diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 4ee53518b..6679f0cee 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -263,35 +263,6 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
     end
 end
 
-switch options_.MCMC_jumping_covariance
-    case 'hessian' %Baseline
-        %do nothing and use hessian from mode_compute
-    case 'prior_variance' %Use prior variance
-        if any(isinf(bayestopt_.p2))
-            error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
-        else
-            hh = diag(1./(bayestopt_.p2.^2));
-        end
-    case 'identity_matrix' %Use identity
-        hh = eye(nx);
-    otherwise %user specified matrix in file
-        try
-            load(options_.MCMC_jumping_covariance,'jumping_covariance')
-            hh=jumping_covariance;
-        catch
-            error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
-        end
-        [nrow, ncol]=size(hh);
-        if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
-            error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
-        end
-        try %check for positive definiteness
-            chol(hh);
-        catch
-            error(['Specified jumping_covariance is not positive definite'])
-        end
-end
-
 if ~options_.mh_posterior_mode_estimation && options_.cova_compute
     try
         chol(hh);
@@ -393,6 +364,39 @@ if np > 0
     save([M_.fname '_params.mat'],'pindx');
 end
 
+switch options_.MCMC_jumping_covariance
+    case 'hessian' %Baseline
+        %do nothing and use hessian from mode_compute
+    case 'prior_variance' %Use prior variance
+        if any(isinf(bayestopt_.p2))
+            error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
+        else
+            hh = diag(1./(bayestopt_.p2.^2));
+        end
+        hsd = sqrt(diag(hh));
+        invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
+    case 'identity_matrix' %Use identity
+        invhess = eye(nx);
+    otherwise %user specified matrix in file
+        try
+            load(options_.MCMC_jumping_covariance,'jumping_covariance')
+            hh=jumping_covariance;
+        catch
+            error(['No matrix named ''jumping_covariance'' could be found in ',options_.MCMC_jumping_covariance,'.mat'])
+        end
+        [nrow, ncol]=size(hh);
+        if ~isequal(nrow,ncol) && ~isequal(nrow,nx) %check if square and right size
+            error(['jumping_covariance matrix must be square and have ',num2str(nx),' rows and columns'])
+        end
+        try %check for positive definiteness
+            chol(hh);
+            hsd = sqrt(diag(hh));
+            invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
+        catch
+            error(['Specified jumping_covariance is not positive definite'])
+        end
+end
+
 if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
         (any(bayestopt_.pshape >0 ) && options_.load_mh_file)  %% not ML estimation
     bounds = prior_bounds(bayestopt_, options_.prior_trunc);