Why does grid search cross validation give same value of mean square error for different values of C and gamma in support vector regression ?

I am using libsvm in matlab for  time series prediction using support vector regression . When I use grid search cross validation to select parameters C and gamma, the value of cross validation mean square error is coming same for  different values of these parameters.So,by default the best C and gamma are the first values in the given range of parameters which is clearly not the case.

How can I sort this issue and find best value of parameters ?

My code is as follows,

[C,gamma] = meshgrid( -10:1:10, -10:1:10);

for j=1:numel(C) mse_cv(j) = svmtrain(svm_label,svm_data, ... sprintf('-s %d -t %d -c %f -g %f -p %f -v %d -h %d ',s,t, 2^C(j), 2^gamma(j),eps, folds,h )); end

here, 

svm_label =

49.6665 49.6665 49.6668 49.6670 49.6671

and 

svm_data =

 49.6664  49.6665  49.6665  49.6668  49.6670

eps=0.005 ,t=2,s=3,v=5 and h=0.

[Aagya Niraula]

hey Sharda! Is your problem solved? As I am getting stuck with the same problem so can you tell me how did you solve it if possible.