Merge pull request #749 from Mv77/PortfolioWithBadAsset

Deal with cases in ConsPortfolioModel when the agent wants to short the risky asset.

HARK/ConsumptionSaving/ConsPortfolioModel.py

@@ -778,31 +778,30 @@ def solveConsPortfolio(solution_next,ShockDstn,IncomeDstn,RiskyDstn,
         FOC_s = EndOfPrddvds
         Share_now   = np.zeros_like(aNrmGrid) # Initialize to putting everything in safe asset
         cNrmAdj_now = np.zeros_like(aNrmGrid)
-        constrained = FOC_s[:,-1] > 0. # If agent wants to put more than 100% into risky asset, he is constrained
-        Share_now[constrained] = 1.0
+        constrained_top = FOC_s[:,-1] > 0. # If agent wants to put more than 100% into risky asset, he is constrained
+        constrained_bot = FOC_s[:,0] < 0. # Likewise if he wants to put less than 0% into risky asset
+        Share_now[constrained_top] = 1.0
         if not zero_bound:
             Share_now[0] = 1. # aNrm=0, so there's no way to "optimize" the portfolio
             cNrmAdj_now[0] = EndOfPrddvdaNvrs[0,-1] # Consumption when aNrm=0 does not depend on Share
-        cNrmAdj_now[constrained] = EndOfPrddvdaNvrs[constrained,-1] # Get consumption when share-constrained
-
+            constrained_top[0] = True # Mark as constrained so that there is no attempt at optimization
+        cNrmAdj_now[constrained_top] = EndOfPrddvdaNvrs[constrained_top,-1] # Get consumption when share-constrained
+        cNrmAdj_now[constrained_bot] = EndOfPrddvdaNvrs[constrained_bot,0]
         # For each value of aNrm, find the value of Share such that FOC-Share == 0.
         # This loop can probably be eliminated, but it's such a small step that it won't speed things up much.
         crossing = np.logical_and(FOC_s[:,1:] <= 0., FOC_s[:,:-1] >= 0.)
         for j in range(aNrm_N):
-            if Share_now[j] == 0.:
-                try:
-                    idx = np.argwhere(crossing[j,:])[0][0]
-                    bot_s = ShareGrid[idx]
-                    top_s = ShareGrid[idx+1]
-                    bot_f = FOC_s[j,idx]
-                    top_f = FOC_s[j,idx+1]
-                    bot_c = EndOfPrddvdaNvrs[j,idx]
-                    top_c = EndOfPrddvdaNvrs[j,idx+1]
-                    alpha = 1. - top_f/(top_f-bot_f)
-                    Share_now[j] = (1.-alpha)*bot_s + alpha*top_s
-                    cNrmAdj_now[j] = (1.-alpha)*bot_c + alpha*top_c
-                except:
-                    print('No optimal controls found for a=' + str(aNrmGrid[j]))
+            if not (constrained_top[j] or constrained_bot[j]):
+                idx = np.argwhere(crossing[j,:])[0][0]
+                bot_s = ShareGrid[idx]
+                top_s = ShareGrid[idx+1]
+                bot_f = FOC_s[j,idx]
+                top_f = FOC_s[j,idx+1]
+                bot_c = EndOfPrddvdaNvrs[j,idx]
+                top_c = EndOfPrddvdaNvrs[j,idx+1]
+                alpha = 1. - top_f/(top_f-bot_f)
+                Share_now[j] = (1.-alpha)*bot_s + alpha*top_s
+                cNrmAdj_now[j] = (1.-alpha)*bot_c + alpha*top_c
 
     # Calculate the endogenous mNrm gridpoints when the agent adjusts his portfolio
     mNrmAdj_now = aNrmGrid + cNrmAdj_now