Take the 2-minute tour ×
Quantitative Finance Stack Exchange is a question and answer site for finance professionals and academics. It's 100% free, no registration required.

I am new to regression analysis. Let's say initially I have a linear regression

x = alag(x1) + blag(x2) + clag(x3) -- eq 1

I want to predict the price x based on the the price of x from previous days.

Let's say I believe that lagged volatility y and lagged range (high - low) z also would affect today's price, how could I regress the data? Do I simply do

x = alag(x1) + blag(x2) + clag(x3) + dlag(y1) + elag(y2) + flag(y3) + glag(z1) + hlag(z2) + ilag(z3) -- eq 2

Intuitively, I think that the combination of the three factors together for a particular day is useful for the prediction. For example,

x = alag(All factors lag 1) + blag(All factors lag 2) + clag(All factors lag 3) --eq 3

However, by using eq2, it seems like I am treating all factors independently irregardless the data point is from the same day or not. So is there a method to handle the lagged data in groups or I am getting it wrong by thinking that way?

Thanks.

share|improve this question
    
it really depends on what your assumptions are of independence between the lagged data. –  Matt Wolf Apr 6 '13 at 7:22
add comment

2 Answers

I think that the combination of the three factors together for a particular day is useful for the prediction.

A shortcut.

Did you formally determine the nature of this combination of the 3 factor together?

Say you come up with the combination: factor1 + factor2 + factor3.

What you can do is considering it as a new variable, eg v to that v = factor1 + factor2 + factor3

You now just have to plug the new variable v into you equation.

share|improve this answer
add comment

For forecasting and regression methods there is a great, free online textbook by Rob Hyndman. Chapter 9 deals with time-series regression. It is perfectly fine to have correlated factors on the rhs as in your equation 2. The forecast will work but the clear attribution to the regression factors will not possible.

share|improve this answer
add comment

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.