Let's say a Hedge Fund is tracking a stock price. Now the fund has three columns of data, Stock price, Index 1, and Index 2. All of these have data from 2016/01/01 - 2017/01/01. If the fund is to decide which Index it is going to use as the main benchmark for the price of the stock, then which is a better way to make such a decision?
- Simple Regression for P = beta0 + beta1 * Index1 + error and P = beta0 + beta1 * Index2 + error
However, I feel this approach is wrong, because the data we are using is time series data, and this violates many assumptions of OLS model, so we should not use this, am I understanding this correctly?
- Time series regression for the above model.
Could we run this as a time series model?
- Correlation Coefficient between (P, Index1), and (P, Index2).
Could you please inform me which is better and the reasons behind it?
Thank you very much!