Hurst's Exponent

Aparna_Anand_51fbn · August 19, 2020, 4:15am

Hi

I am looking to work with Hurst's Exponent for understanding Mean Reversion of a Time Series.Need help with interpetating the reult and also understanding signiface of lag in the calculation. I am analysis data for 1 year or around 250 data points.

Thanks

Akshay_Nautiyal_2ycOh · August 19, 2020, 5:20pm

Hello Aparna,

Hurst exponent is a measure to corroborate if a given time series is mean reverting or trending. It is a measure between 0 and 1. When it is is closer to 0 it indicates a mean-reverting series and when it is closer to 1 it informs of a trending series. In essence, it is telling us the change in autocorrelation in a series as the lag increases. This blog deep dives into the nitty-gritty of hurst calculation.

Aparna_Anand_51fbn · August 20, 2020, 10:26am

Hi Akshay

Thanks for your reply. I am looking for Python code and its interpetation for Hurst's Exponent. Can you help around that.

Thanks

Vibhu_uxX · August 21, 2020, 3:19am

There is a package in Python hurst, which helps in calculating Hurst exponent of any time series data directly.

Link: https://pypi.org/project/hurst/

Hope that helps.

Aparna_Anand_51fbn · August 21, 2020, 7:31am

Thanks! Vibhu

Cathy_Mawart_2b5lD · October 8, 2023, 9:06pm

Hi, quick question, so basically Hurst exponent(to check is there is mean reverting, trending or random walk) is more complete than calculating the ADF(only to see if there is a stationarity) ?

Akshay_Choudhary · October 9, 2023, 12:54pm

Hi Cathy,

The Hurst exponent and the ADF test serve different purposes. They are not directly comparable in terms of completeness because they address different aspects of time series behaviour.

The Hurst exponent provides information about the long-term behaviour of a time series, including whether it exhibits trends, mean reversion, or randomness.
The ADF test is primarily used to assess the stationarity of a time series, which is a prerequisite for some time series modelling techniques.

So, to understand a time series comprehensively, you may consider using both methods in combination. First, you can use the ADF test to check for stationarity. If the series is non-stationary, you can further analyze its long-term behavior using the Hurst exponent to determine whether it exhibits mean-reverting, trending, or random walk characteristics.

Hope this helps!

Thanks,
Akshay