Calculating half life of mean reverting series with python

Question

I am currently attempting to calculate the halflife of a mean reverting series using python programming language and the theory of the Ornstein–Uhlenbeck process.

I have a series which when plotted looks like:

Which obviously looks rather mean reverting. I am carrying out the following using python code to find the halflife (FYI the series shown above is held in the variable (z_array):

import numpy as np
import statsmodels.api as sm

#set up lagged series of z_array and return series of z_array
z_lag = np.roll(z_array,1)
z_lag[0] = 0
z_ret = z - z_lag
z_ret[0] = 0

#run OLS regression to find regression coefficient to use as "theta"
model = sm.OLS(z_ret,z_lag)
res = model.fit()

#calculate halflife
halflife = -log(2) / res.params[0]
print  'Halflife = ',halflife

The code runs fine, however for this series I am getting a halflife of 680.5 days - I can see from the chart that this looks very wrong. Full reversions are happening within a fraction of that time frame.

Could someone please advise me as to where I am going wrong with this?

Any help much appreciated!

Answer 1

I found out what I was doing wrong - the OLS function was regressing with no intercept value - so I had to use the "add_constant" method to add an intercept term to the X series (z_lag) as follows:

z_lag = np.roll(z_array,1)
z_lag[0] = 0
z_ret = z_array - z_lag
z_ret[0] = 0

#adds intercept terms to X variable for regression
z_lag2 = sm.add_constant(z_lag)

model = sm.OLS(z_ret,z_lag2)
res = model.fit()

halflife = -log(2) / res.params[1]

I'm now getting a more resonable halflife of 15 days!