diff --git a/doc/dynare.texi b/doc/dynare.texi
index 3bbaf9c6578daf902a21787e364916e5ddb0381d..7ecf288d836b51ed2dacd145f5d415333e6d73bc 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -7082,7 +7082,7 @@ cannot be less than the number of constrained periods.
 Number of simulations. Default: @code{5000}.
 
 @item conf_sig = @var{DOUBLE}
-Level of significance for confidence interval. Default: @code{0.80}
+Level of significance for confidence interval. Default: @code{0.90}
 
 @end table
 
diff --git a/matlab/bvar_forecast.m b/matlab/bvar_forecast.m
index 1b88e0ae61552c32fad4d0c79ee976a3be268f89..9b22ff679ee60333320245cbb4a327ea6cc082ac 100644
--- a/matlab/bvar_forecast.m
+++ b/matlab/bvar_forecast.m
@@ -103,7 +103,7 @@ end
 % Plot graphs
 sims_no_shock_mean = mean(sims_no_shock, 3);
 
-sort_idx = round((0.5 + [-options_.conf_sig, options_.conf_sig, 0]/2) * options_.bvar_replic);
+sort_idx = round((0.5 + [-options_.bvar.conf_sig, options_.bvar.conf_sig, 0]/2) * options_.bvar_replic);
 
 sims_no_shock_sort = sort(sims_no_shock, 3);
 sims_no_shock_down_conf = sims_no_shock_sort(:, :, sort_idx(1));
diff --git a/matlab/bvar_irf.m b/matlab/bvar_irf.m
index a2e124f3a3717ec6ded3c0f0ccc56eeacad8228b..17228b5c433b3298e4dea1b9b8fdee825bfb86b9 100644
--- a/matlab/bvar_irf.m
+++ b/matlab/bvar_irf.m
@@ -102,7 +102,7 @@ posterior_mean_irfs = mean(sampled_irfs,4);
 posterior_variance_irfs = var(sampled_irfs, 1, 4);
 
 sorted_irfs = sort(sampled_irfs,4);
-sort_idx = round((0.5 + [-options_.conf_sig, options_.conf_sig, .0]/2) * options_.bvar_replic);
+sort_idx = round((0.5 + [-options_.bvar.conf_sig, options_.bvar.conf_sig, .0]/2) * options_.bvar_replic);
 
 posterior_down_conf_irfs = sorted_irfs(:,:,:,sort_idx(1));
 posterior_up_conf_irfs = sorted_irfs(:,:,:,sort_idx(2));
diff --git a/matlab/forcst.m b/matlab/forcst.m
index 2684df8ee86b805f9d0a1643d8eb855748ed4b9b..0cc8dda2d245b6bb63e65e2e43f7b9b7bc13aeb4 100644
--- a/matlab/forcst.m
+++ b/matlab/forcst.m
@@ -78,7 +78,7 @@ for i=1:horizon
     sigma_y = sigma_y+sigma_u;
 end
 
-fact = norminv((1-options_.conf_sig)/2,0,1);
+fact = norminv((1-options_.forecasts.conf_sig)/2,0,1);
 
 int_width = zeros(horizon,M_.endo_nbr);
 for i=1:nvar
diff --git a/matlab/forecast_graphs.m b/matlab/forecast_graphs.m
index 5dab918116823621bbbf5b00dbe8546cec0b2855..ea1932e856bcc5a8a7bcbbd8e9f4d96471705058 100644
--- a/matlab/forecast_graphs.m
+++ b/matlab/forecast_graphs.m
@@ -74,7 +74,7 @@ for j= 1:nvar
             fprintf(fidTeX,'\\centering \n');
             fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s/graphs/forcst%d}\n',dname,n_fig);
             fprintf(fidTeX,'\\label{Fig:forcst:%d}\n',n_fig);
-            fprintf(fidTeX,'\\caption{Mean forecasts and %2.0f%% confidence intervals}\n',options_.conf_sig*100);
+            fprintf(fidTeX,'\\caption{Mean forecasts and %2.0f%% confidence intervals}\n',options_.forecasts.conf_sig*100);
             fprintf(fidTeX,'\\end{figure}\n');
             fprintf(fidTeX,' \n');
         end       
@@ -110,7 +110,7 @@ if m > 1
         fprintf(fidTeX,'\\centering \n');
         fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s/graphs/forcst%d}\n',dname,n_fig);
         fprintf(fidTeX,'\\label{Fig:forcst:%d}\n',n_fig);
-        fprintf(fidTeX,'\\caption{Mean forecasts and %2.0f\\%% confidence intervals}\n',options_.conf_sig*100);
+        fprintf(fidTeX,'\\caption{Mean forecasts and %2.0f\\%% confidence intervals}\n',options_.forecasts.conf_sig*100);
         fprintf(fidTeX,'\\end{figure}\n');
         fprintf(fidTeX,' \n');
     end
diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index a5844b9968be22fa902f6c07ac450c9dffe5b9e5..e5a9a8e44c363289489b22d00654b983640889a1 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -109,6 +109,7 @@ options_.bvar_prior_mu = 2;
 options_.bvar_prior_omega = 1;
 options_.bvar_prior_flat = 0;
 options_.bvar_prior_train = 0;
+options_.bvar.conf_sig = 0.6;
 
 % Initialize the field that will contain the optimization algorigthm's options declared in the
 % estimation command (if anny).
@@ -305,6 +306,8 @@ options_.prior_draws = 10000;
 options_.sampling_draws = 500;
 
 options_.forecast = 0;
+options_.forecasts.conf_sig = 0.9;
+options_.conditional_forecast.conf_sig = 0.9;
 
 % Model
 options_.linear = 0;
@@ -513,7 +516,7 @@ options_.estimation.moments_posterior_density.gridpoints = 2^9;
 options_.estimation.moments_posterior_density.bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
 options_.estimation.moments_posterior_density.kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton.
 % Misc
-options_.conf_sig = 0.6;
+% options_.conf_sig = 0.6;
 oo_.exo_simul = [];
 oo_.endo_simul = [];
 ys0_ = [];
diff --git a/matlab/imcforecast.m b/matlab/imcforecast.m
index b9ffeac4e33f80d60cac14a512620d9ed9df5d5c..e310a642bde4fd1cf95073bd9de59bb10bda7960 100644
--- a/matlab/imcforecast.m
+++ b/matlab/imcforecast.m
@@ -63,8 +63,8 @@ if ~isfield(options_cond_fcst,'periods') || isempty(options_cond_fcst.periods)
     options_cond_fcst.periods = 40;
 end
 
-if ~isfield(options_cond_fcst,'conf_sig') || isempty(options_cond_fcst.conf_sig)
-    options_cond_fcst.conf_sig = .8;
+if ~isfield(options_cond_fcst,'conditional_forecast') || ~isfield(options_cond_fcst.conditional_forecast,'conf_sig')  || isempty(options_cond_fcst.conditional_forecast.conf_sig)
+    options_cond_fcst.conditional_forecast.conf_sig = .8;
 end
 
 if isequal(options_cond_fcst.parameter_set,'calibration')
@@ -228,7 +228,7 @@ end
 mFORCS1 = mean(FORCS1,3);
 mFORCS1_shocks = mean(FORCS1_shocks,3);
 
-tt = (1-options_cond_fcst.conf_sig)/2;
+tt = (1-options_cond_fcst.conditional_forecast.conf_sig)/2;
 t1 = round(options_cond_fcst.replic*tt);
 t2 = round(options_cond_fcst.replic*(1-tt));
 
diff --git a/matlab/simultxdet.m b/matlab/simultxdet.m
index ca34c596c703bc6b230212756764f1ecac9f768c..2de1bb4b7ff19281b59e4978098b720799a89c07 100644
--- a/matlab/simultxdet.m
+++ b/matlab/simultxdet.m
@@ -134,7 +134,7 @@ for i=1:iter
     sigma_y = sigma_y+sigma_u;
 end
 
-fact = norminv((1-options_.conf_sig)/2,0,1);
+fact = norminv((1-options_.forecasts.conf_sig)/2,0,1);
 
 int_width = zeros(iter,endo_nbr);
 for i=1:nvar