Sep
27

## Mean Reversion Speed Estimation - What Am I Missing?

I am stuck by the method to

where mu is the mean value of the prices over time, and dW is simply some random Gaussian noise. Given a time series of the daily spread values, we can easily find theta(and mu) by performing a linear regression fit of the daily change in the spread dz against the spread itself, then we are able to calculate the half-life as log(2)/theta, which is the expected time it takes for the spread to revert to half its initial deviation from the mean. This half-life can be used to determine the optimal holding period for a mean-reverting position and as a measure for exit-trading strategy.

with Ito lemma, where the error term is normally distributed, therefore basically regression of dz on returns a value for instead of for theta itself.

In order to check the issue, I simulate a mean reversion process with dt=0.05, theta=0.75, mu=0.02 and run a regression

ols(dz, (mu - z(t-1))) = 0.03767243

It is far away from the true value 0.75, and we get a closer number 0.7680074 if we invert it back based on the equation .

Is it an error of the book or am I missing something? help me.

Hot posts:

15 Incredibly Stupid Ways People Made Their Millions

Ino.com: Don't Join Marketclub until You Read This MarketClub Reviews

Online stock practice

World Changing Mathematical Discoveries

Value at Risk xls

Random posts:

Quantitative Asset Management library

Download Multiple Stock Quotes

Week in Review 260112 Credit Default Swap

Publish / Apply Quant Jobs

How to Use Bar Patterns to Spot Trade Setups

**estimate the mean reversion speed**(and hence half life) described in the book Quantitative Trading: How to Build Your Own Algorithmic Trading Business, on page 140 the author said suppose the mean reversion of a time series can be modeled by an equation called the**Ornstein-Uhlenbeck**formula, and denote the mean-reverting process of a stock z be writen aswhere mu is the mean value of the prices over time, and dW is simply some random Gaussian noise. Given a time series of the daily spread values, we can easily find theta(and mu) by performing a linear regression fit of the daily change in the spread dz against the spread itself, then we are able to calculate the half-life as log(2)/theta, which is the expected time it takes for the spread to revert to half its initial deviation from the mean. This half-life can be used to determine the optimal holding period for a mean-reverting position and as a measure for exit-trading strategy.

**So far so good.**But then I am confused by the example 7.5 for theta estimation, where the author simply regresses dz on , is it correct? as we know if a process is mean reverted like above, it can be rewriten aswith Ito lemma, where the error term is normally distributed, therefore basically regression of dz on returns a value for instead of for theta itself.

In order to check the issue, I simulate a mean reversion process with dt=0.05, theta=0.75, mu=0.02 and run a regression

ols(dz, (mu - z(t-1))) = 0.03767243

It is far away from the true value 0.75, and we get a closer number 0.7680074 if we invert it back based on the equation .

Is it an error of the book or am I missing something? help me.

**People viewing this post also viewed:**

Hot posts:

Random posts:

animal jam hack