Quantitative Finance Stack Exchange is a question and answer site for finance professionals and academics. It's 100% free, no registration required.

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

Ernest Chan in its book "Algorithmic Trading" shows how to use the Kalman Filter for mean reversion pair trading.

I have seen that he uses the measurement prediction error for calculating the spread size. In other works, he bases the spread calculation on:

$$ e = y_{t} - \hat{y} $$

where, $ \hat{y} $ is the measurement prediction based on the state variable predictor $ \hat{x}(k+1|k)$ where $k$ is the time/measurement.

I was wondering what the advantage of using the measurement prediction error instead of the residuals is. With residuals I mean $y - y_c$ where $y_c$ is the estimate of the measurement based on the updated/corrected state variable.

Thanks.

share|improve this question

Your Answer

 
discard

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

Browse other questions tagged or ask your own question.