**3. Results**

#### **3.1 Corporate governance stock returns volatility**

#### *3.1.1 Methodology and data*

In this paper, we examine the three-way linkages between corporate governance and stock return volatility. Our study focuses on French companies composing the SBF 120 index for the data collection; we were required to use a data source, i.e., the database "http://investir.lesechos.fr." The sample period runs from 2006 to 2012.

The following regression equation is formulated to test empirically the

$$\begin{split} \text{VOL}\_{i} &= \alpha + \beta\_{1} \text{CEO}\_{i} + \beta\_{2} \text{FD}\_{i} + \beta\_{3} \text{INDD}\_{i} + \beta\_{4} \text{CPA}\_{i} \\ &+ \beta\_{5} \text{LEV}\_{i} + \beta\_{6} \text{SIZE}\_{i} + \beta\_{7} \text{PER}\_{i} + \beta\_{8} \text{TURN}\_{i} + \varepsilon\_{i} \end{split} \tag{1}$$

The *Vol*, as the dependent variable in the model, is measured by the standard deviation of annual stock returns. Concerning the independent variable is as follows:

The *CEO* is the chairman also serving as CEO. The *INDD* is independent directors and measured by the ratio of independent directors. The *FD* is outside directors. The *CPA* is audit size.

In addition, the variable of corporate governance is as follows: *PER* is ROA. The *TURN* is firm size (total liabilities). The *SIZE* is firm size, and the *LEV* is firm's debt ratio. Our work is a panel data study, Eq. (1) can be written in the form of panel data as follows:

$$\text{VOL}\_{it} = a + a\_i + \sum\_j \beta\_j E\_{jit} + \sum\_n \delta\_n Y\_n + \varepsilon\_{it} \tag{2}$$

Since our study is a panel data study, Eq. (3) can be written in a panel data form as follows:

$$\text{VOL}\_{it} = a + a\_i + \sum\_{j-1} \beta\_j E\_{jit} + \sum\_n \delta\_n Y\_n + \varepsilon\_{it} \tag{3}$$

$$\text{VOL}\_{\text{it}} = a + \text{VOL}\_{\text{it}} + \beta\_{\text{vd}} \text{INDD}\_{\text{it}} + \beta\_{\text{vf}} \text{FD} + \beta\_{\text{vv}} V\_{i, t-1} + \sum\_{n} \delta\_{n} Y\_{n} + \varepsilon\_{\text{it}} \tag{4}$$

$$\text{INIDD}\_{\text{it}} = a + a\_{\text{i}} \text{INT}\_{\text{it}} + \beta\_{\text{vd}} \text{VOL}\_{\text{it}} + \beta\_{\text{vf}} \text{FD} + \beta\_{\text{vv}} \text{INDD}\_{\text{i}, \text{t}-1} + \sum\_{n} \delta\_{n} Y\_{n} + \varepsilon\_{\text{it}} \tag{5}$$

$$FD\_{\rm it} = a + a\_{\rm i}FD\_{\rm it} + \beta\_{\rm vd}VOL\_{\rm it} + \beta\_{\rm vf}INDD + \beta\_{\rm vv}FD\_{\rm i, t-1} + \sum\_{n} \delta\_{n}Y\_{n} + e\_{\rm it} \tag{6}$$

We then use the production function in Eq. (4) to derive the empirical models to simultaneously examine the interactions between stock return volatility; INDD is independent directors, and FD is outside directors. These simultaneous-equation models are also constructed on the basis of the theoretical and empirical insights from the existing literature. While estimating the causal links between CEO is

#### *The Primary Origin of the Financial Crisis DOI: http://dx.doi.org/10.5772/intechopen.86173*

Regarding the relationship between the independence of the board and the risk of liquidity, the first who examined the debate were Anderson et al. [25], who used the cost of the debt of the company as a proxy. They found that the more independent board is, the more the debt cost decreases. Pathan [26] found that the inde-

In this paper, we examine the three-way linkages between corporate governance and stock return volatility. Our study focuses on French companies composing the SBF 120 index for the data collection; we were required to use a data source, i.e., the database "http://investir.lesechos.fr." The sample period runs from 2006 to 2012. The following regression equation is formulated to test empirically the

þ *β*<sup>5</sup> *LEVi* þ *β*<sup>6</sup> *SIZEi* þ *β*7*PERi* þ *β*8*TURNi* þ *ε<sup>i</sup>*

(1)

*VOLi* ¼ *α* þ *β*1*CEOi* þ *β*<sup>2</sup> *FDi* þ *β*<sup>3</sup> *INDDi* þ *β*<sup>4</sup> *CPAi*

The *CEO* is the chairman also serving as CEO. The *INDD* is independent directors and measured by the ratio of independent directors. The *FD* is outside

*VOLit* ¼ *α* þ *α<sup>i</sup>* þ ∑

*VOLit* ¼ *α* þ *α<sup>i</sup>* þ ∑

*VOLit* ¼ *α* þ *VOLit* þ *βvdINDDit* þ *βvf FD* þ *βvvVi,t*�<sup>1</sup> þ ∑

*INDDit* ¼ *α* þ *αiINTit* þ *βvdVOLit* þ *βvf FD* þ *βvvINDDi,t*�<sup>1</sup> þ ∑

*FDit* ¼ *α* þ *αiFDit* þ *βvdVOLit* þ *βvfINDD* þ *βvvFDi,t*�<sup>1</sup> þ ∑

The *Vol*, as the dependent variable in the model, is measured by the standard deviation of annual stock returns. Concerning the independent variable is as fol-

In addition, the variable of corporate governance is as follows: *PER* is ROA. The *TURN* is firm size (total liabilities). The *SIZE* is firm size, and the *LEV* is firm's debt ratio. Our work is a panel data study, Eq. (1) can be written in the form of panel

> *Ejit* þ ∑ *n*

*βjEjit* þ ∑ *n*

*δnYn* þ *εit* (2)

*δnYn* þ *εit* (3)

*δnYn* þ *εit* (4)

*δnYn* þ *εit* (5)

*δnYn* þ *εit* (6)

*n*

*n*

*n*

*j βj*

*j*�1

Since our study is a panel data study, Eq. (3) can be written in a panel data form

We then use the production function in Eq. (4) to derive the empirical models to simultaneously examine the interactions between stock return volatility; INDD is independent directors, and FD is outside directors. These simultaneous-equation models are also constructed on the basis of the theoretical and empirical insights from the existing literature. While estimating the causal links between CEO is

pendent board is negatively associated with the market risk.

**3.1 Corporate governance stock returns volatility**

**3. Results**

lows:

data as follows:

as follows:

**16**

*3.1.1 Methodology and data*

*Financial Crises - A Selection of Readings*

directors. The *CPA* is audit size.

chairman also serving as CEO, CPA is audit size, PER is ROA, LEV is debt ratio, and SIZE is firm size are included as instrumental variables (e.g., [17, 27]).

In Eq. (5), INDD: present the independent directors CEO is chairman also serving as CEO, FD is outside.

Directors, and VOL is stock return volatility, are the main factors of resistance of the company during the variations of the stock markets.

In this research, we use a dynamic panel data model of lagged levels of the dependent variables and for this reason; we utilize the Blundell and Bond [28] twostep system GMM methodology. This methodology is explained on the basis that traditional OLS estimator is biased in the presence of the lagged-dependent variable as regressor, and it also reports for the prospective endogeneity of certain dependent variables.

### *3.1.2 Empirical result and discussion*

**Table 1** presents the descriptive statistics for the regression variables. In this table, we can see "Mean", "standard deviation", "Min", and "Max". The stock return volatility showed the maximum standard deviation 1.67%, and there is also a much smaller standard deviation of 0.003%, with a mean of 39.86% and a maximum of 1.16%. For an independent variable, the chairman also serving as CEO showed the maximum standard deviation of 48.16%, and there is also a much smaller standard deviation of 0%, with a mean of 63.56% and a maximum of 1%.

The independent directors showed the maximum standard deviation of 13.32%, and there is also a much smaller standard deviation of 0%, with a mean of 1.04% and a maximum of 33.3%. The outside directors illustrate the standard deviation of 18.58%, and there is also a much smaller standard deviation of 5.55%, with a mean of 29.16% and a maximum of 1.60%.

Concerning the control variable, the audit size showed the maximum standard deviation of 13.52%, with a mean of 32.96% and a maximum of 1.2%. The debt ratio presents the maximum standard deviation of 81.03%, with a mean of 53.09% and a maximum of 65.553%. For the firm's size showed a standard deviation of 79.27%, with a mean of 6.48% and a maximum of 8.90%. We can see "Mean", "standard deviation", "Min", and "Max". Finally, the ROA presents the maximum standard deviation of 57.01%, and there is also a much smaller standard deviation of 6.95%, with a mean of 7.27% and a maximum of 9.28%.

Next, **Table 2** provides the correlation matrix for the dependent variable, stock return volatility, and all the independent variables. It also presents the correlation coefficients among the variables in our analysis. At first glance, it can be seen that


#### **Table 1.**

*Summary statistics of corporate governance.*


outside directors have a positive and significant (at 1%) impact on the stock market volatility. This result suggests that these variables contribute to the increase in the

The empirical results about Arellano-Bover/Blundell-Bond (ABBB) method showed that the outside directors and ROA have a positive and significant impact on the stock return volatility. Also, in this method we can see that the audit size have a negative and significant impact on the stock return volatility. This result suggests that the audit size contribute to decrease and reduce stock return volatility. Moreover, the different reports about robust regressions (OLS-fe, OLS-ar, ABBB, and AB) pointed out that the debt ratio (LEV) has a positive and significant impact on the stock market volatility. This suggests that the stock return volatility is elastic on the leverage ratio, and a 10% increase in the leverage ratio increases the stock return volatility within a range of 0.026%. This result indicates that the debt ratio

**Table 4** presents the random effect regression effects. The first model (1) included only the control variable; the result indicates that the ROA has a positive and significant impact on the stock return volatility, while the firm size has a negative and significant impact on the stock return volatility. For model (2) that contains the dependent variable, we can see that the outside directors have a negative and significant impact on the stock return volatility; the outside directors can help to reduce the stock return volatility. According to Vo [29], the foreign

In model (3), when combining control variable with outside directors and independent directors, we found that the ROA has a positive and significant impact on the stock return volatility, while the firm size has a negative and significant impact on the stock return volatility. Finally, model (4) included all variables; the result indicates that the outside directors and ROA have a positive and significant impact

In **Table 5**, we can see that the CEO, audit size, debt ratio, and total liabilities have statically significant and positive impacts on the stock return volatility; this result indicates that these variables contribute to increase the stock return volatility. Moreover, the fact that foreign ownership, firm's size, and ROA have a negative

Stock return volatility 0.0152933 0.0213691 Chairman also serving as CEO 0.0171294 0.0164611 Outside directors (FD) 0.1786031\* 0.0000928 0.1834594\* Independent directors 0.0340352 0.0337171 04.42e-10 Audit size 0.0063078 0.0063206 0.0426624 Relative ROA 0.0002156\*\*\* 0002145\*\*\* 0.0002644\*\*\* Debt ratio 0.0012812 0.0012117 0.0043407 Firm size 0.052031\*\* 0.051979\*\* 0.0046849 Constant 0.7526633\* 0.7519738\* 0.1674346 Fixed/random effect 4.80 1.88 4.75 42.55\*

**Model 1 Model 2 Model 3 Model 4**

**Variables Stock return volatility**

Breusch-Pagan LM test (p-value) 789.20\* 793.62\* 789.37\*

*Random effect regressions (the impact of corporate governance on the stock returns volatility).*

*, \*\*, and \*\*\* significant at 1, 5, 10, percent levels, respectively.*

stock return volatility.

increases the stock return volatility.

*The Primary Origin of the Financial Crisis DOI: http://dx.doi.org/10.5772/intechopen.86173*

on the stock return volatility.

*The \**

**19**

**Table 4.**

director can stabilize the stock return volatility.

#### **Table 2.**

*The correlation matrix of corporate governance.*


#### **Table 3.**

*Robustness tests—no feedback and governance variables not endogenous.*

the stock price fluctuation is negatively correlated with the independent directors, relative ROA, and firm size which suggests that these variables help stabilize the stock return volatility. The stock price volatility is a positive correlation between the debt ratio, CEO, outside director, and audit size. In fact, all these have contributed to the increase of the stock price volatility.

In **Table 3**, we based on four methods (Ols-Fe, Ols-Ar, Ab, Abbb). Concerning the first method (OLS-fe), we can see only that the chairman also serving as CEO and relative ROA have a positive and significant (at 1%) impact on the stock market volatility, while the second method (OLS-ar), this result indicates that the CEO and ROA have a positive and significant (at 5%) impact on the stock market volatility. Concerning the Arellano-Bond regression (AB) method, we note that the CEO and

### *The Primary Origin of the Financial Crisis DOI: http://dx.doi.org/10.5772/intechopen.86173*

outside directors have a positive and significant (at 1%) impact on the stock market volatility. This result suggests that these variables contribute to the increase in the stock return volatility.

The empirical results about Arellano-Bover/Blundell-Bond (ABBB) method showed that the outside directors and ROA have a positive and significant impact on the stock return volatility. Also, in this method we can see that the audit size have a negative and significant impact on the stock return volatility. This result suggests that the audit size contribute to decrease and reduce stock return volatility. Moreover, the different reports about robust regressions (OLS-fe, OLS-ar, ABBB, and AB) pointed out that the debt ratio (LEV) has a positive and significant impact on the stock market volatility. This suggests that the stock return volatility is elastic on the leverage ratio, and a 10% increase in the leverage ratio increases the stock return volatility within a range of 0.026%. This result indicates that the debt ratio increases the stock return volatility.

**Table 4** presents the random effect regression effects. The first model (1) included only the control variable; the result indicates that the ROA has a positive and significant impact on the stock return volatility, while the firm size has a negative and significant impact on the stock return volatility. For model (2) that contains the dependent variable, we can see that the outside directors have a negative and significant impact on the stock return volatility; the outside directors can help to reduce the stock return volatility. According to Vo [29], the foreign director can stabilize the stock return volatility.

In model (3), when combining control variable with outside directors and independent directors, we found that the ROA has a positive and significant impact on the stock return volatility, while the firm size has a negative and significant impact on the stock return volatility. Finally, model (4) included all variables; the result indicates that the outside directors and ROA have a positive and significant impact on the stock return volatility.

In **Table 5**, we can see that the CEO, audit size, debt ratio, and total liabilities have statically significant and positive impacts on the stock return volatility; this result indicates that these variables contribute to increase the stock return volatility. Moreover, the fact that foreign ownership, firm's size, and ROA have a negative


#### **Table 4.**

*Random effect regressions (the impact of corporate governance on the stock returns volatility).*

the stock price fluctuation is negatively correlated with the independent directors, relative ROA, and firm size which suggests that these variables help stabilize the stock return volatility. The stock price volatility is a positive correlation between the debt ratio, CEO, outside director, and audit size. In fact, all these have contributed

Volatility 0.4377655\* 0.2849317\*

**Variables Volatility CEO FD IND Audit**

1.000

0.0846 0.1055

0.0287 0.4753

0.0207 0.6057

0.0119 0.7676

0.0866\* 0.0307

0.0584 0.1452

1.000

0.1974\* 0.0001

0.3308\* 0.0000

0.0912 0.0811

0.0846 0.1055

0.0451 0.3893

**Variables Ols-Fe Ols-Ar Ab Abbb** Volatility stock return 0.0213691 0.0261624 0.000477 0.012617 Chairman also serving as CEO 0.0164611 0.1483642 0.2842368\*\* 0.16203 Outside directors (FD) 0.1834594\* 0.1840265\*\* 0.2219233\*\* 0.2840837\*\* Independent directors 0.0446437 0.0539175 0.080685 0.1292659 Audit size 0.042662 0.075450 0.0526602 0.209908\*\*\* Relative ROA 0.0002644\* 0.0002502\*\* 0.0001734 0.0003577\*\* Debt ratio 0.0043407 0.0064761 0.008987 0.0338603 Firm size 0.0046849 0.018764 0.0280472 0.0128239 Constant 0.1674346 0.013684 0.351875\*\*\* 0.1473147

1.000

0.0456 0.2560

0.0034 0.9323

0.0341 0.3949

0.0084 0.8341

0.1286\* 0.0013

0.3950

0.7419

0.0174

0.7674

0.0027

0.9632

Stock return volatility 1.000

*Financial Crises - A Selection of Readings*

Outside directors (FD) 0.0445

Independent directors 0.0132

Audit size 0.0953\*

Relative ROA 0.0119

Debt ratio 0.1199\*

Firm size 0.0019

*The correlation matrix of corporate governance.*

*The \* indicate significance at the percent levels.*

**Volatility stock return**

Chairman also serving as

CEO

**Table 2.**

*The \**

**Table 3.**

**18**

**size**

1.000

0.0073 0.8549

0.0051 0.8999

0.3175\* 0.0000

1.000

0.0012 0.9760

0.0201 0.6174 1.000

0.0534 0.1827

1.000

**ROA Debt ratio** **Firm size**

In **Table 3**, we based on four methods (Ols-Fe, Ols-Ar, Ab, Abbb). Concerning the first method (OLS-fe), we can see only that the chairman also serving as CEO and relative ROA have a positive and significant (at 1%) impact on the stock market volatility, while the second method (OLS-ar), this result indicates that the CEO and ROA have a positive and significant (at 5%) impact on the stock market volatility. Concerning the Arellano-Bond regression (AB) method, we note that the CEO and

to the increase of the stock price volatility.

*, \*\*, and \*\*\* significant at 1, 5, 10, percent levels, respectively.*

*Robustness tests—no feedback and governance variables not endogenous.*


positive and significant impact on the stock return volatility; this result indicates that these variables contribute to increase and stabilize the stock return volatility. In this area, this result compared to the study of Steven et al. [19], they indicate that the outside directors contribute to stabilize the stock return volatility. Also, we found the independent directors and ROA have a negative effect on the stock return volatility; this result indicates that these variables contribute to decrease and stabi-

Moreover, the results indicate that the stock return volatility has a negative and significant (1%) impact on the independent directors. This stipulates that the independent directors contributed to the minimization of the volatility of the stock returns, that is to say, they are considered a real factor of corporate governance. In this context, the independent directors are considered a sign of good governance.

**Table 6** reports the results of Arellano and Bover [30] and Blundell and Bond [28] "system GMM" estimation of [Eq. (2)], using different measures of the firm. In the GMM system, first-differenced variables are used as instruments for the equations in levels, and the estimates are robust to unobserved heterogeneity, simultaneity, and dynamic endogeneity (if any). The diagnostic tests in **Table 5** show that the model [Eq. (2)] presenting the effect of the stock return fluctuation on the independent director is well-fitted with statistically insignificant test statistics of the first-order autocorrelation in first differences (AR1) and Hansen J-statistics of overidentifying restrictions. Accordingly, in **Table 5**, we could see statistically insignificant AR (1) for all the firm's measures. Likewise, the Hansen's J-statistics of overidentifying restriction test, the null instrument validity, and the statistically insignificant Hansen J-statistics for all the firm's measures indicate that the instruments are valid in the respective estimation. Finally, the number of instruments (i.e., 24) used in the model is less than the panel (i.e., 212) which makes the Hansen J-statistics more reliable. By contrast, Eq. (1) presents the impact of the independent directors on the stock price fluctuation and shows that it is well fitted with the statistically significant test statistics of the first-order autocorrelation in the first differences of AR (1) and with the Hansen J-statistics of overidentifying restric-

In this paper, we examine the linkages between stock returns and risk management. Our study focuses on French companies composing the SBF 120 index for the data collection; we were required to use a data source, i.e., the database "http://

Annual returns are computed as geometric and arithmetic growth rates, respec-

The study is an extension of the approach suggested by Karolyi et al. [20], Longin and Solnik [31] to examine the future contracts (such as foreign exchange

Pt<sup>1</sup> for the annual data.

investir.lesechos.fr." The sample period runs from 2006 to 2013.

This result is consistent with the findings of Huang et al. [17].

**3.2 Risk management and the financial crisis**

tively. In particular, we used the formula PtPt<sup>1</sup>

rates, treasury bond, and index of stock prices).

*3.2.1 Data description and variable*

*3.2.1.1 Stock returns volatility*

*3.2.1.2 Exchange rate*

**21**

lize the stock return volatility.

*The Primary Origin of the Financial Crisis DOI: http://dx.doi.org/10.5772/intechopen.86173*

tions.

#### **Table 5.**

*Linear regression.*


#### **Table 6.**

*Three-stage least squares for simultaneous equations.*

effect on the stock return volatility; these results indicate that these variables contribute to decrease and stabilize the stock return volatility.

**Table 6** contains three-stage least squares for simultaneous equations. In this table, the result suggests that the outside directors (FD) and audit size have a

#### *The Primary Origin of the Financial Crisis DOI: http://dx.doi.org/10.5772/intechopen.86173*

positive and significant impact on the stock return volatility; this result indicates that these variables contribute to increase and stabilize the stock return volatility. In this area, this result compared to the study of Steven et al. [19], they indicate that the outside directors contribute to stabilize the stock return volatility. Also, we found the independent directors and ROA have a negative effect on the stock return volatility; this result indicates that these variables contribute to decrease and stabilize the stock return volatility.

Moreover, the results indicate that the stock return volatility has a negative and significant (1%) impact on the independent directors. This stipulates that the independent directors contributed to the minimization of the volatility of the stock returns, that is to say, they are considered a real factor of corporate governance. In this context, the independent directors are considered a sign of good governance. This result is consistent with the findings of Huang et al. [17].

**Table 6** reports the results of Arellano and Bover [30] and Blundell and Bond [28] "system GMM" estimation of [Eq. (2)], using different measures of the firm. In the GMM system, first-differenced variables are used as instruments for the equations in levels, and the estimates are robust to unobserved heterogeneity, simultaneity, and dynamic endogeneity (if any). The diagnostic tests in **Table 5** show that the model [Eq. (2)] presenting the effect of the stock return fluctuation on the independent director is well-fitted with statistically insignificant test statistics of the first-order autocorrelation in first differences (AR1) and Hansen J-statistics of overidentifying restrictions. Accordingly, in **Table 5**, we could see statistically insignificant AR (1) for all the firm's measures. Likewise, the Hansen's J-statistics of overidentifying restriction test, the null instrument validity, and the statistically insignificant Hansen J-statistics for all the firm's measures indicate that the instruments are valid in the respective estimation. Finally, the number of instruments (i.e., 24) used in the model is less than the panel (i.e., 212) which makes the Hansen J-statistics more reliable. By contrast, Eq. (1) presents the impact of the independent directors on the stock price fluctuation and shows that it is well fitted with the statistically significant test statistics of the first-order autocorrelation in the first differences of AR (1) and with the Hansen J-statistics of overidentifying restrictions.

#### **3.2 Risk management and the financial crisis**

#### *3.2.1 Data description and variable*

In this paper, we examine the linkages between stock returns and risk management. Our study focuses on French companies composing the SBF 120 index for the data collection; we were required to use a data source, i.e., the database "http:// investir.lesechos.fr." The sample period runs from 2006 to 2013.

#### *3.2.1.1 Stock returns volatility*

Annual returns are computed as geometric and arithmetic growth rates, respectively. In particular, we used the formula PtPt<sup>1</sup> Pt<sup>1</sup> for the annual data.

#### *3.2.1.2 Exchange rate*

The study is an extension of the approach suggested by Karolyi et al. [20], Longin and Solnik [31] to examine the future contracts (such as foreign exchange rates, treasury bond, and index of stock prices).

effect on the stock return volatility; these results indicate that these variables con-

Durbin-Wu-Hausman 11.87514\* 21.45378\* 12.52223\*

**Volatility stock return Coef. Std. Err. t P > |t|** Chairman also serving as CEO 0.0314574\*\*\* 0.0169676 1.85 0.065 Outsider directors (FD) 0.0590011\*\* 0.0526383 1.12 0.026 Independent directors 0.0666269 0.0410113 1.62 0.105 Audit size 0.164201\*\* 0.082536 1.99 0.047 Relative ROA 0.352634\* 0.1010953 3.49 0.001 Debt Ratio 0.0004149\* 0.0000889 4.67 0.000 Firm size 0.0386955\*\* 0.0123863 3.12 0.002 Constant 0.0506746\*\* 0.0179811 2.82 0.005

**Variables (1) (2) (3)**

Stock return volatility 0.997335 0.088921\*

Outsider directors (FD) 0.2347926\*\* 0.3823154\*

Debt ratio 0.0002094 0.0002125 0.0001434 Firm size 0.0631012 0.0670512\* AR (1) 3.28\* 2.34\*\* 3.04\*\* Test de Hansen 32.88\*\* 10.28 13.09

**FDi (outside directors)**

0.0024182 0.0150367 0.0204414

**IND (independent directors)**

**Volatility stock return**

Independent directors 0.9719472\* 0.2.198526\*

Audit size 0.3733843\*\* Relative ROA 0.4283953\*\*

Wu-Hausman F test 12.17108 F (1365) 0.00054 22.59621 F (1365) 0.00000 12.85766 F (1365)

*Three-stage least squares for simultaneous equations.*

*, \*\*, and \*\*\* significant at 1, 5, 10, percent levels, respectively.*

**Table 6** contains three-stage least squares for simultaneous equations. In this table, the result suggests that the outside directors (FD) and audit size have a

tribute to decrease and stabilize the stock return volatility.

*, \*\*, and \*\*\* significant at 1, 5, 10, percent levels, respectively.*

**Variables**

*Financial Crises - A Selection of Readings*

*The \**

**Table 5.** *Linear regression.*

CEO

0.00038

*The \**

**Table 6.**

**20**

Chairman also serving as

#### *3.2.1.3 Treasury bills*

This measure has been used in the previous studies, including those of Koulakiotis et al. [32]. We want to help enrich the earlier work by studying French companies.

#### *3.2.1.4 Market index*

This variable was also considered by Zhian et al. [33] and Koulakiotis et al. [32].

### *3.2.2 Model*

$$\text{FD}\_{\text{it}} = a + a\_{\text{i}} \text{FD}\_{\text{it}} + \beta\_{\text{vd}} \text{VOL}\_{\text{it}} + \beta\_{\text{vf}} \text{INDD} + \beta\_{\text{vv}} \text{FD}\_{\text{i},\text{t}-1} + \sum\_{n} \delta\_{\text{n}} Y\_{n} + e\_{\text{it}} \tag{7}$$

*n*

have a positive effect on the stock return volatility, which is clearly evidenced

In this paper, we examine the three-way linkages between stock returns, corporate governance, and risk management. Our study focuses on French companies composing the SBF 120 Index For the data collection; we were required to use a data source, i.e., the database "http://investir.lesechos.fr." The sample period runs from 2006 to 2013. Annual returns are computed as geometric and arithmetic growth

The board of directors is an important internal mechanism in business that contributes to the control of management. In this sense, several authors consider that a large board strengthens its ability to control and improve its information sources. In this context, several studies found that companies with a large board of directors are realizing better performance (Daily and Dalton) [34]. Hence, we set

H1: The impact of the board is positive on the stock market volatility

Institutional investors have an active role in corporate governance. In this sense, Pound [35] pointed out that institutional shareholders are better equipped regarding knowledge and monitoring of professional skills than individual shareholder. In this way, the agency problems can be reduced. Current research also supports the monitoring mechanism on the part of institutional investors [36, 37]. Moreover, institutional control also plays an important role in the company's performance. Cornett et al. [38] reported that institutional investors have a positive influence on the performance of a company. Sias and Starks [39] found that higher institutional shareholdings would have a positive impact on stock prices. On the other hand, Dennis et al. [40] showed that abnormal stock returns during periods of high market volatility linked to the percentage of institutional ownership could be used to predict abnormal stock returns during the liquidity crisis. Beber et al. [41] found that institutional ownership affects liquidity. To do this, we put forward the

H2: The impact of institutional investors is negative on the stock market

The study is an extension of the approach suggested by Karolyi [20], Longin, and Solnik [31] to examine the future contracts (such as foreign exchange rates,

Pt<sup>1</sup> for the annual data.

**3.3 Risk management and corporate governance**

rates, respectively. In particular, we used the formula PtPt<sup>1</sup>

in all the regressions.

*3.3.1 Data description and variable*

*The Primary Origin of the Financial Crisis DOI: http://dx.doi.org/10.5772/intechopen.86173*

*3.3.1.1 Dependent variables*

*3.3.1.2 Independent variables*

• Board of directors

the following assumption:

• Institutional investors

following hypothesis:

• Exchange rate

volatility

**23**

In **Table 7,** we can see all that the maximum standard deviation of the stock returns in the financial crisis in our sample is 73%, and there is also a much smaller standard deviation of 37%. These results show that the great impact of the financial crisis on all firm's stock price volatility.

**Table 8** shows the correlations of all the variables. In this table, it can be seen that the stock return volatility is negatively correlated with the exchange rates, which suggests that the exchange rate variables help stabilize the stock return volatility. The stock return volatility is also positively correlated with the treasury bills.

In **Table 9,** the results confirm that an exchange rate is negatively and significantly correlated with the stock return volatility. Moreover, the treasury bills


#### **Table 7.**

*Summary statistics of management risk.*


#### **Table 8.**

*The correlation matrix of management risk.*


#### **Table 9.**

*Summary statistics of risk management and the financial crisis.*

have a positive effect on the stock return volatility, which is clearly evidenced in all the regressions.
