Linear Genetic Programming
1) I think this depends on the implementation of linear GP you use. Since you're writing your own implementation, I would recommend looking at some other people's implementations. IMO, this really depends on what kind of linear GP system you wrote/are using. I haven't looked myself, but ECJ might have an implementation that you can reference.

2) There are TONS of methods to crossover/mutate your individuals. I would argue that you could perform crossover using either of the approaches you mention. The one you should use will depend on the problem you're tackling and how you represent your individuals. Sometimes it's best to design crossover and mutation methods specifically for your system. A Google Scholar search for various mutation and crossover operations in GP, GAs, and linear GP will give you A LOT to work with. 

3) Time series forecasting is a particularly difficult problem. In my very limited experience, economic data can be pretty volatile. Using a standard linear GP algorithm probably won't give you especially good results. With a problem that difficult, you're going to need more advanced approaches than just different crossover/mutation operators. It might be worth your time to look into some probabilistic, generalization, or strongly-typed GP approaches. Check out the Field Guide to GP for some more on those. Again, Google Scholar is a great resource for learning more about those.

I hope this helps!