In the application in the text we noted that NYSE stock returns appeared to have some weak conditional mean dynamics, yet we ignored them and proceeded directly to model volatility.

a.  Instead, first fit autoregressive models using the SIC to guide order selection, and then fit GARCH models to the residuals.  Redo the entire empirical analysis reported in the text in this way, and discuss any important differences in the results.* Remarks, suggestions, hints, solutions: Neglected conditional mean dynamics may masquerade as conditional variance dynamics, so it is important that they be adequately modeled first.  The conditional mean “model” used in the text was simply a constant term, which is arguably adequate to the extent that the true conditional mean dynamics, if any, are negligible, which is effectively what the students are asked to assess in this problem.

b.  Consider instead the simultaneous estimation of all parameters of AR(p)-GARCH models.  That is, estimate regression models where the regressors are lagged dependent variables and the disturbances display GARCH.  Redo the entire empirical analysis reported in the text in this way, and discuss any important differences in the results relative to those in the text and those obtained in part a above.* Remarks, suggestions, hints, solutions: It’s usually better to do things in one step rather than two...