More fun with maximum likelihood estimation
A while ago, I wrote a post
where I jotted down some notes. I ended the post with the following:
Note: The first time I worked through this exercise, I thought it was cute, but I would never compute and as above so the maximum likelihood estimation, as presented, is not meaningful to me. Hence, this is just a warm up for what comes next. Stay tuned…
Well, it has been over a year and I’m trying to get a friend interested in MLE for a side project we might work on together, so thought it would be good to revisit it now.
To briefly review, the probability of observing independent samples may be approximated by
where is a probability density and represents the parameters we are trying to estimate. The key observation becomes clear after a slight change in perspective.
If we take the th root of the above probability (and divide by ), we obtain the geometric mean of the individual densities, i.e.
In computing the geometric mean above, each sample is given the same weighting, i.e. . However, we may have reason to want to weigh some samples heavier than others, e.g. if we are studying samples from a time series, we may want to weigh the more recent data heavier. This inspired me to replace with an arbitrary weight satisfying
With no apologies for abusing terminology, I’ll refer to this as the likelihood function
Replacing with would result in the same parameter estimation as the traditional maximum likelihood method.
It is often more convenient to work with log likelihoods, which has an even more intuitive expression
i.e. the log likelihood is simply the weighted (arithmetic) average of the log densities.
I use this approach to estimate stable density parameters for time series analysis that is more suitable for capturing risk in the tails. For instance, I used this technique when generating the charts in a post from back in 2009:
which was subsequently picked up by Felix Salmon of Reuters in
and Tracy Alloway of Financial Times in
If I find a spare moment, which is rare these days, I’d like to update that analysis and expand it to other markets. A lot has happened since August 2009. Other markets I’d like to look at would include other equity markets as well as fixed income. Due to the ability to cleanly model skew, stable distributions are particularly useful for analyzing fixed income returns.