Estimating Ornstein–Uhlenbeck Parameters with Linear Regression

# How can I estimate the Ornstein-Uhlenbeck paramters of some mean reverting data that I have on R?


# How can I estimate the Ornstein-Uhlenbeck paramters of some mean reverting data that I have on R?












I have mean reverting data (Difference of 2 stock prices, that I want to do pairs trading on). I want to simulate my own mean reverting data as similar as possible to the real data that I have.

The approach that I want to take is Least Squares Regression. Maximum Likelihood is too complicated.

Thank you all.

## Answer by Richi Wa (score 10)

https://quant.stackexchange.com/a/17193

Similar to Juan Gil's answer but a bit differently I would say the following based on this:

The OU process $$dX_t = \kappa(\theta-X_t)dt + \sigma dW_t$$ can be (Euler-Maryuama discretization) discretized at times $n \Delta t,n=1,\ldots,\infty $ which gives with $t = k \Delta t$ $$ X_{k+1} - X_k = \kappa \theta \Delta t -\kappa X_k \Delta t + \sigma (W_{k+1} - W_k), $$ rearranging and setting $\sigma (W_{k+1} - W_k) = \sigma \sqrt{\Delta t} \epsilon_k $ we get: $$ X_{k+1} = \kappa \theta \Delta t - (\kappa \Delta t - 1) X_k + \sigma \sqrt{\Delta t} \epsilon_k. $$ So you can model an AR(1) process and then identify the parameters using the equation above.

Thinking about it again one can probably leave $X_{k+1} - X_k$ on the lhs and then one simply does a regression but I don't know exactly about the error terms in this case.

I have found this with R code, there an MLE approach is used. You find various solutions in this Stack Overflow question.

## Answer by Juan Ignacio Gil (score 6)

https://quant.stackexchange.com/a/17191

For a Ornstein-Uhlenbeck process, the maximum likelihood parameters are the ones from least squares regression.

If your process is:

$$ dX=\kappa (\theta-X)dt+\sigma dW $$

you can do a linear regression in the form

$$ \frac{dX}{dt}=a+bX+\epsilon $$

So your parameters will be:

$$ \kappa=-b $$

$$ \theta=-\frac{a}{b} $$

$$ \sigma=std(\epsilon dt) $$

## Answer by Gustave Euler (score 0)

https://quant.stackexchange.com/a/78826

The previous estimator relying on regression is effective only when the degree of mean reversion is sufficiently high that the influence of volatility noise on the calculation is minimal. However, an alternative method proposed by Falk, based on robust statistics, is designed to operate effectively even in the presence of real-world data.[Falk, 1997] Falk, M. (1997). On mad and comedians. Annals of the Institute of Statistical Mathematics, 49(4):615–644.