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Rendement et volatilité en présence de noise traders

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par Ilef Ben Hadj Ayed
Faculté des Sciences Economiques et de Gestion de Mahdia - mastère de recherche en finance  2012
  

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Annexe 14 : estimation du modèle avec la variable darms

Dependent Variable: R

 
 

Method: ML - ARCH (Marquardt) - Generalized error distribution (GED)

Date: 12/13/12 Time: 12:46

 
 

Sample (adjusted): 1 515

 
 

Included observations: 515 after adjustments

 

Convergence achieved after 37 iterations

 

Presample variance: backcast (parameter = 0.7)

GARCH = C(4) + C(5)*RESID(-1)^2 + C(6)*GARCH(-1) + C(7)*D_ARMS1 +

        C(8)*D_ARMS2

 
 
 
 
 
 
 
 
 
 
 
 

Variable

Coefficient

Std. Error

z-Statistic

Prob.  

 
 
 
 
 
 
 
 
 
 

@SQRT(GARCH)

3.927422

0.789013

4.977641

0.0000

C

-0.112866

0.020834

-5.417449

0.0000

DARMS

-0.080989

0.039858

-2.031939

0.0422

 
 
 
 
 
 
 
 
 
 
 

Variance Equation

 
 
 
 
 
 
 
 
 
 
 
 

C

6.13E-07

1.35E-06

0.454222

0.6497

RESID(-1)^2

0.009185

0.002448

3.752843

0.0002

GARCH(-1)

0.990218

0.001805

548.5897

0.0000

D_ARMS1

1.27E-06

2.94E-07

4.317550

0.0000

D_ARMS2

-2.30E-06

7.59E-07

-3.030629

0.0024

 
 
 
 
 
 
 
 
 
 

GED PARAMETER

1.114063

0.086462

12.88507

0.0000

 
 
 
 
 
 
 
 
 
 

R-squared

0.330500

    Mean dependent var

-0.018178

Adjusted R-squared

0.327885

    S.D. dependent var

0.026614

S.E. of regression

0.021819

    Akaike info criterion

-4.865986

Sum squared resid

0.243743

    Schwarz criterion

-4.791817

Log likelihood

1261.992

    Hannan-Quinn criter.

-4.836919

Durbin-Watson stat

1.882561

 
 
 
 
 
 
 
 
 
 
 
 
 


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