I'm curious if anyone can validate my train of thought here with the utility of Hidden Markov models for modeling things happening on higher frequency trading activity versus lower frequency, and in the case of the former I do not necessarily mean HFT—but relatively high frequency.
Hidden Markov models have been used all over quant finance for various things, as an example this paper goes into the use of Hidden Markov models over GARCH (1,1) models for predicting volatility. My intuition however tells me that trying to train Hidden Markov models on raw financial data over larger periods of time is not always going to be the best idea given you're training it on something heavily random, i.e., forcing it to train on Brownian motion.
On the other hand, with high(er) frequency trading data does the story change a bit? The data is more "dense" and eliminating the noise factor is easier to do with data on higher frequency time scales than on days / weeks thus making it more exploitable by a Hidden Markov model than on longer time scales.
With high frequency trading specifically, I would think the applications of Hidden Markov models could be more beneficial for a couple reasons:
- It would be easier for the Hidden Markov model to do its job of sorting returns into groups where all of these respective groups have some corresponding probability distribution, and these would be the states for the Hidden Markov model.
- Given these states fit together in a nicer way, wouldn't that mean it would be easier for the Hidden Markov model to find certain things of interest? They are able to statistically separate out groups with different price movements—perhaps find things “deeper” (e.g., say it finds that over a period of time the returns are not correlated, it may be able to find another relation that is of more subtle nature, a certain return on $X$ is followed by $Y$ with x% stronger correlation, etc…)
Is there any truth to these thoughts?
