please help me with those questions! thanks!

Consider again the portfolio risk-return information from part III illustrated below. 1. The points vfinx, vpacx and vbltx are the locations for the estimated pairs (^i,^i). As a result, there is estimation error in each of the points on the graph. On the graph above, indicate the approximate magnitude of estimation error for each point. That is, for each point sketch the approximate 95% confidence region for the true pair (i,i). In particular, indicate if estimation error is larger in the horizontal or vertical direction. Hint: recall the standard error formulas for ^ and ^. 2. Next, consider the estimated global minimum variance portfolio (point labeled Global Min). The estimated portfolio weights m^ have estimation error, and the estimated expected return ^p,m=m^^ and volatility ^p,m=(m~m)1/2 also have estimation error. The magnitude of these estimation errors can be quantified using the bootstrap. The figure below, shows 500 bootstrap estimates of the pair (p,m,p,m). Using this diagram, briefly discuss the estimation error in the pair (^p,m,^p,m) 3. Finally, consider using the bootstrap to evaluate estimation error in the global minimum variance weights. Recall, from Table 1 above the global minimum variance weights are estimated to be The bootstrap estimates of bias for the weights are: > colmeans (w.gmin.boot) - gmin.port\$weights The bootstrap SE estimates of the weights are: >apply (w.gmin.boot, 2, sd) vfinx vpacx vbltx 0.14080.12750.0768 Based on the bootstrap bias and SE estimates, how well are the global minimum variance weights are estimated? Are some weights estimated better than others? Briefly justify your answer. Consider again the portfolio risk-return information from part III illustrated below. 1. The points vfinx, vpacx and vbltx are the locations for the estimated pairs (^i,^i). As a result, there is estimation error in each of the points on the graph. On the graph above, indicate the approximate magnitude of estimation error for each point. That is, for each point sketch the approximate 95% confidence region for the true pair (i,i). In particular, indicate if estimation error is larger in the horizontal or vertical direction. Hint: recall the standard error formulas for ^ and ^. 2. Next, consider the estimated global minimum variance portfolio (point labeled Global Min). The estimated portfolio weights m^ have estimation error, and the estimated expected return ^p,m=m^^ and volatility ^p,m=(m~m)1/2 also have estimation error. The magnitude of these estimation errors can be quantified using the bootstrap. The figure below, shows 500 bootstrap estimates of the pair (p,m,p,m). Using this diagram, briefly discuss the estimation error in the pair (^p,m,^p,m) 3. Finally, consider using the bootstrap to evaluate estimation error in the global minimum variance weights. Recall, from Table 1 above the global minimum variance weights are estimated to be The bootstrap estimates of bias for the weights are: > colmeans (w.gmin.boot) - gmin.port\$weights The bootstrap SE estimates of the weights are: >apply (w.gmin.boot, 2, sd) vfinx vpacx vbltx 0.14080.12750.0768 Based on the bootstrap bias and SE estimates, how well are the global minimum variance weights are estimated? Are some weights estimated better than others? Briefly justify your