1 Assignment #6 Predictive models (Submit a Word document for this assignment) This homework uses data on variance risk premium (VRP) and market returns. Research shows that VRP is associated with expected returns. Our goal is to verify this finding. The first worksheet of the dataset includes monthly S&P500 returns and monthly levels of the variance risk premium. The question is: does VRP predict monthly returns on the S&P500 index? 1) Run a regression of returns on lagged VRP. Note that the dataset includes the values for VRP and returns for the same month. You need to lag the VRP variable by one month so that the model becomes predictive (review the video on the relationship between CAPE ratio and expected returns if you need help on this). Having lagged the VRP variable, regress Return on lagged VRP. The second worksheet of the dataset file includes the quarterly S&P500 returns and levels of the variance risk premium. This data helps us learn whether VRP predicts quarterly returns on the S&P500 index 2) Run a regression of quarterly returns on lagged VRP. Once again, because this data shows the values for VRP and returns during the same quarter, VRP should be lagged before setting up the regression. You now have two regression outputs. One uses monthly data and one uses quarterly data. 3) Report the relevant results of these two regressions in one table. 4) Interpret your results. Is VRP related to future returns? If so, how can a trader uses the level of VRP for decision making? 5) Make an out-of-sample prediction of S&P500 return using the estimated model and the last value of VRP for the monthly and quarterly data. 1 Assignment #6 Predictive models (Submit a Word document for this assignment) This homework uses data on variance risk premium (VRP) and market returns. Research shows that VRP is associated with expected returns. Our goal is to verify this finding. The first worksheet of the dataset includes monthly S&P500 returns and monthly levels of the variance risk premium. The question is: does VRP predict monthly returns on the S&P500 index? 1) Run a regression of returns on lagged VRP. Note that the dataset includes the values for VRP and returns for the same month. You need to lag the VRP variable by one month so that the model becomes predictive (review the video on the relationship between CAPE ratio and expected returns if you need help on this). Having lagged the VRP variable, regress Return on lagged VRP. The second worksheet of the dataset file includes the quarterly S&P500 returns and levels of the variance risk premium. This data helps us learn whether VRP predicts quarterly returns on the S&P500 index 2) Run a regression of quarterly returns on lagged VRP. Once again, because this data shows the values for VRP and returns during the same quarter, VRP should be lagged before setting up the regression. You now have two regression outputs. One uses monthly data and one uses quarterly data. 3) Report the relevant results of these two regressions in one table. 4) Interpret your results. Is VRP related to future returns? If so, how can a trader uses the level of VRP for decision making? 5) Make an out-of-sample prediction of S&P500 return using the estimated model and the last value of VRP for the monthly and quarterly data
