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.

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.