Portfolio Optimization of a Global portfolio

Is in the field of finance more specific in the area of Portfolio Management, the idea is to do a dynamic portfolio optimization, using information from 20 years, 14 countries indexes , we seek to demonstrate that a global Portfolio Index improves if we add emerging markets (indexes of 7 countries), our portfolio improves both performance and risk diversification, and to do this optimization we seek to test cointegration and correlation between the different countries, we would agree that there is no Correlation in order to diversify the portfolio. We also seek to optimize the portfolio by applying trading techniques, which would maximize the sharpe ratio and minimize variance among others. And mention a little about what the spanning test might mean. The information that I am going to provide you is everything that I have been able to do on the subject, we already have the data that are indices of the countries consists of the closing prices of the indices of each country of the 01/01/1995 to 01/01 / 2017 I have the monthly and daily observations. The risk free that would be used in portfolio optimization would be a 1-month US Treasury rate. The Thesis would be in the field of finance more specific in the area of Portfolio Management, the idea is to do a dynamic portfolio optimization, using information of 20 years, are 14 countries indexes, 7 emerging and 7 developed, we seek to demonstrate that a Global portfolio (developed countries) if we add emerging markets (indices of 7 countries), our portfolio improves in both yield and risk diversification, and to do this optimization we would look for cointegration and correlation between the different countries, we would agree That there would be no correlation in order to diversify the portfolio. We also seek to optimize the portfolio by applying trading techniques, which would maximize the sharpe ratio and minimize variance among others. And mention a little about what the spanning test might mean. The information that I am going to provide you is everything that I have been able to do on the subject, we already have the data that are indices of the countries consists of the closing prices of the indices of each country of the 01/01/1995 to 01/01 / 2017 I have the monthly and daily observations. The risk free that would be used in portfolio optimization would be a 1-month US Treasury rate. The indexes countries that I’m ussing are : France Germany United Kingdom Canada USA Hungary Russia China Peru Chile Mexico Japan For the dynamic portfolio: Assuming you are constructing rolling one step ahead forecasts. You would set the Back period to 60 months (and the forecasts would start from month 61 to the end of the sample). Rolling windows would keep the estimation window fixed to 60 – so you are dropping one observation at the beginning and adding one observation in the estimation as you move forward. I wrote this down to you the other day. You will be rebalancing / optimizing every month, so I guess the gap period will be set 1 in the spreadsheet. What is this means that you will be optimized using estimates from 1 month to 1 month to calculate the weights and then calculate the returns for month 61. Then you will use data from month 2 to 61 to calculate the weights and returns for month 62 And you keep doing this until the end of the sample.