/12/2004 (daily frequency). The fourth step is to rule out countries which, after the third step, do not have at least two Islamic banks and two conventional banks. The fifth step is to rule out countries whose sectorial stock market data do not go back to 31/12/2014. The final step is to select four banks for each country (two Islamic and two non-Islamic banks) (cf. Table 1). The choice is based on their average market capitalization over the period ranging from December 31 st , 2004 to November 7 th , 2014: for each country, we retain the Islamic bank (resp. the non-Islamic bank) having the highest average market capitalization and the one having the lowest value. Figure 1 illustrates the selection procedure.

Econometric methodology

4.a. The MA-GARCH bivariate model

The ARCH models, introduced by [START_REF] Engle | Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation[END_REF], and the GARCH model, developed by [START_REF] Bollerslev | Generalized Autoregressive Conditional Heteroskedasticity[END_REF] are used in the literature. The use of GARCH models is widespread. The preference for these models is justified by their ability to introduce the correlation between securities in the expression of the conditional variances. Therefore, to estimate the effects of the recent financial crisis on Islamic and conventional banks of the GCC countries, we use the Diagonal-BEKK model, derived from the Full-BEKK model by [START_REF] Engle | Multivariate simultaneous generalized ARCH[END_REF]. The conditional covariance matrix of Full-BEKK model is given by: Islamic ( 4)

𝑦 𝑡 = 𝜇 𝑡 + 𝜀 𝑡 (1) 𝜀 𝑡 |𝛺 𝑡-1 ∼ 𝑁(0, 𝐻 𝑡 ) kj K k p j j t kj ki t t K k q i ki t B H B A A C C H             1 1 1 1 1 1 ' ' ' '   Where ) , ( ,t m it t r r y  is a (2×1)
Conventional ( 4) Islamic ( 5)

Conventional ( 11)

Islamic (2)
Conventional ( 3)

Islamic (3)
Conventional ( 5)

Islamic (5) Conventional (2)
Islamic ( 5)

Conventional ( 5)

Islamic (3)
Conventional ( 7)

Islamic (2)
Conventional ( 4)

Islamic (1)
Conventional ( 6)

Islamic (2)
Conventional ( 3)

Islamic (1)
Conventional ( 5)

Islamic ( 4)

Conventional (0)
Islamic ( 5)

Conventional ( 4) In some cases, the Full-BEKK model can present several technical difficulties due to the high number of parameters, such as a non-convergence and non-positivity of conditional variances matrix. This leads us to use the following diagonal version of this model:

Islamic (2) NBAD (
          p j j j t j i q i i t t i t B H B A A C C H 1 ' 1 1 ' ' '   (2)
Where 𝐴 and 𝐵 are diagonal matrices. The parameters in 𝐴 reveal the extent to which the conditional variances of the two variables are correlated with past squared errors: the diagonal elements of A measure the ARCH effect on residuals of asset 𝑖 (A11) and on residuals of the market portfolio (A22). The parameters in B depict the extent to which the current levels of the conditional variances are correlated with past conditional variances: the diagonal elements of B (i.e. B11 and B22) reflect the level of persistence in the conditional variance.

The time varying beta of bank i is calculated as follows:

𝛽 𝑖,𝑡 = 𝐻 ̂12,𝑡 𝐻 ̂22,𝑡 (3)
Where 𝐻 ̂12,𝑡 is the estimated conditional covariance between the returns of bank (𝑖) and the market portfolio returns, and 𝐻 ̂22,𝑡 is the estimated conditional variance of the market portfolio returns from the bivariate GARCH model. Since the conditional covariance and conditional variance are time-dependent, the beta of each asset is also time dependent.

4.b. The effects of the crisis

For an investor, an increase in the volatility of an asset increases the investment risk level. Following the recent global financial crisis, an increase in the volatility of the GCC markets has been observed. To measure the effects of the financial crisis on Islamic and conventional banks over time, we perform a beta regression [eq.4] 6 . We also use binary variables (i.e. dummy variables) which can take the value 0 or 1. In this study, we perform two tests. For the first one, the dummy variable takes the value 0 over the pre and the post crisis periods, respectively from

, 0 0 1 , 1 2 2 ( ) ( ) i t t i t it t t t t t D CV CV D MV MV D               
(4) 6 The β coefficient is widely used in financial applications. This coefficient measures to which extent an asset's expected return is correlated with the broad market return. In other words, it represents the systematic risk that cannot be eliminated, even with portfolio diversifications. In the classic CAPM model (Sharpe, 1964;Lintner, 1965), β is assumed to be time invariant. However, this assumption has been questioned in many studies. There now exists widespread evidence that β is unstable over time (among others: Blume, 1971;Fama and Macbeth, 1973;Fabozzi and Francis, 1978;Sunder, 1980;Bos and Newbold, 1984;Collins et al., 1987;Kim, 1993).

With , it  the beta of the bank 𝑖 at time 𝑡, t i CV , the conditional volatility of bank 𝑖, t MV the conditional market volatility and t  the error term. The parameters 𝛼 1 and 𝛼 2 measure the effect of the conditional volatility of the individual bank and the market on the beta of the bank during the total period respectively. If the sign on both the parameters is positive, then a rise in the volatility of the bank or market should increase the beta of the bank.  and 2  are positive and significant, then the volatility of bank and market during the crisis period will affect the beta of the bank under study. Negative and significant 1

 and 2  imply the opposite. Insignificant 1  and 2  imply that the volatility of bank and market during the crisis period have no extra effect on the beta of the bank.

Empirical results and policy implications

Tables 2, 3 and 4 present the BEKK bivariate MA-GARCH(1,1) results for Abu Dhabi, Kuwaiti and Qatari banks respectively. The bivariate GARCH results are quite stable. The ARCH coefficients (i.e. A11 and A22) for the Abu Dhabi Banks (Table 2), Kuwaiti Banks (Table 3) and Qatari ones (Table 4) are all positive and highly significant, implying volatility clustering, both at the level of banks returns and sectorial markets returns. The persistence of the volatility is confirmed by the B11 and B22 coefficient which are both positive and significant. The positive coefficients highlight that volatility was a double-side phenomenon, generated both by idiosyncratic and systemic factors. From an economic perspective, the system is the sum of the individual components; volatility, as the result of individual and collective market contributors, captures the mutual influences between individual entities as well, underpinning a potential transmission chain phenomenon. This finding is in line with the concept of "shift-volatility" transmission in the East Asian equity markets revealed by [START_REF] Aloy | Shift-Volatility Transmission in East Asian Equity Markets[END_REF] who unveiled the volatility propagation from a low to a high level.

The null hypothesis test of no serial auto-correlation uses the Ljung-Box Q statistic on standardized residuals and squared standardized residuals. As the results are significant for all banks, we reject the null hypothesis. This finding reveals the fact that volatility clustering is still a phenomenon marked by uncertainty. It is influenced by error factors impossible to be precisely captured in the light of economic quantitative models. The hazard component of the volatility, especially in the context of a financial crisis, was frequently analyzed by researchers (Chiara, 2013;[START_REF] Jurado | Measuring Uncertainty[END_REF]. Bloom (2014) brought forth that error term captures even a cyclical component, prevailing especially in periods of economic turbulences, (i.e. it just rises sharply in recessions and decrease in booms). This means that in the context of a financial crisis, the unpredictability factor and implicitly the error term is more likely than during the upturn phase of the economic cycle, capturing inclusively auto-correlation effects. The potential explanations for this cyclical feature pertain both to micro and macro endogenous shocks, outlining the mixture between idiosyncratic and systemic dimensions, with deep implications for the auto-correlation phenomenon of the residuals. analysis, Qatar has faced the global crisis from a position of strength and used their resources to stabilize the financial sector. Overall, in spite of a series of shocks, there has been no systemic breakdown and the impact on banking system has been moderate.

For the AB bank, the i  value over the crisis period is higher than its pre-crisis value, but lower than its post-crisis value. Regarding the QIIB bank, the i  value over the crisis period is higher than its pre-and post-crisis values'. Fig. 3 illustrates the time-varying beta for all banks. The time-varying beta has been calculated based on Eq.4. The time-varying beta is more volatile during the financial crisis: this can be seen in all of the figures. The estimation of Eq.5 should help us to understand the reasons behind the increase in the volatility during the crisis period.

The statistic output reveals the intensity of the contagion phenomenon at the group level; even if at the country level there appeared slight differences in terms of i  magnitude over versus the pre-and post-crisis period, at the group level the volatility generated by the financial crisis is overwhelming.

It can be concluded that Qatar banking system could not act as stabilizer for the region; at the group level, banks revealed a significant vulnerability to the shocks propagated by the financial turbulences, bringing forth the important exposure to global risk factor. This finding can have deep implications for the risk management systems of banks.

Tables 8, 9 and 10 show the results of the beta regression during the crisis period. We observe that all α1 (i.e. one for each bank), measuring the effect of the conditional volatility of the individual bank on the beta of the bank, are positive and significant. This result confirms the direct effect of the banks volatilities on respective betas.

The size in absolute values of significant coefficient is greater than unity, implying a size effect of the bank's volatility on betas. This finding is in line with [START_REF] Moussa | Bank stock volatility and contagion: empirical investigation with application of multivariate GARCH models[END_REF] who unveiled that larger financial institution exhibit a higher systematic risk in comparison with the smaller ones, although their overall risk is not significantly different from the latter. Larger banks are more exposed to systemic risk and their beta implicitly captures this enhanced sensitivity.

By adding the dummy variable to the volatility of the bank, we get significant and negative 𝛾 1 coefficients in all cases, except of SIB which presents a positive 𝛾 1 coefficient. The negative sign of 𝛾 1 implies that the bank's volatility affects inversely the beta during the crisis period, in line with the conclusions of [START_REF] Adam | Time-Varying Betas of Banking Sectors[END_REF] who unveiled the predominance of the systemic dimension in the context of a financial crisis where the transmission of shocks is enhanced in the virtue of the international banking network During stressed periods, banks' beta is mainly under the impact of systemic shocks while the idiosyncratic dimension of volatility reduces its intensity. This finding is in opposition with the conclusions related to the 𝛼 2 coefficients, which measure the effect of the conditional volatility of the market on the bank's beta; the statistic output highlights that it is significant and negative for all banks. In other words, the negative volatility coefficient implies that as volatility increases beta decreases.

Taking into account the negative impact of bank's volatility on beta, we would expect a positive effect of the market conditional volatility. This aspect can be explained in light of the size effect that is also brought forth by the absolute values of 𝛼 2 reflect the size of the banks' volatilities on their betas. All values are slightly higher than unity. 𝛾 2 shows a positive and significant sign for all banks in all countries, except for SIB and AB. The absolute values of 𝛾 2 are greater than one, which reflects a size effect of the market volatility on the beta of the bank over time. The values of 𝛾 1 coefficients in absolute value are slightly higher than unity reflecting the size effect of the banks volatilities during crisis period on the banks betas.

These findings are in line with [START_REF] Yang | Bank stock returns, leverage and the business cycle[END_REF] who revealed that larger and implicitly more profitable banks tend to be less correlated with the market return, facing therefore a lower risk premium.

Tables 11, 12 and 13 synthesize the results of the second beta regression, where the dummy variable equals 1 during "crisis and post crisis" period. α1 coefficients are positive and significant for all banks, reflecting the direct effect of the conditional volatility of the individual bank on the beta of the bank. The 𝛾 1 coefficients are negative and significant for all banks, except of NBAD bank in case of which 𝛾 1 coefficient is positive and significant. The negative sign of 𝛾 1 implies that the bank's volatility affects inversely the beta during the "crisis and post crisis" period. Regarding NBAD bank, the positive sign of 𝛾 1 reflects the pro-cyclicity of the volatility of this bank and its beta.

For all banks, except of the SIB bank, 𝛼 2 coefficients are negative and significant, while 𝛾 2 coefficients are positive and significant. This implies that there is an inverse relationship between market volatility and the betas of the banks (except of the SIB bank). According to the absolute values of coefficients 𝛼 2 and 𝛾 2 , both the bank and market volatilities during the crisis seem to impose a large size effect on the time varying beta of the bank. This aspect sets forth the alpha effect: banks with a lower beta might capture a significant alpha, being characterized by significantly lower distress during the crisis. In opposition, previous researchers pointed out that banks with a high beta exhibit, ex-post, significantly higher systematic risk [START_REF] Altunbas | Bank risk during the financial crisis: do business models matter?[END_REF].

Conclusions

This paper shows that Islamic banks are more resilient than conventional banks during crisis times. In fact, Islamic banks seem to act as a stabilizer of the global financial system. The absence of the interest rate from the Islamic finance universe could provide some stability to Islamic banks when compared to conventional ones. Depositors in Islamic banks like shareholders hold shares whose value fluctuates depending on the bank's performance. The flexibility of the value of those shares allows the Islamic banks to adapt and regulate imbalances on capital market. When compared to the interest-based system, the Islamic system based on profit and loss sharing conduct to amortization in a more rapid and flexible manner of banking and financial crises, since the nominal value of investment deposits is not guaranteed, but depends on the performance of the bank.

Given the specific features of conventional and non-conventional banking, this paper brings forth the impact of the financial crisis on both Islamic and conventional banks in three GCC countries: Qatar, Kuwait and Abu Dhabi in UAE. In a first stage, a diagonal BEKK model has been constructed to calculate the daily beta for each bank of the sample over the period that lie between 31/12/2004 and 7/11/2014. In a second stage, the calculated betas were used as endogenous variables to estimate the extent to which the conditional variances of the banks and of the sectorial capital stock markets (exogenous variables) explain the dynamics. We also introduced dummy variables to detect the effect of the crisis on the betas during the crisis period.

We find that Islamic and conventional banks have been largely affected by the crisis in the same way, except of Ahli Bank (Qatar) and the Sharjah Islamic Bank (Abu Dhabi) that were less affected by the crisis. The results also show that small banks (Islamic and conventional) have been less affected than larger banks. These results are in line with other studies which have found that Islamic banks are not more resilient than conventional ones. This economic result can be explained by the structure of the Islamic-bank financing that marginalizes the PLS-based instruments. This turns out to be consistent with the economic reality in the GCC countries, since the Islamic banks engage much more in non-participatory activities which are, technically, conventional-like products.

  vector containing the returns of the asset (𝑖) and the market portfolio (𝑚), 𝜇 𝑡 is a (2×1) mean vector of 𝑦 𝑡 , 𝜀 𝑡 is a (2×1) matrix of the conditional covariance, t H is the conditional variance matrix, 𝐶 is a (2×2) lower triangular matrix with intercept parameters, and 𝐴 𝑘𝑖 (𝑖 = 1, … , 𝑞 and 𝑘 = 1, … , 𝐾) and kj B (𝑗 = 1, … , 𝑝 and 𝑘 = 1, … , 𝐾) are (2×2) matrices of parameters.

  Figure 1: Sample selection procedure

1

   and 2 capture the potential additional effect of the bank and market conditional volatility due to the financial crisis. If 1

Figure

  Figure 2: The mean betas Abu Dhabi Kuwait Qatar

Figure 3 :

 3 Figure 3: Time-varying betas

Table 1 : Retained banks

 1

		Conventional Banks	Islamic Banks
		H	L	H	L
	Abu Dhabi	National Bank of Abu Dhabi (NBAD)	Commercial Bank Intl. (CBI)	Abu Dhabi Islamic Bank (ADIB)	Sharjah Islamic Bank (SIB)
	Kuwait	National Bank of Kuwait (NBK)	Burgan Bank (BB)	Kuwait Finance House (KFH)	Kuwait Intl.Bank (KIB)
	Qatar	Qatar National Bank (QNB)	Ahli Bank (AB)	Qatar Islamic Bank (QIB)	Qatar Intl. Islamic Bank (QIIB)
	H and L refer to highest and lowest capitalization respectively.

  31/12/2004 to 15/09/2008 and from 01/04/2009 to 07/11/2014, and the value 1 for the crisis period. For the second test, the dummy variable takes the value 1 from 16/09/2008 until the end of our study period (i.e. 07/11/2014). The determination of crisis period is based on the information provided by the Bank for International Settlements and the Federal Reserve Bank of St. Louis. Our regression is given by:

Table 2 : Abu Dhabi MA-GARCH (1,1) (BEKK) banks results

 2

	Bank	Cst1	Θ1	Cst2	Θ2	C11	A11	B11	C22	A22	B22	C12	L
	ADIB	-0.0022 -0.1168 (-1.99) (-1.80)	-0.0021 -0.0811 0.0082 0.4145*** 0.9046*** 0.0076*** 0.4262*** 0.9046*** 0.0009 7075.0 (-2.27) (-1.28) (1.67) (6.39) (37.58) (6.39) (6.29) (37.58) (0.51)
	CAI	-0.0019 -0.1283 (-1.99) (-2.03)	-0.0022 -0.0577 0.0071 0.4145*** 0.9101*** 0.0104*** 0.4354*** 0.9002*** 0.0018 6106.6 (-1.77) (-1.02) (1.66) (6.86) (42.07) (6.85) (7.00) (39.16) (0.91)
	NBAD	-0.0024 -0.1578 (-2.20) (-2.39)	-0.0027 -0.1216 0.0064 0.4085*** 0.9127*** 0.0052*** 0.4362*** 0.8985*** 0.0090* 7779.3 (-1.98) (-1.78) (1.82) (7.48) (44.93) (4.57) (7.42) (37.47) (2.61)
	SIB	-0.0020 -0.2078** -0.0035* -0.1500 0.0068 0.3955*** 0.9185*** 0.0068* 0.4218*** 0.9067*** 0.0036 6839.9 (-1.72) (-3.33) (-3.08) (-2.40) (1.54) (6.27) (35.46) (3.31) (5.83) (27.88) (0.91)
	Test for serial correlation in the residuals
			ADIB	CBI		NBAD		SIB
	Standardized squared residuals
	LB Bank	14.1299	13.7651	11.5098	6.8716
	LB Market	8.6949	25.0536	32.6445	70.7850
	Standardized residuals
	LB Bank	91.6717	152.5390	45.8853	66.1867
	LB Market	41.5505	66.7722	50.3343	60.1115
	LB=Ljung-Box statistics for serial correlation of the order 10.

***, **, * imply significance at the 1%, 5% et 10% levels respectively. T-statistics in parentheses; L = log likelihood function value.

Table 3 : Kuwait MA-GARCH (1,1) (BEKK) banks results

 3 5% et 10% levels respectively. T-statistics in parentheses; L = log likelihood function value.

	Bank	Cst1	Θ1	Cst2	Θ2	C11	A11	B11	C22	A22	B22	C12	L
	BB	-0.0002 -0.1154 (-0.28) (-2.09)	0.0003 -0.0770 0.0032 *** 0.3887 *** 0.9214 *** 0.0051 *** 0.4146 *** 0.9100 *** 0.0019 7809.6 (0.45) (-1.50) (4.36) (6.76) (51.49) (5.70) (6.97) (45.38) (2.33)
	KFH	0.0002 (0.36)	-0.1818 0.0007 * -0.1463 0.0030 ** (-2.92) (1.16) (-2.44) (3.38)	0.369 7*** 0.9291 *** 0.0032 ** 0.3911 *** 0.9194 *** 0.0037 * (5.68) (43.07) (3.84) (5.71) (36.42) (2.58)	8340.7
	KIB	-0.0001 -0.1304 -0.0005 -0.0945 0.0031 ** 0.3902 *** 0.9207 *** 0.0062 *** 0.4227 *** 0.9063 *** 0.0040 * (-0.23) (-2.32) (-0.56) (-1.71) (3.79) (6.03) (41.75) (0.92) (5.72) (33.56) (3.00)	7618.6
	NBK	0.0002 -0.1525 * 0.0009 -0.1038 (0.31) (-2.66) (1.23) (-1.91)	0.0030 * (2.67)	0.3804 *** 0.9248 *** (4.59) (30.59)	0.0038 * (2.60)	0.3970 *** 0.9170 *** 0.0035 8559.8 (4.81) (29.17) (2.53)
	Test for serial correlation in the residuals
			BB	KFH		KIB	NBK
	Standardized squared residuals
	LB Bank		41.1825	76.4514		49.4054	36.2285
	LB Market		121.2770	100.7820		105.0270	58.4250
	Standardized residuals
	LB Bank		60.8093	4.5317		56.7920	46.4087
	LB Market		43.1821	72.5232		50.5364	76.7154

***, **, * imply significance at the 1%,

Table 4 : Qatar MA-GARCH (1,1) (BEKK) banks results

 4

	Bank	Cst1	Θ1	Cst2	Θ2	C11	A11	B11	C22	A22	B22	C12	L
	AB	-0.0012 -0.1479 * -0.0016 -0.1115 0.0054 * 0.3928 *** 0.9196 *** 0.0070 *** 0.4193 *** 0.9078 *** 0.0043 7048.44 (-1.45) (-3.01) (-1.66) (-2.42) (2.99) (5.78) (39.37) (5.05) (5.71) (33.52) (2.16)
	QIIB	-0.0020 0.0403 -0.0016 0.0064 (-1.55) (0.49) (-1.33) (0.09)	0.0061 (2.25)	0.4259 *** 0.9048 *** 0.0055 * 0.4040 *** 0.9147 *** 0.0042 8006.26 (6.36) (35.17) (3.85) (6.55) (40.19) (1.62)
	QIB	-0.0009 -0.0977 -0.0005 -0.0715 0.0055 (-1.09) (-1.57) (-0.56) (-1.23) (2.54)	0.4026 *** 0.9154 *** 0.0052 * 0.4214 *** 0.9058 *** 0.0068 * 8398.68 (5.54) (34.14) (2.82) (5.80) (31.94) (2.59)
	QNB	-0.0008 -0.1676 -0.0010 -0.1611 0.0053 (-0.95) (-2.15) (-1.04) (-2.05) (2.33)	0.3607 *** 0.9327 *** 0.0034 0.3809 *** 0.9242 *** 0.0064 * 8882.68 (6.23) (46.11) (2.45) (6.25) (43.33) (2.85)
	***, **, * imply significance at the 1%, 5% et 10% levels respectively.
	T-statistics in parentheses; L = log likelihood function value.
	Test for serial correlation in the residuals
		AB		QIIB	QIB	QNB
	Standardized squared residuals
	LB Bank	70.2348	18.6862	3.7257	2.8392
	LB Market	150.053	23.0453	108.964	174.514
	Standardized residuals
	LB Bank	58.8493	46.9958	21.1761	21.7466
	LB Market	52.6484	54.7248	27.7436	55.0237
	***, **, * imply significance at the 1%, 5% et 10% levels respectively.
	T-statistics in parentheses; L = log likelihood function value.

Table 5 : Abu Dhabi mean beta during all periods

 5

	Banks	ADIB	CBI	NBAD	SIB
	Total Period 31-12-2004 -----07/11/2014	0.8226	0.7814	0.9648	0.8344
	Pre-crisis period 31-12-2004 -----15/09/2008	0.5750	0.4841	0.9453	0.5797
	Crisis period 16/09/2008 -----31/03/2009	0.9681	0.9821	0.9863	1.0096
	Crisis and post-crisis period 01/04/2009 -----07/11/2014	0.9715	0.9602	0.9765	0.9875

Table 6 : Kuwait mean beta during all periods

 6

	Banks	BB	KFH	KIB	NBK
	Total Period 31-12-2004 -----07/11/2014	0.8942	1.0320	0.9617	1.0034
	Pre-crisis period 31-12-2004 -----15/09/2008	0.7450	1.0553	0.9289	1.0153
	Crisis period 16/09/2008 -----31/03/2009	0.9868	1.0356	0.9591	1.0069
	Crisis and post-crisis period 01/04/2009 -----07/11/2014	0.9839	1.0180	0.9814	0.9962

Table 7 : Qatar mean beta during all periods

 7

	Banks	AB	QIIB	QIB	QNB
	Total Period 31-12-2004 -----07/11/2014	0.9006	0.9495	1.0183	1.0003
	Pre-crisis period 31-12-2004 -----15/09/2008	0.7681	0.8779	1.0712	1.0199
	Crisis period 16/09/2008 -----31/03/2009	0.9780	1.0158	0.9525	0.9708
	Crisis and post-crisis period 01/04/2009 -----07/11/2014	0.9802	0.9926	0.9865	0.9885

Table 8 : Abu Dhabi beta Crisis period test results

 8 -statistics in parentheses; CV= bank volatility; CVD= bank volatility x time dummy; MV= market volatility; MVD= market volatility x time dummy ; DW=Durbin-Watson statistics; SSE=standard error of estimate; SSR=sum of square residuals.

	Betat		Constant				Dummy				CVt			CVDt	MVt	MVDt	Rho	Diagnostics
	ADIB		0.8270 ***				0.0686			3.9621 ***			-3.2230 ***	-4.1373 ***	3.4033 ***	0.9410 ***	R 2 = 0.143, DW= 1.90 ,
				(27.80)				(1.19)				(9.76)			(-6.62)	(-8.36)	(5.80)	(140.00)	SEE= 0.269 , SSR= 159.2
	CBI		0.7705 ***				0.1387			3.0939 ***			-2.5264 ***	-3.1685 ***	2.6191 **	0.9252 ***	R 2 = 0.145, DW= 2.12,
				(24.50)				(1.90)				(7.32)			(-4.80)	(-5.86)	(3.98)	(123.00)	SEE= 0.321, SSR= 226.6
	NBAD		1.0014 ***				0.0043			3.6079 ***			-3.0087 ***	-3.8787 ***	3.2518 ***	0.9159 ***	R 2 = 0.158, DW= 1.99 ,
				(59.50)				(0.10)				(10.10)			(-6.85)	(-8.69)	(5.96)	(115.00)	SEE= 0.183, SSR= 72.9
	SIB		0.8376 ***				0.0410			2.2267 ***			-1.8404	-2.2565 **	1.8681	0.9268 ***	R 2 = 0.128, DW=2.26 ,
				(33.70)				(0.72)				(4.52)			(-2.36)	(-3.92)	(2.05)	(125.00)	SEE= 0.248, SSR= 137.8
	, i t Beta	0    	0	t D		1 CV i t , 			1	( CV D it t	)			2	t MV			2	(	t t MV D	)		t

 ; ***, **, * imply significance at the 1%, 5% et 10% levels respectively. T

Table 9 : Kuwait beta Crisis period test results

 9

	Betat	Constant				Dummy			CVt		CVDt	MVt	MVDt	Rho	Diagnostics
	BB	0.8863 *** (42.90)				0.0490 (0.95)				3.0770 *** (4.78)		-2.5096 ** (-3.45)	-3.1888 *** (-4.07)	2.6092 * (2.97)	0.9157 *** (115.00)	R 2 = 0.088, DW= 1.93, SEE= 0.217, SSR= 110.2
	KFH	1.0327 ***				0.0659				6.4767 ***		-5.8367 ***	-6.9050 ***	6.2123 ***	0.9177 ***	R 2 = 0.047, DW= 2.02,
	KIB NBK , i t Beta   0  	(76.20) 0.9579 *** (47.20) 1.0046 *** (90.40) 0 1 t D CV i t ,  			1	(1.94) 0.0855 (1.44) 0.0087 (0.29) ( ) it t CV D			2	(9.28) 3.3609 *** (4.76) 4.4253 *** (7.59) 2 ( t MV MV D t t  	)	(-5.67) -2.7402 * (-3.29) -3.9056 *** (-5.55) t   ; ***, **, * imply significance at the 1%, 5% et 10% levels respectively. (-9.33) (5.67) (116.00) -3.6438 *** 2.9743 * 0.8950 *** (-4.12) (2.86) (101.00) SEE= 0.233, SSR= 134.2 SEE= 0.141, SSR= 48.9 R 2 = 0.038, DW= 1.95, -4.7086 *** 4.1579 *** 0.9055 *** R 2 = 0.060, DW= 2.01, (-7.30) (5.33) (108.00) SEE= 0.124, SSR= 37.2
	T-statistics in parentheses; CV= bank volatility; CVD= bank volatility x time dummy; MV= market volatility; MVD= market volatility x time dummy ; DW=Durbin-Watson
	statistics; SSE=standard error of estimate; SSR=sum of square residuals.

Table 10 : Qatar beta Crisis period test results

 10

	Betat		Constant				Dummy					CVt		CVDt	MVt	MVDt	Rho	Diagnostics
	AB	0.8431 ***				0.2014 **			3.3924 ***		-2.3395 **	-3.3935 ***	2.1050	0.8981 ***	R 2 = 0.154, DW= 2.03,
					(42.70)				(3.58)					(5.99)		(-3.46)	(-4.43)	(2.31)	(101.00)	SEE= 0.227, SSR= 112.5
	QIIB	0.8702 ***				0.1574 *			3.4526 ***		-2.3462 ***	-3.1103 ***	1.9428 *	0.8983 ***	R 2 = 0.191, DW= 2.05,
					(49.90)				(3.16)					(10.10)		(-5.90)	(-6.77)	(3.69)	(102.00)	SEE= 0.213, SSR= 93.6
	QIB	0.9679 ***				0.0001			4.4321 ***		-3.7387 ***	-4.8757 ***	4.1003 ***	0.8824 ***	R 2 = 0.129, DW= 1.92,
					(69.80)				(0.00)					(11.80)		(-8.50)	(-9.32)	(6.78)	(93.30)	SEE= 0.175, SSR= 68.5
	QNB	1.0169 ***				-0.0710 *			6.5347 ***		-6.1852 ***	-7.0467 ***	6.7127 ***	0.9033 ***	R 2 = 0.008, DW= 1.87,
				(115.00)				(-2.90)				(8.96)		(-6.92)	(-8.69)	(6.76)	(105.00)	SEE= 0.095, SSR= 23.4
	, i t Beta	0    	0	t D		1 CV i t , 			1	( CV D it t	)			2	t MV			2	(	t t MV D	)	t  ; ***, **, * imply significance at the 1%, 5% et 10% levels respectively. 
	T-statistics in parentheses; CV= bank volatility; CVD= bank volatility x time dummy; MV= market volatility; MVD= market volatility x time dummy ; DW=Durbin-Watson
	statistics; SSE=standard error of estimate; SSR=sum of square residuals.

Table 11 : Abu Dhabi beta Crisis and post crisis period test results

 11 ; ***, **, * imply significance at the 1%, 5% et 10% levels respectively. T-statistics in parentheses; CV= bank volatility; CVD= bank volatility x time dummy; MV= market volatility; MVD= market volatility x time dummy ; DW=Durbin-Watson statistics; SSE=standard error of estimate; SSR=sum of square residuals.

	Betat		Constant				Dummy					CVt				CVDt	MVt	MVDt	Rho	Diagnostics
	ADIB		0.6099 *** (24.60)				0.3468 *** (11.30)			11.9573 *** (10.70)			-10.5013 *** (-9.22)	-6.7972 *** (-7.87)	5.3281 *** (5.88)	0.8903 *** (98.70)	R 2 = 0.521, DW= 1.87 , SEE= 0.269, SSR= 88.9
	CBI		0.4986 *** (19.00)				0.4436 *** (13.50)			10.6036 *** (8.01)			-9.2362 *** (-6.86)	-5.3588 *** (-4.75)	3.9662 ** (3.39)	0.8592 *** (84.80)	R 2 = 0.520, DW= 2.06, SEE= 0.321, SSR= 127.2
	NBAD		1.0206 *** (41.90)				-0.0399 (-1.34)			-7.7966 *** (-8.04)				8.9114 *** (9.44)	-6.1831 *** (-8.74)	5.0366 *** (6.63)	0.9117 *** (112.00)	R 2 = 0.216, DW= 1.99, SEE= 0.183, SSR= 67.9
	SIB		0.6208 *** (31.90)				0.3554 *** (14.50)			4.5570 *** (4.27)				-3.0607 * (-2.67)	-1.3837 (-1.61)	-0.2475 (-0.25)	0.8536 *** (82.30)	R 2 = 0.535, DW= 2.18, SEE= 0.248, SSR= 73.4
	, i t Beta	0    	0	t D		1 CV i t , 			1	it t CV D (	)			2	t MV			2	(	t t MV D	)			t

Table 12 : Kuwait beta Crisis and post crisis period test results

 12 ; ***, **, * imply significance at the 1%, 5% et 10% levels respectively. T-statistics in parentheses; CV= bank volatility; CVD= bank volatility x time dummy; MV= market volatility; MVD= market volatility x time dummy ; DW=Durbin-Watson statistics; SSE=standard error of estimate; SSR=sum of square residuals.

	Betat	Constant			Dummy					CVt					CVDt	MVt	MVDt	Rho	Diagnostics
	BB	0.7532 ***		0.2159 ***			18.2286 ***			-17.2216 ***	-17.1778 ***	16.1659 ***	0.8833 ***	R 2 = 0.325, DW= 1.91,
			(33.10)				(7.60)						(8.30)				(-7.77)	(-7.45)	(6.93)	(95.10)	SEE= 0.217, SSR= 81.5
	KFH	1.0331 *** (52.30)			0.0017 (0.07)				15.1766 *** (9.30)			-12.9161 *** (-7.51)	-15.3276 *** (-9.85)	12.9031 *** (7.78)	0.9139 *** (113.00)	R 2 = 0.122, DW= 2.03, SEE= 0.141, SSR= 45.1
	KIB	0.9085 *** (30.80)			0.0815 (2.21)				18.4323 *** (7.73)			-17.3980 *** (-7.21)	-19.1571 *** (-6.88)	18.0782 *** (6.40)	0.8911 *** (99.10)	R 2 = 0.082, DW= 1.94, SEE= 0.233, SSR= 128.12
	NBK	1.0117 *** (63.10)		-0.0164 (-0.83)				15.9079 *** (11.00)			-15.0541 *** (-10.20)	-15.8599 *** (-11.10)	14.9788 *** (10.10)	0.9013 *** (105.00)	R 2 = 0.136, DW= 2.02, SEE= 0.124, SSR= 34.2
	, i t Beta	0    	0	t D		1 CV i t , 			1	( CV D it t	)			2	t MV			2	(	t t MV D	)			t

Table 13 : Qatar beta Crisis and post crisis period test results

 13 ; ***, **, * imply significance at the 1%, 5% et 10% levels respectively. T-statistics in parentheses; CV= bank volatility; CVD= bank volatility x time dummy; MV= market volatility; MVD= market volatility x time dummy ; DW=Durbin-Watson statistics; SSE=standard error of estimate; SSR=sum of square residuals.

	Betat	Constant		Dummy					CVt					CVDt	MVt	MVDt	Rho	Diagnostics
	AB	0.6907 ***		0.2842 ***				95.6087 ***			-93.7985 ***	-116.9270 *** 114.8370 ***	0.9048 ***	R 2 = 0.314, DW= 2.01,
			(22.60)			(7.49)					(13.60)				(-13.40)	(-13.00)	(12.80)	(105.00)	SEE= 0.227, SSR= 91.1
	QIIB	0.7510 *** (34.70)		0.1909 *** (7.00)				2.3712 *** (13.50)			-0.8188 *** (-10.30)	2.6512 (2.15)	-4.3768 ** (-3.59)	0.8742 *** (90.90)	R 2 = 0.296, DW= 2.03, SEE= 0.213, SSR= 82.6
	QIB	0.9576 *** (48.90)			0.0013 (0.05)					33.2304 *** (22.70)			-31.8703 *** (-21.60)	-40.7973 *** (-19.00)	39.3650 *** (18.20)	0.8830 *** 995.10)	R 2 = 0.138, DW= 1.95, SEE= 0.175, SSR= 67.8
			1.0040 ***		-0.0130				129.6840 *** -128.1570 ***	-139.1860 *** 137.5770 ***	0.9162 ***	R 2 = 0.052, DW= 1.87,
	QNB	(79.10)			(-0.84)					(33.10)				(-32.60)	(-32.70)	(32.30)	(117.00)	SEE= 0.095, SSR= 22.4
	, i t Beta	0    	0	t D		1 CV i t , 			1	it t CV D (	)			2	t MV			2	(	t t MV D	)			t

d We refer to[START_REF] Dusuki | Banking for the Poor: The Role of Islamic Banking in Microfinance Initiatives[END_REF] for fundamental distinctions between Islamic and conventional banks. e For the U.A.E we distinguish between Dubai and Abu Dhabi as they each have a separate stock market


S M Wahidur Rahaman

Jean-Claude Daran

Eric Manoury

Rinaldo Poli
email: rinaldo.poli@lcc-toulouse.fr.

S M Wahidur Rahaman

The cyclooctadiene ligand in [IrCl(COD)]2 is hydrogenated under transfer hydrogenation conditions: a study in the presence of PPh3

Keywords: Iridium, pre-catalyst activation, transfer hydrogenation, cyclooctadiene, triphenylphosphine, hydride ligands

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Transfer hydrogenation is a catalyzed process that allows unsaturated compounds such as aldehydes, ketones and imines to be reduced by a hydrogen donor DH 2 , which is transformed to a by-product D, typically isopropanol giving acetone or formic acid giving CO 2 [1][2][3][4][5][6][7]. Hence, it performs the same transformations as hydrogenation but, relative to the latter, presents the main advantage of removing the hazards associated to the use of the gaseous and flammable H 2 .

However, because of the smaller thermodynamic driving force relative to hydrogenation, harsher conditions -namely higher temperatures -are generally required. Catalysts used to accelerate this reaction span from Lewis acids, which operate through the Meerwein-Ponndorf-Verley mechanism, to unsaturated transition metal hydrides that are able to promote a coordination/insertion mechanism, to transition metal complexes with π-loaded ligands such as amido functions able to operate via an outer-sphere (the so-called Noyori-Morris) mechanism [8,9]. Many transition metal complexes are able to promote both hydrogenation and transfer hydrogenation and a strong base is generally required in both cases to achieve high activity.

Rhodium and iridium complexes obtained from [MX(COD)] 2 (M = Rh, Ir; X = Cl, OMe) in the presence of various ligands have received considerable attention as pre-catalysts for transfer hydrogenation . The nature of the active species and the mechanism have been discussed in several experimental and computational contributions [14,40,[55][56][57][58][59][60][61][62]. It seems that a different mechanism may be adopted depending on the nature of the metal and of the supporting ligand. For instance, working with a computational model of the [RhCl(COD)] 2 /chiral diamine precatalysts used in the Lemaire group, Delbecq et al. find that only the outer sphere mechanism is able to account for the observed enantioselectivity of the reaction [57,58]. Oro et al., on the other hand, conclude that systems generated from [Ir(µ-OMe)(diene)]/PR 3 function through a coordination/insertion mechanism [60].

One crucial question is whether the diene ligand in the precatalyst remains coordinated to the metal in the catalytically active species or whether it is displaced, either as such or in a hydrogenated form. It seems clear that COD is removed under hydrogenation conditions (i.e. under H 2 ), but whether this occurs under transfer hydrogenation conditions (particularly in warm isopropanol) does not bring consensus. It has been shown that certain systems differing only by the nature of the diene have different activity. For instance, Lemaire et al. have shown that the activity trend at room temperature of the [RhCl(diene)] 2 /diamine/KOtBu/iPrOH system as the diene is changed goes as COD > norbonadiene > hexadiene > (ethylene) 2 [55]. Similarly, the iridium system [IrCl(diene)] 2 /aminosulfide/HCOOH/NEt 3 has shown activities in the order COD >> (COE) 2 at 60°C. These observation have led the authors to propose that the diene or alkene remains metal bonded in the active species [18]. On the basis of these reports, the abovementioned computational investigations on Rh-and Ir-catalyzed transfer hydrogenation cycles were carried out on COD-containing systems, or models thereof [55][56][57][58][59][60].

However, evidence for COD release from iridium complexes under transfer hydrogenation conditions has been gathered since quite some time ago. Kvintovics where COD is not present, while a GC/MS analysis during catalysis with the COD derivative shows the presence of cyclooctene [61]. Finally, Oro et al. have recently show that addition of

sodium isopropoxide to [(COD)Ir{κ 3 PCP-1,3-(Ph 2 PCH 2 CH 2 )-2-CH 2 Im}] + PF 6
-affords free COD upon heaying to 80°C [65]. It should also be underlined that certain Rh and Ir complexes are known to be active catalysts for the transfer hydrogenation from alcohols to olefins [66][67][68][69][70].

In this contribution, we report our investigations of the reaction between [IrCl(COD)] 2 and PPh 3 under a variety of conditions, including those typical of catalytic transfer hydrogenation. Indeed, in work by the Oro's group, complex [IrH(COD)(PPh 3 ) 2 ], which is accessible directly from [IrCl(COD)] 2 and PPh 3 in the presence of base, was proven an efficient precatalyst for the transfer hydrogenation of cyclohexanone (62% conversion in 24 h in isopropanol at 60°C with a 1% catalys loading) [71]. However, a detailed investigation of the high-temperature activation has not been previously reported for this system to the best of our knowledge. We will show here that the COD ligand is hydrogenated and removed from the metal upon warming. Our investigation has retraced a few already well established transformations, but has also brought to the surface a few unexpected and surprising results.

Experimental

General

All reactions were carried out under an argon atmosphere using standard Schlenk techniques.

Reaction of [Ir(µ-Cl)(COD)] 2 and PPh 3 in isopropanol in the presence of KOtBu at the reflux temperature: generation of fac-[IrH 3 (PPh 3 ) 3 ]

PPh 3 (72 mg, 0.274 mmol) was added to an isopropanol solution (10 mL) of [Ir(µ-Cl)(COD)] 2 (30 mg, 0.045 mmol) and the solution was stirred at room temperature for 3 hours. Potassium tertbutoxide (50 mg, 0.446 mmol) was added to the mixture and refluxed for 3 hours, with NMR monitoring (see Results and Discussion). The yellow solution became colorless with generation of a white solid. The precipitate was filtered off and the filtrate was concentrated to dryness under reduced pressure. Yield: 70 mg (80%, based on iridium). 1 H NMR (400 MHz, CDCl 3 ):  7.64-6.82 (m, 45H, Ph), -12.25 (AA'A''XX'X'', 3H). 31 P { 1 H} NMR (CDCl 3 , 400 MHz):  9.3 (s).

Colorless crystals suitable for X-ray diffraction were obtained as a CDCl 3 solvate from the NMR solution upon standing.

In a separate experiment, the same procedure was repeated using [Ir(µ-Cl)(COD)] 2 (80 mg, 0.119 mmol), PPh 3 (124 mg, 0.473 mmol), KOtBu (132 mg, 1.18 mmol) and mesilylene (10 mg) as an internal standard in 7 mL of iPrOH. After 3 h of heating at 85°C, gas-chromatographic analysis of the solution revealed the generation of cyclooctene (54.4 %) and cyclooctane (32.2 %), while a peak at the elution time expected for cyclooctadiene was not observed.

X-ray structural analysis.

A single crystal of fac-[IrH 3 (PPh 3 ) 3 ]•CDCl 3 was mounted under inert perfluoropolyether at the tip of glass fiber and cooled in the cryostream of a Bruker APEXII diffractometer fitted with a Mo microfocus source. The structure was solved by direct methods (SHELXt) [72] and refined by least-squares procedures on F 2 using SHELXL-97 [73]. All H atoms attached to carbon were introduced in calculation in idealised positions and treated as riding models. The Ir atom as well as the C atom of the chloroform solvate are located on a 3-fold axis and then only a third of the whole molecule defines the asymmetric unit. The search for the H hydride in the region where it could be expected gave one peak which could be attributed to the hydride. This H was freely refined isotropically and gave satisfactory results. The drawing of the molecules was realised with the help of ORTEP32 [74,75]. Crystal data and refinement parameters are shown in Table 1.

Results and discussion

The interaction between [IrCl(COD)] 2 and PPh 3 , at various P/Ir ratios, in isopropanol solution was investigated sequentially under three different conditions. First, the phosphine ligand was added to the complex at room temperature in the absence of a base. Then, a strong base was added at room temperature. Finally, the resulting solution was warmed up to the reflux temperature. [IrCl(COD)(PPh 3 )], as previously described [76]. We have only been able to find 1 H NMR characterization for this compound in the literature [77,78]. We observe the phosphine resonance in the 31 P { 1 H} NMR spectrum at δ 21.7. When the amount of PPh 3 was raised to 2 equiv per Ir, only one broad resonance was observed at an average position between those of [IrCl(COD)(PPh 3 )] and free PPh 3 , indicating rapid self-exchange between free and coordinated ligand (equation 1, Figure 1). This shows that the formation of a 5-coordinate bistriphenylphosphine adduct is not favorable.

[

IrCl(COD)(PPh 3 )] + PPh 3 PPh 3 + [IrCl(COD)(PPh 3 )] (1)
The NMR ( 1 H, 31 P) properties of the product of equation 2 ( 1 H triplet at δ -13.77 with J HP = 21.9 Hz, collapsing to a singlet upon broadband P decoupling; 31 P{ 1 H} singlet at δ 7.5, see Figure 1S) match those previously reported for [IrH(COD)(PPh 3 ) 2 ] [71, 82]. The 13 C NMR spectrum appears to be reported here for the first time. All phenyl groups are equivalent on the NMR timescale. The CH and CH 2 groups of the COD ligand give two resonances each, consistent with mirror symmetry. Their assignment was confirmed by a DEPT135 13 C NMR experiment (see Figure 2). The structure of this compound has not been elucidated, but it can reasonably be assumed identical to those of the structurally characterized analogues with the bidentate diphosphines L 2 = N(sec-Bu)(CH 2 CH 2 PPh 2 ) 2 [83] and HC(o-anisyl)(o-C 6 H 4 PiPr 2 ) 2 [84], namely trigonal bipyramidal with the H ligand and one of the two COD double bonds placed in the axial position, thus rendering the two P atoms equivalent and the two COD double bonds inequivalent, in agreement with the NMR evidence. A particular feature of this spectrum is that the resonances of the phenyl ipso, ortho and meta atoms, as well as that of the COD equatorial C atoms, which

show coupling to the P nuclei, are virtual triplets rather than doublets, consistent with a strong homonuclear P-P coupling (see excerpts in Figure 2). (see Figure 1S). Mutual coupling of these two resonances was confirmed by a H-P HMQC NMR experiment. This pattern is consistent with the presence of only one type of hydride and three equivalent PPh 3 ligands. The simplest possibility is [IrH(PPh 3 ) 3 ], analogous to the Rh complex known as Wilkinson's catalyst, which has apparently never been described. This product could result from the replacement of COD by a third PPh 3 equivalent. Having a 16-electron configuration, this compound would be expected to adopt a square planar geometry with inequivalent cis and trans phosphine ligand, but an average spectrum could result from hydride positional scrambling in the fast kinetic regime. On the other hand, an isomeric ortho-metallated complex, [IrH 2 (PPh 3 ) 2 (κ 2 :C,P-2-C 6 H 4 PPh 2 )], has been reported [85] and could also be consistent with the observed spectrum under the hypothesis of fluxional behavior, but the 1 H NMR properties reported for that compound (quartet at δ -7.7 with J PH = 15 Hz) are different than those observed here.

When the experiment was repeated with a P/Ir ratio of 10, the major Ir product was still [IrH(COD)(PPh 3 ) 2 ] and the above described by-product remained visible at low intensity (see Figure 2S), but an additional by-product (also very minor) also appeared. This is characterized by complexes have been known since the early 60's [86][87][88][89][90][91] and reported several times, including in more recent contributions [92][93][94], their NMR spectral properties have not been thoroughly described. The fac isomer is characterized by a complex 6-line pattern centered at δ -12.2 in the 1 H NMR spectrum for the AA'A"XX'X" system. This feature has been well described and simulated in a previous contribution [93]. However, we find that the 31 P resonance is located at δ 9.3 (see Figure 3) and not at the previously reported value of 30.05 [93], as confirmed by the 1 H- 31 For the mer isomer, the 31 P properties have apparently never been reported and only a variable temperature 1 H NMR spectrum in CDCl 3 has been previously described [85]. The better resolved sharpen at 243 K to reveal additional HH coupling (see Figure 6S). The 1 H{ 31 P} spectrum at 243 K collapses these two hydride resonances into a doublet and a triplet (J HH = 3.5 Hz) and the 1 H COSY (Figure 5S) further confirms their mutual coupling. These properties agree with and complete those previously reported in CDCl 3 [85]. The corresponding 31 P{ 1 H} resonances are found at δ X -21.3 (2P, doublet) and δ Y 17.1 (1P, triplet) with J PP = 15 Hz (mutual coupling confirmed by 31 P COSY) and the coupling to the above-described hydride 1 H resonances was confirmed by HMQC (Figure 5S).

Product III shows an ABXY 2 spin system. The spectral properties, shown in detail in Figure 4, are very similar but not identical to those described for mer-[IrClH 2 (PPh 3 ) 3 ] [95]. Notably, although the chemical shifts of the hydride resonances in the 1 H spectrum and of the phosphines in the 31 P spectrum and the HH and HP coupling constants are very close to those reported for this compound, the PP coupling (14.4 Hz) is about half that reported for the chloro derivative (26 Hz).

It seems likely, therefore, that the complex corresponds to mer-[IrXH 2 (PPh 3 ) 3 ], where X is an alkoxide ligand originating from the base or from the solvent. Its formation can be formally seen which is one of the proposed products, followed by β-H elimination with the generation of acetone, while the expelled COD may remain unaltered. Alternatively, thermal activation of the [IrH(COD)(PPh 3 ) 2 ] complex may lead to hydride ligand migration to the COD ligand, followed by further alcohol activation and transfer hydrogenation of COD, which would eventually be eliminated as hydrogenated products. A quantitative gas-chromatographic analysis of the isopropanol solution revealed the generation of cyclooctene (54.4%) and cyclooctane (32.2%), while cyclooctadiene was not detected. Thus, it appears that transfer hydrogenation of the COD ligand is required to accomplish the trasformation.

Another surprising feature of the above reaction is the generation of tris-PPh 3 complexes from a mixture where the PPh 3 /Ir ratio is only 2. Nevertheless, the 1 H and 31 The COD hydrogenation and elimination from the metal cordination sphere in basic isopropanol, in the absence of H 2 , is in line with the previous work already mentioned in the introduction on related systems with diphospine, diphosphinite and pyridine-functionalized Nheterocyclic carbene ligands [13,61,63]. However, whereas those previous contributions only indicated the formation of cyclooctene, we also find that a considerable amount of cyclooctane is generated in this case. A possible reason for the more extensive COD hydrogenation before expulsion in the present case is that the supporting PPh 3 ligand, through its lower binding ability and/or lack of chelate effect, dissociates more easily from the metal center allowing more isopropanol to oxidatively add and to transfer hydrogen atoms to the organic ligand before this can be expelled. However, more mechanistic work is needed before firmly rationalizing this observation. At any rate, the results described here clearly demonstrate that the previously reported transfer hydrogenation processes catalyzed by [IrCl(COD)] 2 /phosphine ligand/strong base in isopropanol at high temperature likely generate active species that are devoid of the COD ligand. In the presence of PPh 3 [71], whether the active catalyst is one or both of the isomeric [IrH 3 (PPh 3 ) 3 ] complexes or whether these are themselves precursors of the real catalyst is not yet quite clear. These componds, in our hands, appear quite stable under the conditions used for transfer hydrogenation (prolonged reflux in isopropanol in the presence of strong base). However, it cannot be excluded that they generate irreversibly minor amounts of a very active catalyst or that the catalysis is promoted, when less than 3 equivalents of PPh 3 per Ir atom are used, by the other H-and PPh 3 -free compounds that are generated by the thermal activation. It has been reported that compound [IrH 3 (PPh 3 ) 3 ] (undefined isomer), catalyzes the transfer hydrogenation of aldehydes by formic acid [97].

It is also worthy to compare the presently described behavior with that of complex diffraction study has confirmed the molecular geometry (see Figure 5). The molecule sits on a crystallographic threefold axis. Only the mer isomer was previously structurally characterized [99]. Other structurally characterized [IrH 3 L 3 ] molecules with phosphorus donor L ligands have been described with both the mer (for instance with L 3 = PhP(CH 2 CH 2 CH 2 PCy 2 ) 2 [100] or (PMe 2 Ph)(PiPr 3 ) 2 [101]) and the fac geometry (with L = PMePh 2 [102] or L 3 = N(CH 2 CH 2 PPh 2 ) 3 [103]). With respect to the closest structure of the fac-[IrH 3 (PMePh 2 ) 3 ] complex, the Ir-H distance

Conclusions

The main contribution of the present work is the unambiguous demonstration that the [IrCl(COD)] 2 /PPh 3 /KOtBu precatalyst for transfer hydrogenation in isopropanol loses the COD ligand in a hydrogenated form (mixture of cycloctene and cyclooctane) when thermally activated.

Thus, previous assumptions that this catalytic system, as well as related ones with other monodentate phosphine (and probably also bidentate diphosphines), operate by COD-containing active species must be discarded. This investigation arrives at the same conclusion as that of Jiménez et al. based on a systems with a pyridine-functionalized N-heterocyclic carbene as ligand [61]. It is of interest to verify whether this phenomenon is specific to those ligands or general for Ir-based precatalysts used as transfer hydrogenation precatalysts. It is also pertinent to ask the same question about the related Rh stystems, at least those requiring high temperatures to achieve significant activity.

Along the present study, we have also completed the NMR spectroscopic characterization of compound [IrH(COD)(PPh 3 ) 2 ] with the 13 C{ 1 H} spectrum, we have presented for the first time a

  et al. have shown that, in the presence of 2 equivalents of the chelating diphosphinite (2R,4R)-2,4-bis(diphenylphosphinoxy)pentane (BDPOP) and 10 equivalents of NaOMe, complex [IrCl(COD)] 2 loses COD within 60 min in isopropanol at room temperature to yield cyclooctene as the only detected product (no free COD and no cyclooctane) [13]. Spogliarich et al. have reported that the activation of [Ir(COD)P 2 ] + (P 2 = chiral diphosphine: chiraphos, prophos or diop) refluxing iPrOH yields COD-free compounds with brigding hydrides [63], analogous to the [Ir 2 (µ-H) 3 H 2 L 4 ] + complexes previously reported by Crabtree et al. [64], even without adding a strong base to the system. In a much more recent contribution, Jiménez et al. have shown that, in isopropanol at 80°C and in the presence of KOH, the transfer hydrogenation catalytic activity of [Ir(COD){κC-Me(o-pyCH 2 )Im} 2 ] + is identical to that of separately synthesized [IrH 2 {κC,κN-Me(o-pyCH 2 )Im} 2 ] +

Figure 1 .

 1 Figure 1. 31 P{ 1 H} NMR spectra of the [IrCl(COD)] 2 + PPh 3 reaction in isopropanol (aliquot

Figure 2 .

 2 Figure 2. 13 C{ 1 H} and DEPT135 NMR spectra of [IrH(COD)(PPh 3 ) 2 ], with expanded excerpts for

a 1 H

 1 hydride resonance at δ -11.64, characteristic of coupling with one trans P atom (large doublet coupling of ca. 280 Hz) and two cis P atoms (smaller triplet coupling of ca. 20 Hz). This pattern is also indicative of a [IrH(PPh 3 ) 3 ] stoichiometry, though this time not fluxional, compatible with an octahedral Ir III product having two additional X-type ligands. Efforts at further elucidating the nature of this product were not made. The 31 P{ 1 H} NMR spectrum confirmed the presence of [IrH(COD)(PPh 3 ) 2 ] as the major product and of the minor δ 14.3 by-product (Figure2S), while the excess free PPh 3 gave a broad resonance centered at ca. δ -3, showing dynamic exchange with one of the minor by-products. The 1 H spectrum of this mixture recorded in THF-d 8 solution showed the same products in the same relative proportion.Warming the solution obtained as in equation 2 to the reflux temperature intially leads to the rapid disappearance of the [IrH(COD)(PPh 3 ) 2 ] complex. It is still the main compound after 10 min but it is completely consumed after 1 h. Several products are initially generated, as shown by 1 H (hydride region) and 31 P{ 1 H} NMR spectroscopy (Figure3), but continued reflux led to the decrease of the unknown intermediates and the accumulation of three products I, II and III, of which I is dominant (relative ratio = ca. 81:4:15 according to the hydride resonances integration) when the spectrum is recorded in CDCl 3 . However, the spectrum of the same product mixture recorded in THF-d 8 shows compounds I and II in approximately equimolar amounts (ca. 47:53), while III was absent and other minor inidentified products are present (Figure3S). Products I and II have been identified respectively as facand mer-[IrH 3 (PPh 3 ) 3 ]. Curiously, whereas both

  Figure3) and not at the previously reported value of 30.05[93], as confirmed by the 1 H-31 P

Figure 3 .

 3 Figure 3. Monitoring by 1 H NMR in the hydride region (a) and 31 P{ 1 H} (b) of the reaction between [IrCl(COD)] 2 , PPh 3 (4 equiv) and excess NaOMe in boiling iPrOH. The spectra were taken from dried aliquots redissolved in CDCl 3 . The starred resonance is assigned to the Ph 3 PO impurity.

1 H

 1 NMR spectrum in THF-d 8 solution, in which the relative proportion of this isomer is significant, reveals the expected pattern of an A 2 BX 2 Y spin system (A,B = H; X,Y = P) for the hydride resonances: δ A -10.86 (2H, broad quartet, J HP = 16 Hz, due to degeneracy of the couplings to the 2P X and 1P Y nuclei) and δ B -12.30 (1H, broad dt, J HPY = 116 Hz; J HPX = 21 Hz), which

  as an alcohol oxidative addition to [IrH(PPh 3 ) 3 ]. It is notable that the characteristic resonances of the previously described[94,96] anionic hydrides complexes [IrH 2 (PPh 3 ) 3 ] -and [IrH 4 (PPh 3 ) 4 ] -are absent. The correlation between the 1 H and 31 P resonances is confirmed by the HMQC experiment in CDCl 3 (Figure4S).

Figure 4 . 1 H

 41 Figure 4. 1 H (a) and 31 P{ 1 H} (b) NMR properties of the minor product III resulting from the reaction between [IrCl(COD)] 2 , PPh 4 (4 equiv) and excess NaOMe in boiling iPrOH. The upper trace for the H B resonance is the simulated spectrum using the parameters indicated in the molecular diagram (in Hz units).

1 . 1 .

 11 Scheme 1.

( 1 .

 1 39(5) Å) is much shorter (cf. 1.627(4) Å) while the Ir-P distance (2.3296(5) Å) is slightly longer (cf. 2.314(2) Å), but the X-ray measurement is known to underestimate the metal-H distances, while the structure of fac-[IrH 3 (PMePh 2 ) 3 ] was more precisely determined by neutron diffraction[102]. In the structure of the isomeric mer-[IrH 3 (PPh 3 ) 3 ], also determined by X-ray diffraction, the Ir-H distances were rather unprecisely determined as 1.58(25), 1.62(28) and 1.59(26) Å, while the Ir-P distances are 2.347(3) Å for the unique P atom trans to H and 2.287(3) and 2.285(3) Å for the two P atoms trans to each other. The tendency for the Ir-P bonds to be lengthened by the trans influence of the H ligand is clear and the results for the two mer and fac isomers are consistent in this respect.

Figure 5 .

 5 Figure 5. ORTEP view of fac-[IrH 3 (PPh 3 ) 3 ]. Relevant structural parameters: Ir-P, 2.3296(5); Ir-H, 1.39(5) Å; P-Ir-P i , 103.021(16); H-Ir-P, 89.7(19); H-Ir-P i , 84(2); H-Ir-P ii , 164(2)°. Symmetry

full 1 H

 1 and 31 P{ 1 H} NMR characterization of compound mer-[IrH 3 (PPh 3 ) 3 ] and the X-ray structure of compound fac-[IrH 3 (PPh 3 ) 3 ], we have corrected a literature misassignment of the 31 P NMR resonance for the latter compound, and we have evidenced a few additional by-products formed by thermal activation of the above mentioned precatalyst system. The slowdown of the thermal activation in the presence of additional PPh 3 shows that PPh 3 dissociation from [IrH(COD)(PPh 3 ) 2 ] is a prerequisite for the transformations leading to hydrogenation and elimination of the COD ligand.

. Reaction of [Ir(µ-Cl)(COD)] 2 and PPh 3 in isopropanol in the presence of KOtBu at room temperature: generation of [Ir(COD)H(PPh 3 ) 2 ]

	peak assignments in the 1 H and 13 C NMR spectra were based on COSY, HSQC and HMBC 2D
	experiments. Gas chromatographic analyses were carried out with a Supelco SPB-20 column
	(40°C for 10 minutes then heating 3°C/min up to new isotherm at 120°C): cycloctene (11.6 min);
	cyclooctane (13.1 min); cyclooctadiene (13.6 min).
	2.2PPh 3 (78 mg, 0.297 mmol) was added to an isopropanol solution (10 mL) of [Ir(µ-Cl)(COD)] 2
	(50 mg, 0.074 mmol) and the solution was stirred at room temperature for 3 hours. Potassium tert-
	butoxide (83 mg, 0.74 mmol) was then added to the mixture and stirred at room temperature for 5
	minutes. The white precipitate was removed by Schlenk filtration and the filtrate was concentrated
	under reduced pressure. Yield: 96 mg (78%). 1 H NMR (400 MHz, CDCl 3 ):  7.79-7.10 (m, 30H,
	Ph), 3.81 (br, 2H, COD), 3.47 (br, 2H, COD), 1.83 (br, 4H, COD), 1.54 (br, 4H, COD), -13.77 (t,
	1H, Ir-H, J PH = 22 Hz). 31 P { 1 H} NMR (400 MHz, CDCl 3 ):  7.5. 13 C NMR (400 MHz, CDCl 3 ): 
	139.7 (Ph-ipso, m with virtual coupling), 133.4 (Ph-ortho, virtual t, J PC = 5.8 Hz), 128.1 (Ph-para,
	s), 127.3 (Ph-meta, virtual t, J
	Isopropanol was carefully dried by distillation on calcium hydride and kept over 4Å molecular
	sieves under argon before use. All commercially available chemicals ([IrCl(COD)] 2 , 99%, Strem
	Chemicals; PPh 3 , 99%, Aldrich; NaOMe, 99%, Aldrich) were used as received. 1 H and 13 C{ 1 H}
	NMR spectra were recorded with Bruker Avance 400, Bruker Avance III 400 and Bruker Avance
	500 FT-NMR spectrometers. The resonances were calibrated relative to the residual solvent peaks
	and are reported with positive values downfield from TMS. For all characterized compounds, the

PC = 4.4 Hz), 79.3 (CH COD, s), 47.7 (CH COD, m with virtual coupling), 34.7 (CH 2 COD, s), 31.4 (CH 2 COD, s).

Table 1 .

 1 Crystal data and structure refinement for fac-[IrH 3 (PPh 3 ) 3 ]

	Identification code	[IrH 3 (PPh 3 ) 3 ]
	Empirical formula	C 54 H 48 Ir P 3 , CH Cl 3
	Formula weight	1101.40

  The addition of phosphines to [IrCl(COD)] 2 is a well known process, leading to different products depending on stoichiometry, solvent and ligand denticity (monodentate L, bidentate L 2 ): neutral 4-coordinate [IrCl(COD)L], neutral 5-coordinate [IrCl(COD)L 2 ], ionic [Ir(COD)L 2 ] + Cl -or [Ir(COD)L 2 ] + [IrCl 2 (COD)] -. Addition of 1 equiv of PPh 3 per Ir atom yields a solution of

  the catalyzed hydrogenation of acetophenone[98]. However, in the present case the activation and COD removal occurs without the use of H 2 . The generation of the neutral [IrH 3 (PPh 3 ) 3 ] isomers, instead of [IrH 4 (PPh 3 ) 4 ] -, may be related to the greater basicity of two PPh 3 ligands relative to the (PS) ligand. Crystallization of the product mixture of the [IrCl(COD)] 2 /PPh 3 /KOtBu reaction in refluxing isopropanol from CDCl 3 afforded single crystals of the major fac-[IrH 3 (PPh 3 ) 3 ] product. A

	[IrCl(COD)(PS)], where (PS) is the bidentate ferrocenyl phosphine-thioether CpFe[1,2-
	C 5 H 3 (PPh 2 )(CH 2 StBu)] ligand, which was suggested on the basis of DFT calculations to generate
	the tetrahydrido complex [IrH 4 (PS)] -, isoelectronic with the above-mentioned [IrH 4 (PPh 3 ) 4 ] -, in
	basic isopropanol during

Acknowledgments

We are grateful to the European Commission, Contract No. 654198 (Marie Curie action IIF) for a post-doctoral Fellowship to SMWR, and CNRS (Centre National de la Recherche Scientifique) and IUF (Institut Universitaire de France) for additional funding. We gratefully acknowledge Dr.

Yannick Coppel for help and advice on the NMR experiments.

Supplementary data

Additional NMR spectra as cited in the text (8 pages). The supplementary crystallographic data for fac-[IrH 3 (PPh 3 ) 3 ] have been deposited to the Cambridge Crystallographic Data Centre as CCDC 1501228 and can obtained free of charge from via www.ccdc.cam.ac.uk/data_request/cif.